benefits of project based learning research

Delve Institute of Learning Research

From Theory to Practice: Exploring the Benefits of Project-Based Learning

Welcome to the fascinating world of project-based learning! In this article, we will delve into the benefits of this innovative educational approach and explore how theory can be effectively put into practice. Project-based learning, also known as PBL, is gaining attention as a student-centered teaching method that engages learners through hands-on projects.

In This Article…

• 8 Benefits of Project-Based Learning
• Research In Support of PBL
• 5 Keys to Successful PBL
• Examples in Different Grade-Levels
• Steps to Implementing Project-Based Learning
• Challenges of Using PBL
• Assessing and Evaluating
• PBL Resources and Tools

By immersing students in real-world challenges, PBL encourages critical thinking, problem-solving, collaboration, and creativity.

Throughout this article, we will uncover real success stories of schools and educators who have embraced project-based learning and witnessed remarkable transformations in student achievement and engagement. Are you ready to take a deep dive into the world of project-based learning? Let’s embark on this exciting journey together!

The Benefits of Project-Based Learning

Project-based learning (PBL) is a teaching method in which students learn by working on a project that has a real-world application. PBL is based on the idea that students are more motivated to learn when they are actively engaged in the learning process.

So, what are the specific benefits of project-based learning? Firstly, it enhances student motivation by allowing them to take ownership of their learning and explore topics that interest them. Additionally, PBL nurtures essential skills such as communication and teamwork that are highly valued in today’s professional world. It also promotes deep understanding of subject matter as students engage in meaningful tasks that require application of knowledge.

There are many benefits to PBL, including:

Increased engagement: PBL is a student-centered approach to learning, which means that students are actively involved in the learning process. This leads to increased engagement and motivation.

Deeper learning: PBL allows students to explore topics in more depth than they would in a traditional classroom setting. This leads to deeper learning and understanding of the material.

Development of 21st-century skills: PBL helps students develop the 21st-century skills that they need to be successful in college and the workforce, such as critical thinking, problem-solving, collaboration, and communication.

Real-world application: PBL projects are often based on real-world problems, which helps students see the relevance of what they are learning. This can lead to increased motivation and engagement.

Creativity and innovation: PBL encourages students to be creative and innovative in their thinking. This is important for solving real-world problems.

Self-directed learning: PBL gives students the opportunity to learn independently and at their own pace. This helps them develop self-directed learning skills, which are essential for lifelong learning.

Collaboration: PBL projects often require students to collaborate with each other. This helps them develop teamwork and communication skills.

Reflection: PBL projects often involve reflection, which helps students learn from their experiences.

PBL is not without its challenges, however. It can be time-consuming to plan and implement PBL projects, and it can be difficult to assess student learning. However, the benefits of PBL outweigh the challenges, and it is a valuable teaching method that can help students learn in a meaningful and engaging way.

Research Supporting Project-Based Learning

Project-based learning (PBL) is a pedagogical approach that engages students in authentic, real-world problems and challenges them to collaborate, communicate, and create solutions. Some examples of research supporting PBL are:

• Boss, S., & Krauss, J. (2018). Reinventing project-based learning: Your field guide to real-world projects in the digital age. International Society for Technology in Education.
• Hattie, J., Fisher, D., & Frey, N. (2017). Visible learning for mathematics: What works best to optimize student learning. Corwin Press.
• Larmer, J., Mergendoller, J., & Boss, S. (2015). Setting the standard for project-based learning: A proven approach to rigorous classroom instruction. ASCD.

Key Elements of Successful Project-Based Learning

The key elements of successful project-based learning (PBL) include:

Challenging problem or question: The project must be based on a meaningful problem to be solved, question to be answered, or challenge to be addressed. The problem or question should be relevant to the students’ interests and abilities, and it should be open-ended enough to allow for a variety of solutions.

Sustained inquiry: The project should allow students to engage in sustained inquiry, which means that they should have the opportunity to explore the problem or question in depth. This may involve conducting research, collecting data, and analyzing information.

Authenticity: The project should be authentic, which means that it should have a real-world connection. This could involve working with a community partner, creating a product that can be used by others, or solving a problem that is relevant to the students’ lives.

Student voice and choice: Students should have a voice in the project, which means that they should be able to make decisions about the problem or question, the research they conduct, and the way they present their findings. This helps to increase student engagement and motivation.

Reflection: Students should have opportunities to reflect on their learning throughout the project. This could involve keeping a journal, writing a reflection paper, or presenting their findings to an audience. Reflection helps students to solidify their learning and make connections between the project and their own lives.

These are just some of the key elements of successful project-based learning. It is important to note that not all projects will include all of these elements. The specific elements that are included will depend on the nature of the project and the needs of the students.

Examples of Project-Based Learning in Different Educational Settings

Here are some examples of project-based learning in a 5th-grade Math classroom and a 10th-grade Literature class.

5th Grade Math: Design a new playground

Students could work together to design a new playground for their school or community. They would need to consider factors such as the age range of the children who would be using the playground, the space available, and the budget. They would also need to use their math skills to calculate the dimensions of the playground, the amount of materials needed, and the cost of the project.

5th Grade Math: Solve a Local Problem

Students could work together to solve a local problem that involves math. For example, they could research the number of homeless people in their community and develop a plan to help them. Or, they could research the amount of trash that is generated in their community and develop a plan to reduce it.

10-Grade Literature Class: Create a Podcast

Students could create a podcast about a novel they are reading. This would involve interviewing characters from the novel, discussing the themes of the novel, and creating sound effects. Plus, it would integrate technology skills and audio editing skills.

8 Steps to implement project-based learning in the classroom

Here are the steps to implement project-based learning in the classroom:

• Identify the learning goals: The first step is to identify the learning goals that you want students to achieve through the project. These goals should be specific, measurable, achievable, relevant, and time-bound.
• Choose a project topic: Once you have identified the learning goals, you need to choose a project topic that is relevant to the goals and that students will be interested in. The topic should be open-ended enough to allow for a variety of solutions.
• Create a project brief: The project brief is a document that outlines the project goals, the project topic, the timeline, and the assessment criteria. The project brief should be shared with students so that they know what is expected of them.
• Form project teams: If the project is large or complex, you may want to form project teams. This will allow students to collaborate and share ideas.
• Provide resources: Students will need access to resources, such as books, articles, websites, and experts, in order to complete the project. You should provide students with these resources or help them find them.
• Guide students: As students work on the project, you should provide them with guidance and support. This may involve answering questions, providing feedback, and helping them solve problems.
• Allow students to self-assess: Students should be given the opportunity to self-assess their work throughout the project. This will help them to identify their strengths and weaknesses and to make necessary adjustments.
• Evaluate student learning: At the end of the project, you should evaluate student learning. This can be done through a variety of methods, such as presentations, portfolios, or essays.

These are just the basic steps to implement project-based learning in the classroom. There are many other factors to consider, such as the age and ability level of the students, the availability of resources, and the school culture. However, following these steps will help you to get started with project-based learning.

Challenges and Solutions in Project-Based Learning

There are many challenges and solutions to PBL, and it is important for teachers to be aware of both in order to implement PBL effectively.

Some of the challenges of PBL include:

• Time commitment: PBL can be a time-consuming approach to teaching, as it requires students to spend time researching, planning, and completing the project.
• Assessment: It can be difficult to assess student learning in PBL, as it is not always easy to measure the skills and knowledge that students have gained through the project.
• Student motivation: Some students may not be motivated to work on a project, especially if they are not interested in the topic or if they do not see the relevance of the project to their lives.
• Teacher support: PBL requires teachers to provide a lot of support to students, as they may need help with research, planning, and problem-solving.
• Collaboration: PBL requires students to collaborate with each other, which can be challenging for some students.

Some of the solutions to the challenges of PBL include:

• Planning: Teachers should carefully plan PBL projects, taking into account the time commitment, assessment, student motivation, and teacher support required.
• Scaffolding: Teachers should provide students with scaffolding, such as templates and rubrics, to help them complete the project.
• Choice: Teachers should give students some choice in the projects that they work on, which can help to increase motivation.
• Real-world connections: Teachers should make sure that the projects have real-world connections, which can help students see the relevance of the project to their lives.
• Collaboration: Teachers should provide opportunities for students to collaborate with each other, and they should teach students how to collaborate effectively.

By being aware of the challenges and solutions to PBL, teachers can implement PBL effectively and help students learn in a meaningful and engaging way.

Assessing and Evaluating Project-Based Learning

Here are 4 ways to assess and evaluate project-based learning:

• Portfolios: Portfolios are a collection of student work that showcases their learning. Portfolios can include projects, essays, presentations, and other artifacts. They can be used to assess student learning over time and to track progress.
• Rubrics: Rubrics are scoring guides that list the criteria for success for a project. Rubrics can be used to assess student work objectively and consistently.
• Self-assessment: Self-assessment is when students reflect on their own learning and identify their strengths and weaknesses. Self-assessment can be used to help students learn from their mistakes and to set goals for improvement.
• Peer assessment: Peer assessment is when students assess each other’s work. Peer assessment can be used to give students feedback on their work and to learn from each other.

In addition to these four methods, there are many other ways to assess and evaluate project-based learning. The best approach will vary depending on the specific project and the learning goals.

Project-based learning resources and tools

There are many project-based learning resources and tools available online and in libraries. Here are a few of the most popular:

PBLWorks: PBLWorks is a website that provides resources and support for project-based learning. It includes a library of project templates, a forum for teachers to share ideas, and a blog with articles about PBL.

Project Zero:  Project Zero is a research group at Harvard University that studies the design and implementation of project-based learning. It provides resources and support for teachers, including a toolkit for planning and implementing PBL projects.

Inquiry Hub: Inquiry Hub is a website that provides resources for inquiry-based learning, which is a similar approach to PBL. It includes a library of lesson plans, a forum for teachers to share ideas, and a blog with articles about inquiry-based learning.

Conclusion and Future of Project-Based Learning in Education

Project-based learning (PBL) is a student-centered teaching method in which students learn by working on a project that has a real-world application. PBL is based on the idea that students are more motivated to learn when they are actively engaged in the learning process.

What do you think? Have you used PBL in your classroom? What questions, tips, or ideas can you share?

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• Open access
• Published: 06 January 2022

The key characteristics of project-based learning: how teachers implement projects in K-12 science education

• Anette Markula 1 &
• Maija Aksela   ORCID: orcid.org/0000-0002-9552-248X 1  

Disciplinary and Interdisciplinary Science Education Research volume  4 , Article number:  2 ( 2022 ) Cite this article

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The aim of this multiple-case study was to research the key characteristics of project-based learning (PBL) and how teachers implement them within the context of science education. K-12 science teachers and their students’ videos, learning diaries and online questionnaire answers about their biology related PBL units, within the theme nature and environment, were analysed using deductive and inductive content analysis ( n  = 12 schools). The studied teachers are actively engaged in PBL as the schools had participated voluntarily in the international StarT programme of LUMA Centre Finland. The results indicate that PBL may specifically promote the use of collaboration, artefacts, technological tools, problem-centredness, and certain scientific practices, such as carrying out research, presenting results, and reflection within science education. However, it appeared that driving questions, learning goals set by students, students’ questions, the integrity of the project activities, and using the projects as a means to learn central content, may be more challenging to implement. Furthermore, although scientific practices had a strong role in the projects, it could not be defined how strongly student-led the inquiries were. The study also indicated that students and teachers may pay attention to different aspects of learning that happen through PBL. The results contribute towards a deeper understanding of the possibilities and challenges related to implementation of PBL and using scientific practices in classrooms. Furthermore, the results and the constructed framework of key characteristics can be useful in promoting research-based implementation and design of PBL science education, and in teacher training related to it.

Introduction

Project-based learning (PBL) can be a useful approach for promoting twenty-first century learning and skills in future-oriented K-12 science education. PBL refers to problem-oriented and student-centred learning that is organised around projects (Thomas, 2000 ). This means that the intended learning of new skills and content happens through the project that students carry out in groups (Condliffe et al., 2017 ; Parker et al., 2013 ; Thomas 2000 ). Thus , PBL can be described as a collaborative inquiry-based teaching method where students are integrating, applying and constructing their knowledge as they work together to create solutions to complex problems (Guo et al., 2020 ). It is important that students practice working like this at school, as future generations will need to be able to overcome global environmental problems. As such, science education has to equip students with deeper learning instead of simple memorising of facts; students need the ability to apply their scientific knowledge in situations requiring problem-solving and decision-making (Miller & Krajcik, 2019 ).

PBL relies on four significant ideas from learning sciences: learning is most effective when students (1) construct their understanding actively and (2) work collaboratively in (3) authentic learning environments, whilst being sufficiently scaffolded with (4) cognitive tools (Krajcik & Shin, 2014 ). Compared to traditional teacher-led instruction, PBL has been found to result in greater academic achievement (Chen & Yang, 2019 ; Balemen & Özer Keskin, 2018 ). Additionally, it has been shown to improve students’ skills in critical thinking and question-posing (Sasson et al., 2018 ). There is also some evidence that PBL might contribute to developing students’ intra- and interpersonal competencies (Kaldi et al., 2011 ).

Within science and technology education, one of the key benefits of PBL is arguably immersing students in using scientific practices, such as asking questions (Novak & Krajcik, 2020 ). Whilst various approaches can be taken to PBL, scientific practices are often considered as one of its key characteristics (see Table  1 for discussion about the key characteristics of PBL). The idea is that in PBL, students should participate in authentic research in which they use and construct their knowledge like scientists would (Novak & Krajcik, 2020 ). Using scientific practices has been found to contribute towards students’ engagement when learning science (Lavonen et al., 2017 ), and PBL does indeed appear to have a positive impact on students’ attitudes and motivation towards science and technology (Kortam et al., 2018 ; Hasni et al., 2016 ). PBL allows students to see and appreciate the connection between scientific practices and the real world, significance of learning, carrying out investigations and the open-endedness of the problems under investigation (Hasni et al., 2016 ).

Nevertheless, according to the review done by Condliffe et al. ( 2017 ), the efficacy of PBL in terms of student outcomes is not entirely clear. In a more recent review, however, Chen & Yang ( 2019 ) found more distinctive benefits to learning compared to previous studies. As they suggest, it may be that implementation of PBL has developed between 2000 and 2010, potentially owing to the better availability of training programmes and materials. Nonetheless, whilst Chen & Yang ( 2019 ) did find that PBL improves students’ academic achievement in STEM (science, technology, engineering and mathematics), they also found that the positive effect of PBL appeared to be somewhat bigger in social sciences compared to STEM subjects. Additionally, the various distinctions between different researchers for what makes PBL different from other closely related instructional approaches, such as inquiry-based and problem-based learning, make it challenging to confidently determine exactly how effective PBL is as an instructional method (Condliffe et al., 2017 ).

However, PBL is supported by governments, researchers, and teachers in many countries (Novak & Krajcik, 2020 ; Condliffe et al., 2017 ; Aksela & Haatainen, 2019 ; Annetta et al., 2019 ; Hasni et al., 2016 ) . Studies have found that teachers consider PBL as an approach that promotes both students’ and teachers’ learning and motivation, collaboration and a sense of community at school level, student-centred learning, connects theory with practice and brings versatility to teachers’ instruction (Viro et al., 2020 ; Aksela & Haatainen, 2019 ). However, regardless of teachers’ enthusiasm towards PBL, they can still struggle with its implementation (Tamim & Grant, 2013 ). PBL is a challenging method to use in practice, as it requires a fundamental understanding of its pedagogical foundations (Han et al., 2015 ), and it appears that teachers tend to have limited and differing conceptions about PBL (Hasni et al., 2016 ). For example, PBL is often defined through its distinct characteristics (Hasni et al., 2016 ; Thomas, 2000 ), but these tend to be unknown to teachers (Tamim & Grant, 2013 ). What is more, research has indicated that in order for PBL to be implemented as it is described by researchers, teachers require training and multiple years of practice with it (Mentzer et al., 2017 ). In fact, students display greater learning gains when their teacher is experienced with PBL (Capraro et al., 2016 ; Han et al., 2015 ), and it appears that partial or incorrect implementation of PBL may even have negative consequences for students’ academic performance (Capraro et al., 2016 ; Erdoğan et al., 2016 ).

Both Viro et al. ( 2020 ) and Aksela & Haatainen ( 2019 ) found that according to STEM teachers, the most challenging aspects of implementing PBL are project organisation (for example, time management), technical issues, resources, student-related challenges and collaboration (Viro et al., 2020 ; Aksela & Haatainen, 2019 ). As PBL requires students to study a certain phenomenon in detail by using scientific practices, it takes longer than more traditional approaches (Novak & Krajcik, 2020 ). Researchers have also reported that teachers consider irrelevance to subject teaching and an unfamiliar teaching style among the significant negative aspects of PBL (Viro et al., 2020 ). Implementation of PBL should focus on teaching twenty-first century skills, being student-centred, and building strong and personal interaction between students and teachers (Morrison et al., 2020 ). This requires both teachers and students to take on new roles. In PBL, teachers are often having to act simultaneously as designers, champions, facilitators and managers, and students are expected to be self-directed learners who are able to endure the ambiguity and open-endedness of PBL projects (Pan et al., 2020 ).

Despite the move towards student-centred approaches (for example, inquiry-based teaching) in many national curricula, such as in the United States (National Research Council, 2012 ), Finland (Lähdemäki, 2019 ) and throughout much of Europe (European Commission, 2007 ), there is a distinct lack of research about PBL that is initiated by teachers (Condliffe et al., 2017 ). There is very little research into how teachers understand and use PBL when they are not guided by university researchers, and the models they develop for its implementation (Hasni et al., 2016 ). It is also important to research what kinds of changes teachers make to PBL curricula to adapt them to their classes, and how this process could be supported (Condliffe et al., 2017 ). Often the reality in classrooms differs from the visions in curricula (Abd-El-Khalick et al., 2004 ), and simply reforming the science curricula does not mean that teachers understand how to implement the new concepts into their teaching (Severance & Krajcik, 2018 ). In order to gain a better understanding of how teachers implement PBL and the related possibilities and challenges in practice, and to promote the use of PBL in education, PBL units from K-12 schools were studied from the perspective of key characteristics of PBL. The studied schools were from several different countries and they all had participated in the international StarT programme ( https://start.luma.fi/en/ ) by LUMA Centre Finland (see ‘Participants’).

Key characteristics of PBL

Most projects done at schools are not considered to be PBL, as PBL is often defined more specifically through its distinct characteristics (Hasni et al., 2016 ; Thomas, 2000 ), also referred to as ‘design principles’ (Condliffe et al., 2017 ). However, there is still ambiguity among researchers about what the exact key characteristics or design principles of PBL are (Condliffe et al., 2017 ; Hasni et al., 2016 ). Krajcik & Shin ( 2014 ) propose the following six features as key characteristics of PBL: (1) driving question, (2) learning goals, (3) scientific practices, (4) collaboration, (5) using technological tools, and (6) creating an artefact. These characteristics, including their purpose and features, have been discussed based on the literature review in Table 1 .

In this study, the PBL units were researched by using the six key characteristics found in Table 1 as a framework (Krajcik & Shin, 2014 ). The categories in the content analysis (see Table  2 in ‘Methods’) were based on these characteristics. At the time of doing the analysis, the model proposed by Krajcik & Shin ( 2014 ) was the most recent and detailed description of the characteristics of PBL that allowed study into the quality of the PBL units in practice. Additionally, their framework is in line with the views of other authors who focused on the characteristics of PBL, including the recent systematic review by Hasni et al. ( 2016 ) into the characteristics of STEM PBL used by researchers, and with the reviews done by for example, Condliffe et al. ( 2017 ) and Thomas ( 2000 ). However, in order to study the quality of PBL units under each of the characteristics, the framework was developed further by using the most current literature. For example, the phases of inquiry-based learning (Pedaste et al., 2015 ) were used to study how scientific practices were carried out by the schools.

Most earlier science education studies have looked at teachers’ perceptions of PBL through questionnaires and interviews (Hasni et al., 2016 ), but this study analysed teachers and students’ reports of their projects in practice. Considering the widely recognised challenges in the implementation of PBL, and the shift in many national curricula towards PBL and similar approaches, there is an urgent need to understand how teachers are managing the change, and what kinds of models they are developing for the implementation of the new curricula in their classrooms. The aim of this study is to understand possibilities and challenges related to the implementation of PBL in practice through the key characteristics (Table 1 ). The detailed research questions are: (1) Which key characteristics of PBL do teachers implement in the projects? and (2) How do teachers implement these characteristics in practice?

This study was carried out as a multiple-case study (Yin, 2014 ) on schools that participated in the international StarT programme by LUMA Centre Finland from different countries. A multiple case study allows for comparison between the differences and similarities between the cases (Yin, 2014 ), and therefore to gain a preliminary idea of characteristics or issues that might be common across the schools. The PBL units of twelve K-12 schools were studied (see ‘Participants’ for further details on the selection criteria). The schools participated in the international StarT competition organised by LUMA Centre Finland ( https://start.luma.fi/en/ ) during the academic year of 2016–17 or 2017–18.

The StarT programme

StarT encourages teachers to share their best models for implementing PBL, and students to present the products and research they have done within their groups (StarT programme). The competition has two categories: teachers’ descriptions of the PBL units that were carried out by the schools (‘ best practices’ ), and ‘ students’ projects ’ that describe what individual student groups studied, created and learned during the school’s PBL unit. Each school was able to upload one entry to the teachers’ category, describing the implementation of the project unit from teachers’ perspective as a best practice for other schools, and an unlimited number of students’ projects related to this unit. As such, each ‘ student project ’ is part of the same PBL unit organised by the school, but it describes what one student group produced under the PBL unit implemented by the teachers. Depending on the school and how much freedom the students had in the PBL unit, the student groups might have had completely different research topics, or they might have just produced slightly different artefacts to the same problem.

To participate in each category, the schools needed to upload a three-minute-long video describing the best practice or the project and to answer questions on an online form. Additionally, student groups were required to upload a learning diary, the format of which could be freely chosen. As such, the schools had significant freedom in terms of what they wanted to report about their PBL units. At the time of the data collection, the participants did not receive any professional development training from StarT, but depending on how closely they followed the online channels of StarT, they had access to project ideas and videos from other participants via the programme website, and the programme also included voluntary webinars and newsletters. However, these materials were freely available to anyone on the internet, and participating in the competition did not require any other engagement with the StarT programme.

Content analysis

Deductive content analysis is suitable for research that aims to study an existing model or theory (Hsieh & Shannon, 2005 ). The key characteristics of PBL shown in Table 1 were used as a basis for the deductive and inductive content analysis, where it was determined which characteristics teachers implemented in the projects, and how they did this. In qualitative content analysis, data is analysed by reducing it to concepts that describe the studied phenomenon, for example, through pre-defined categories, whilst also acknowledging the themes rising from the data (Elo et al., 2014 ; Cohen et al., 2007 ). The final categories used in the deductive analysis, and discussion about decisions regarding them, can be seen in Table 2 . The data was looked at inductively within these categories (Marshall & Rossman, 2014 ). An example of the coding combining inductive and deductive content analysis is given in Table  3 .

The analysed materials ( n  = 12 project units and n  = 17 students’ projects; see details under ‘Participants’ and in Table  5 ) were written responses to questions on an online form, videos and learning diaries. The units considered in the analysis were words, sentences, and paragraphs from verbal communication. As the students’ projects were what individual student groups produced within the PBL unit of the school, all of the materials provided by an individual school were considered as an entity when studying how the school carried out PBL. Therefore, there was no differentiation between the source of the information (for example, learning diary or best practice video) but instead all materials from a single school were treated as equal evidence of how the characteristics of PBL were implemented (see Table 3 ). However, since two schools provided multiple student groups’ works as student projects, and there were differences in the approaches that different student groups took to carrying out their project work, also the number of student projects displaying each of the key characteristics is included in Table  6 under ‘Results’.

In order to see how the six key characteristics of PBL were distributed across the projects, the overall frequencies of characteristics displayed in a project unit (1 = present, 0 = not present) were counted. Table  4 displays the sections from the coding framework that were included in the frequency count. Each row in the second column was counted as ‘1’ if it was observed and as ‘0’ if it was not. Including these features in the frequency count allows a satisfactory picture of the distribution of the key characteristics across the studied schools to be drawn (See Fig.  1 and Table 6 ). Scientific practices are emphasised in the count due to their many subcategories, but this was deemed appropriate since they are a good indication of how inquiry-based and student-led the projects were. Learning goals and gains have a significant role too, but their role is similarly justified by their importance – they determine largely whether the projects have resulted in their intended purpose, learning. The results regarding the implementation and distribution of the key characteristics can be found under ‘Results’.

In order to improve the reliability and validity of the study, triangulation was employed (Turner et al., 2017 ) through the use of different types of materials as sources of information. This increases the reliability of studies looking at human behaviour (Cohen et al., 2007 ) and case studies (Yin, 2014 ), as that allows cues from different sources to be combined into a more representative image of a case (Baxter & Jack, 2008 ). Firstly, the materials consisted of three different types of media: written descriptions and answers to questions on an online form, videos, and a learning diary, the medium of which was not pre-defined for the participants. Secondly, the studied schools only consisted of learning communities that had participated in both the teacher category of StarT with a ‘best practice’ (a description of the PBL unit from teachers’ point of view) and the student category with at least one ‘student project’ (description of the work one student group did during the PBL unit). As such, this study includes the viewpoints of both teachers and students. Additionally, the results from coding were agreed upon by both of the authors.

Participants

The study analysed students’ projects and teachers’ best educational practices at K-12 school level ( n  = 12 project units and n  = 17 students’ projects; see Table 5 for details) that were implemented in 2016–2017 or 2017–2018. The projects were mostly ( n  = 9) created and implemented by teachers and students, and as such they reflect the reality of schools when it comes to implementing PBL. Only n  = 3 schools mentioned that they had participated in a (university-led) development programme. As such, the studied PBL units provide a plausible reflection of the reality of active teachers implementing PBL (see ‘Limitations’ for further discussion).

The studied PBL units within the theme ‘Nature and environment’ were chosen from the learning communities that participated in the international StarT programme in 2016–2017 and in 2017–2018. The other themes that the StarT participants could choose for their projects were ‘Technology around us’, ‘Mathematics around us’, ‘This works! A mobile toy’, ‘Stars and space’, ‘Well-being’, ‘Home, culture and internationality’. ‘Nature and environment’ was the most popular single theme during both years of data collection: n  = 132 learning communities from all n  = 277 learning communities indicated that they had done a project related to it in 2016–2017, and n  = 50 out of n  = 229 in 2017–2018. Whilst the studied projects focus on the theme ‘nature and environment’ in the context of biology education, the interdisciplinary nature of the theme makes the results largely applicable for other sciences. The decision to base the study on a single discipline was made in order to gain a more detailed understanding of the implications of STEM PBL for subject teaching; the case in this study focusing on teaching biology through PBL.

The first criteria in selecting the cases for this study was to include only PBL units implemented by K-12 school (ages 7 to 18). Additionally, only projects themed ‘Nature and environment’, where biology had a clear role, were included. Finally, only schools that had provided full sets of materials used in the analysis (written responses, videos and learning diaries) were included. Full sets of materials were required for both teachers’ descriptions of the PBL unit and students’ projects, either in English or Finnish (one school had to be excluded due to an insufficient level of English).

Table 5 presents participants and their school levels: 12 schools matched the criteria described above. In total, 12 project units and 17 students’ projects were analysed, with only two of the schools having provided more than one student project as a part of the project unit. 11 of the studied schools were from six different countries in Europe, and one school was from Southwest Asia. Schools D, E and F (Table 5 ) participated in the same PBL development programme implemented by a local university.

The participants gave permission for using their materials for research purposes upon their participation in StarT. However, as this study looks at the projects from an evaluative perspective, direct quotations or detailed descriptions of individual cases that could be used to identify the schools were not included.

The results for each of the research questions (see end of the chapter “Key characteristics of PBL”) will be presented separately.

(1) The key characteristics of PBL in the projects

The most frequently displayed key characteristics of PBL were collaboration, artefacts, technology, problem-centredness, and out of scientific practices, carrying out research, presenting results and reflection (see Table 6 for more detail). At least some form of collaboration (either between the students, between teachers or with outside partners) took place in all but one of the schools. Any interaction that the schools described as having taken place between different actors was considered as collaboration. Furthermore, technology was used as a part of the projects in all of the schools. Artefacts were also created in all of the studied projects. The results for each of the characteristics are summarised in Table 6 (research question 1), which also outlines how they were implemented (research question 2). As n  = 2 schools provided multiple projects by different student groups, the number of projects ( n  = 17) is higher than the number of schools ( n  = 12).

Regarding scientific practices that students participated in, presenting results (n = 12 schools), interpreting results ( n  = 11) and reflection ( n  = 10) were most commonly demonstrated. However, not all schools ( n  = 4) displayed clearly that students had done any research (such as searching for information, observation and collecting data). As testing hypotheses was not visible in any of the projects ( n  = 0), according to the definition of Pedaste et al. ( 2015 ), the research was considered as” exploration” ( n  = 8) instead of” experimentation” (n = 0). Only n = 4 schools included a mention of students having presented questions that had an impact on the course of the project or the investigations that were carried out.

Driving questions and learning goals were among the key characteristics that were not described well (Table 6 ). None of the twelve schools that were studied displayed evidence of having used a driving question in their projects. However, the majority of the schools (n = 8) did centre their projects around solving a single problem. According to PBL literature, this is not the same as having a driving question (see Table 1 for a more detailed description), but in the absence of driving questions it was considered useful to study whether the projects were at least centred around solving a single problem. Learning goals (goals with a reference to students’ development) were also not that commonly described; materials from n  = 6 schools displayed learning goals set by teachers, but none of the schools displayed learning goals set by students. However, students did appear to set practical goals (goals with no reference to students’ development) in the projects from n  = 3 schools, and teachers mentioned these in most schools too ( n  = 9). Furthermore, students’ descriptions of what they had learnt as a result of the projects were visible in the materials of n  = 10 schools, whereas teachers’ comments regarding that were only visible in those of half ( n  = 6) of the schools.

Figure  1 displays the distribution of the characteristics across the project units. The highest frequency values were for the schools E and F, which both had participated in the same development programme organised by a local university. However, although they did not receive help from researchers, schools A (f = 18), I (f = 17) and C (f = 16) still displayed a reasonably high count of PBL characteristics. In fact, school C had the same frequency of PBL characteristics as school D, which was the third school to participate in the university-led development programme. Figure 1 shows that there is a clear difference between schools whose PBL units were most closely in line with the PBL framework used in this study (f = 21, n  = 2) and the schools that provided project units with the least resemblance to it (f = 9, n = 2).

Frequency of the PBL characteristics demonstrated by the schools A-L ( n  = 12, see Tables  4 and 5 )

(2) Implementation of the key characteristics in the projects

The main results regarding the implementation of the key characteristics are summarised in Table 6 , together with their visibility. The detailed description about the implementation of each of the key characteristics of PBL can be found below: (1) driving question, (2) learning goals, (3) scientific practices, (4) collaboration, (5) using technological tools, and (6) creating an artefact.

Using central problems instead of driving questions did not stop schools from accomplishing some of the characteristics of a good driving question. In all of the schools where the project had a central problem, the problems were related to environmental issues, which meant that they were regarded as socio-scientific issues (Sadler, 2009 ). All of these schools also used local or familiar learning environments, which is another characteristic of a good driving question. For example, they researched everyday phenomena ( n  = 7 projects), used family or peers as audience ( n  = 6), created an impact on the local environment (n = 6) or studied it ( n  = 5). Some also visited local attractions ( n  = 2) or collaborated with students’ families (n = 2).

Interestingly, teachers and students seemed to report different kinds of learning gains; students focused on learning biology ( n  = 7 schools) more than teachers ( n  = 3), who paid attention to progress in learning social skills ( n  = 6), other twenty-first century skills ( n  = 2) and scientific practices ( n  = 2). Students reported these respectively in n  = 4, n  = 1 and n  = 0 schools. Furthermore, teachers did not mention students’ personal development (for example, new perspectives and experiences), which the students themselves noted in n  = 2 schools. Students also mentioned development of their environmental values more often ( n  = 4 compared to teachers in n  = 2 schools). ICT skills were mentioned in n  = 2 schools by students and n  = 1 by teachers.

When words that referred to the students’ development (for example, “develop”, “apply” or “learn”) were used in conjunction with the aims of the project, the goal was interpreted as a learning goal. However, when they were absent, the goal was interpreted as a concrete practical aim (for example, “creating an herb garden”). N  = 5 projects displayed practical goals set by students, all of which were related to biology too. However, none of the goals set by students were learning goals according to the definition described above; they all focused on the practical aims of the work instead. Learning goals set by teachers included learning related to biology ( n  = 5 schools), scientific practices ( n  = 4), social skills ( n  = 3), other twenty-first century skills ( n  = 1) and technical skills ( n  = 1). The learning goals related to biology could be divided into values ( n  = 5 schools), content ( n  = 3) and skills ( n  = 1).

The materials of the study did not allow extensive assumptions about what was teachers’ and what students’ viewpoint, but in terms of learning goals, it was deemed necessary to make a distinction based on the sentence structures. If a continuous part of the text displayed students as implementers and was written in third person (for example, “in this project students are expected to …” or “their goal is to …” ), the learning was interpreted as having been set by the teacher. However, if a continuous part of the text was presented in first person and the text clearly displayed that “we” referred to students, the part of the text that described learning was interpreted as students’ viewpoint to learning.

With regards to different scientific practices, it was not possible to identify how student-led the implementation was due to lack of teachers’ and students’ comments on this. Hypotheses were not presented in any of the projects, although n  = 8 projects included experiments that could have included a hypothesis. The three projects that did not show any signs of doing research and interpreting data were all from the same school and generally vaguely described; these projects did not show evidence of students drawing conclusions either. As all projects were presented to others at least through the video that was shared to StarT, all of them were considered as having presented the results of the project. However, all but one project described having done that in other ways as well, for example, by giving presentations for younger students and parents, and making posters.

Most of the projects were carried out in various learning environments and with a variety of partners. In terms of collaboration, three categories emerged: collaboration between students ( n  = 11 schools), collaboration between teachers ( n  = 9), and collaboration between the school and outside actors ( n  = 9). Collaboration between students was mostly group work ( n  = 16 projects) or presenting the work for other students ( n  = 9 projects). Teachers collaborated mostly with other teachers in the same school ( n  = 8 schools), and in some cases with teachers from another school ( n  = 4); however, n  = 3 of these schools participated in the same development programme of a local university, and this university organised the event where the collaboration happened. The materials did not provide information of how the teachers collaborated with each other or divided tasks. The outside partners were students’ parents ( n  = 9), universities ( n  = 5), media ( n  = 5), museums ( n  = 5), municipalities or other public agencies ( n  = 4), local people ( n  = 3), other experts ( n  = 3), and organisations ( n  = 2).

Technology used by students in their projects could be divided into two categories that emerged from the materials: ICT (information and communication technologies) and technology that was used as a scientific research tool. All technology that is commonly available and used at homes (and schools), such as editing videos, programming and text editing, and calculation programmes, was included in the ICT category. Any technology that is not commonly expected to be found at homes but that can be used to do scientific measurements and observations (for example, pH probes and nitrogen indicators, microscopes and voltage meters) was considered as scientific technology. According to this definition, students used scientific technology in n  = 6 projects and ICT in n  = 15 projects.

The artefacts included for example, reports, slideshows, lessons, webpages and miniature models. Multiple artefacts were created in majority of the projects ( n  = 14). Different categories emerged depending on what the role of these artefacts was in the project. In n  = 2 projects, the artefacts were part of a larger, final artefact. For example, one of the schools developed a webpage on climate change, and the contents of the webpage (for example, campaign videos and articles) were produced by separate student groups. Whilst multiple artefacts were created in many projects, it was more common for them to complement each other, meaning that they dealt with the same topic by answering it from a slightly different angle (n = 6 projects). In one of these projects, students had, for example, created both a video and a slide show on the same topic, or both a written report and a physical miniature model.

In the third category, in which multiple artefacts were made, students created artefacts that dealt with the same theme but did not directly attempt to answer the same question ( n  = 5). These artefacts were the result of multiple activities that were separate from each other. For example, in one project, students created weather maps, recorded air pressure, and made art related to weather. Although all of these activities were related to the same theme, they were clearly separate from one another, and they did not aim to solve a common problem. In the rest of the projects ( n  = 4), only one clear artefact was produced. In n  = 2 of these projects, the artefact was relatively simple, and the materials did not give evidence of students having had to carry out significant research or experimentation in order to create it. In the other n = 2 projects, the artefact was clearly a complex technical product, such as a miniature model of an energy-efficient house or an irrigation system for plants. These projects displayed evidence of the students having done smaller experiments to be able to create the final artefact. However, as the results of these experiments were not turned into clear artefacts, these artefacts were considered as separate from the first category (‘single artefacts form the final artefact’).

The main aim of this study was to understand the possibilities and challenges related to the implementation of key characteristics of PBL. These aims will be discussed in relation to each of the research questions below.

Key characteristics of PBL implemented by the teachers

This study shows that within the context of K-12 science education, using PBL creates opportunities for the implementation of the following key characteristics (Krajcik & Shin, 2014 ): collaboration, artefacts, technology, problem-centredness, and scientific practices (Table 6 ; carrying out research, presenting results, and reflection). However, it might also be true that these characteristics are generally commonly implemented at schools, or aspects of social constructivism or PBL familiar to teachers. For example, Viro et al. ( 2020 ) found that teachers saw development of teamwork skills among the most important characteristics of PBL. However, both Viro et al. ( 2020 ) and Aksela & Haatainen ( 2019 ) also found that teachers consider technical issues and collaboration as significant challenges in science PBL; as such, teachers’ attention may have been directed to describe the use of these practices in their project reports.

This study indicates that schools might struggle especially with implementing driving questions, using students’ questions, and having students set their own learning goals (see ‘Teachers’ implementation of the key characteristics’ for further discussion). Notably, the characteristics that were commonly visible in the studied PBL units were also well-aligned with the StarT format that promotes their implementation (StarT programme). As such, there might be potential in encouraging teachers to implement certain characteristics of PBL through a competition and its instructions and assessment criteria. For example, StarT does not mention driving questions, and although ¾ of the projects were centred around solving a problem, no driving questions were visible. Similar to this study, Haatainen & Aksela ( 2021 ) found that only half of the 12 StarT schools they studied included driving questions in their projects. Driving questions have previously been identified as the most challenging aspect of PBL (Mentzer et al., 2017 ), but it is likely that the studied teachers were not even familiar with the concept as there were no mentions of this ‘hallmark’ of PBL. Based on the results, it might be worthwhile to include the framework used in this study more visibly into the StarT programme in order to direct the teachers’ attention to the desired characteristics. However, although advocated for by StarT (StarT programme), students’ questions were hardly visible at all. Goals set by students were also rare ( n  = 3 schools), and none of them showed signs of learning goals set by students (see next section for further discussion).

Teachers’ implementation of the key characteristics

Artefacts and driving questions would seem to require further instruction. Nearly half of the schools produced single artefacts that resulted from separate activities only linked together through a common theme. Artefacts should, however, answer the driving question and draw the project together (for example, Mentzer et al., 2017 ). Although there were no driving questions, many of the projects that were centred around solving a problem still managed to demonstrate other characteristics of PBL and the qualities of a good driving question well (centred around solving a problem, use of socio-scientific issues, and local or familiar learning environments). This is in line with the findings of Morrison et al. ( 2020 ), who found that teachers are very aware of the importance of authenticity and working with real-world problems in PBL. However, although the driving question can be replaced with a central problem (Hasni et al., 2016 ), it has an important role in unifying the activities within a PBL unit (Thomas, 2000 ). Judging by the artefacts, many of the projects lacked the kind of unity described in literature, especially those with no central problem or one that was defined broadly. Therefore, the observations from this study support the views of Mentzer et al. ( 2017 ), Krajcik & Shin ( 2014 ) and Blumenfeld et al. ( 1991 ) on the importance of a driving question on unifying the PBL unit.

As only half of the schools displayed learning goals and many of the projects mentioned that they had been carried outside of regular lesson time, it seemed like most of the projects were not primarily used as a means to learn central concepts. According to Thomas ( 2000 ), this is not PBL, but Tamim & Grant ( 2013 ) suggest taking a broader outlook on what is considered PBL. Nevertheless, as collaboration, time and organisation of the projects have previously been found to be among the aspects of PBL that teachers find challenging (Viro et al., 2020 ; Aksela & Haatainen, 2019 ), it is not surprising that teachers would prefer to use PBL outside of regular lesson time and focus on developing students’ soft skills, rather than focusing on content acquisition. However, spending sufficient time and covering central content have been identified among the central variables for successful PBL teaching in science education (Tal et al., 2006 ), in addition to building strong teacher-student relationships (Morrison et al., 2020 ). This indicates that for PBL to be a truly useful method for teachers, the recent changes in curricula towards less content and covering more skills (Novak & Krajcik, 2020 ) need to be sustained, and these changes need to be reflected in the standardised tests too.

The learning goals mentioned by the teachers were well aligned with the learning gains associated with PBL (for example, scientific practices, social skills and other twenty-first century skills, environmental values), but this does not equal working with concepts central to their curricula. Furthermore, for students to benefit from the learning gains associated with PBL, the focus should be on learning rather than doing a project; the teachers’ attention should be on what the students can research and find out, instead of focusing on what students can create and do (Lattimer & Riordan, 2011 ). Mentzer et al. ( 2017 ) found that projects implemented by teachers who had used PBL for no longer than a year did not resemble a coherent research project, and that this changed only after two or three years of PBL implementation. The projects tended to be a collection of lessons that were poorly connected to each other, and that consisted of either highly structured activities that had the same pre-defined outcome for all students, or of activities in which the main purpose was to research without a clear outcome (Mentzer et al., 2017 ). Similarly, in this study, the projects were often a collection of separate activities tied together through a common theme. According to Blumenfeld et al. ( 1991 ), this could be solved with a good driving question which brings cohesion to the project and ensures that students are working with central concepts and problems.

Although scientific practices were represented generally well across the studied schools, students’ questions were hardly visible, and goals set by students were rare ( n  = 3 schools). As such, it remains unclear how student-led the projects were exactly. For example, Herranen & Aksela ( 2019 ) highlight the importance of training teachers to use students’ questions as the basis of classroom inquiries, as this has clear implications for how authentically the inquiry will resemble that of scientists. Teachers might see PBL as student-centred (Aksela & Haatainen, 2019 ) and use scientific practices in their projects, but the reality is that they can be employed in a highly teacher-led fashion too (Colley, 2006 ). Earlier research into StarT projects indicated that the projects varied from having “complete student autonomy” to having “teacher-led activities with little student choice” (Haatainen & Aksela, 2021 ).

Furthermore, Severance & Krajcik ( 2018 ) found that even with support from researchers, teachers struggled to understand the idea of using scientific practices in their teaching. Also, teachers themselves consider lack of support for PBL implementation, including teachers’ professional skills and motivation, among the most common hindrances to PBL implementation (Viro et al., 2020 ). In line with this, the n  = 3 schools in this study that received support for the implementation of PBL from a university, all displayed a higher count of PBL characteristics and scientific practices than most of the studied schools (Fig. 1 ). However, whilst two of them displayed the highest count of characteristics across all cases, one of them had a lower count, closer to the values of schools that did not receive help. This highlights the importance of providing additional support for the schools in terms of the pedagogy of PBL and implementing scientific practices, and the fact that even support from a university does not guarantee research-based implementation of PBL. Even when teachers implement PBL units designed by researchers, they can adapt the unit significantly when moulding it for their educational context (Condliffe et al., 2017 ). Depending on the teachers’ beliefs, it is likely that all of these adaptations are not beneficial for learning (Condliffe et al., 2017 ).

Additionally, teachers who intended to teach biology through the projects (5/12 schools) mainly focused on developing students’ values towards nature and environment. This can of course be expected as all projects aimed to solve environmental issues, but it should not give a reason to exclude goals related to subject-specific content and skills. Especially, as the data consisted of projects in which biology had a clear role, and the students frequently (7/12 schools) mentioned having learnt biology content. However, the teachers mentioned this in three schools only. The explanation could be that students had a more liberal idea of what constitutes as biology content, or that the teachers had not even attempted to teach core content through the projects, and thus did not pay particular attention to development in that area. Nevertheless, the different views between teachers and students in terms of perceived learning gains may be an interesting point to study in the future.

Overall, it seems like the teachers mainly used PBL for learning soft skills, which is commonly reported about PBL (Guo et al., 2020 ; Aksela & Haatainen, 2019 ). For instance, in a study of PBL in mathematics, Viro et al. ( 2020 ) found that less than half of the in- and pre-service teachers they surveyed ( n  = 64) considered learning mathematics among the three most important characteristics of a successful PBL unit. Other options that they considered as most important for a successful PBL unit in mathematics were all related to student motivation and learning of twenty-first century skills. In line with this, the results indicate a need to emphasise the importance of planning the PBL unit around the core curriculum so that in-depth subject teaching can occur (Grossman et al., 2019 ; Tal et al., 2006 ). Context-based and problem-based approaches to instruction are seen as useful for student learning in biology (Cabbar & Senel, 2020 ; Jeronen et al., 2017 ), but if the focus is not on central concepts, then it remains uncertain how useful the PBL units are from the perspective of academic performance.

Development of twenty-first century skills is vital for solving issues related to sustainability, which makes PBL an attractive approach for teaching topics related to it (Konrad et al., 2020 ). Using environmental issues as the starting point of PBL projects in science education has become increasingly popular, and there is a growing body of evidence of its usefulness as a way to implement STEM PBL (for example, Hugerat, 2020 ; Triana et al., 2020 ; Kricsfalusy et al., 2018 ). This study is in line with that as students stated that their environmental attitudes had developed in several schools ( n  = 4). Teachers mentioned developing students’ environmental values as learning goals of the projects in n  = 5 schools, and n  = 2 schools mentioned that the goal had been reached. However, as the participants of this study had a lot of freedom in terms of what they decided to report about their projects, teachers not explicitly mentioning the development of environmental values does not necessarily mean that the goal was not reached.

Limitations

Content analysis can only focus on what is visible in the materials (Cohen et al., 2007 ). As teachers and students have reported their project work to the StarT competition that searches good models for the implementation of PBL, it can be expected that the teachers would highlight (and instruct their students to highlight) the aspects of PBL that they consider important in the videos and written descriptions that they provided. Consequently, if a certain characteristic of PBL is not visible in their materials at all, it is likely that teachers are either not aware of it or do not consider it that important for the implementation of PBL. However, as participating in competitions such as StarT is usually extra work for the teachers, they might struggle to find the time to provide materials that accurately represent their views on what was essential for the project. Furthermore, the form of reporting was very open-ended (for example, videos and learning diaries). As such, it remains possible that if the instructions for reporting the PBL unit had included specific questions about certain characteristics, teachers might have been able to comment on them. Nevertheless, it remains true that in their reports, teachers would include what they valued and focused on most in their projects.

What is more, as participation in StarT is completely voluntary, it is likely that the sample of teachers and schools studied is limited to those that are already actively interested and implementing PBL. As such, the results cannot necessarily be expected to represent PBL that is carried out in an average classroom; the focus is clearly on teachers who are already actively engaged in PBL and science education programmes. As one would expect, PBL implementation can be greatly influenced by school context and whether it is supported by school leadership or not (Condliffe et al., 2017 ).

A further limitation to the results is the scope of the materials and the limitations they had for determining the extent of student-centredness in projects; only inferences can be ascertained about which decisions were made by the students and which by the teachers. However, the interpretations that were made during the coding process have been carefully described in ‘Methods’. As such, whilst the materials limited the deductions that could be made confidently, the analysis is reliable within said limitations.

The number of separate schools in this study is 12. However, three of them did interact with each other as they participated in the same development programme organised by a local university. Nevertheless, as Stake ( 2000 ) states, the main aim of a case study is not to generalise results but to understand the cases better. The aim of the study is not to claim that the results would be true to all teachers but to gain more understanding of how individual teachers might see PBL and find trends across individual cases.

This study supports the notion that teachers have varying conceptions of PBL and its characteristics (Hasni et al., 2016 ). The study provided new information of PBL that takes place at schools that are active participants in international education competitions, as they have not been researched from the perspective of the characteristics of PBL earlier. As such, it also shows how teachers who are actively engaged in PBL implement the characteristics, therefore giving an idea of what the ‘best-case scenario’ of the implementation of PBL units that are not guided by researchers might be. Additionally, due to the international sample of schools studied, the study is not limited to a specific educational context.

This study provides important information for teacher training, as it has paved the way into studying the quality of PBL units created by teachers as opposed to those created by researchers through the lens of key characteristics of PBL. Based on the results, the authors believe it is important to ensure that teacher training and curriculum development consider how teachers can use PBL to teach central content, and how schools can better support teachers to carry this out in terms of resources and time.

In line with Morrison et al. ( 2020 ) and Tsybulsky & Muchnik-Rozanov ( 2019 ), the authors believe it would be important for teachers themselves to learn through PBL during their pre-service training. Furthermore, for teachers to be able to fully grasp the pedagogical approach required in PBL, both teacher training and research should consider the key characteristics and their implementation, especially those that have been shown to cause more difficulties for teachers through this and earlier studies (for example, teaching central content, students’ questions and driving questions). Additionally, it may be useful to direct efforts into studying the key characteristics from the perspective of flexible implementation; which of the characteristics should be followed rigidly, and could some of them be interpreted more flexibly to suit local educational contexts better? For example, considering the importance placed on the driving question in PBL literature, and the difficulties in its implementation, it would be useful to understand how the characteristic could be contextualised into a format that is more easily accessible to teachers.

Finally, a viable framework was created for analysing how the key characteristics of PBL were implemented in teachers’ projects. It can be adapted for studying PBL units also in other settings. The used approach to analysing project units can also be used as a starting point for studying PBL artefacts, which has been advocated for by Guo et al. ( 2020 ) and Hasni et al. ( 2016 ). What is more, it allows studying PBL from the point of view of students, which has also been done clearly less in PBL research (Habók & Nagy, 2016 ).

The authors believe that research should continue to address PBL units from the perspective of the key characteristics of PBL. This allows research to be grounded in the practice of schools, and for researchers to pinpoint the most critical aspects of PBL that professional development initiatives should focus on. PBL remains a challenging instructional method and a lot more training and resources are still needed for it to live up to its potential. The results from this study and the constructed framework of key characteristics can be useful in promoting research-based implementation and design of PBL science education, and in teacher training related to it.

Abbreviations

• Project-based learning

Science, technology, engineering and mathematics.

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Markula, A., Aksela, M. The key characteristics of project-based learning: how teachers implement projects in K-12 science education. Discip Interdscip Sci Educ Res 4 , 2 (2022). https://doi.org/10.1186/s43031-021-00042-x

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Project-Based Learning Benefits [Research-Based]

While the roots of project-based learning can be traced back to the late 19th century— and American educational philosopher John Dewey —this experiential form of learning has existed since the beginnings of humankind. Researchers and educational reformers have only begun to study it in earnest in the past few decades, as efforts have increasingly been made to identify instructional methods that serve the broadest possible range of learners. 

Here, we look at what some of the research tells us about the effectiveness of hands-on learning in pre-K–12 classrooms.

Project-Based Learning Overview

Project-based learning (PBL) is an instructional approach that offers students the chance to acquire knowledge and skills by actively engaging in projects that simulate real-world challenges and problems. This approach emphasizes learning through hands-on experiences; as such, it is often referred to as “learning by doing,” “experiential learning,” or “discovery learning.”

PBL is used in classrooms from preschool to 12th grade and manifests in many different types of projects. Examples of PBL include classroom debates, field trips, mock trials, community service projects, and much more—the possibilities are nearly endless.

12 project-based learning engagements to start using today >>

Is PBL Effective? What Research Reveals

The philosophy behind PBL is that it reflects the project-based nature of our daily tasks and careers. Rather than simply memorizing content or completing rote assignments, PBL empowers learners to break down problems and work with diverse stakeholders to implement effective solutions. Though most young learners who engage in PBL are years away from their first career, hands-on engagements that connect to real-world challenges can help them build skills like self-sufficiency, creativity, and critical thinking—all endlessly applicable to the working world.

Though research is ongoing, multiple studies have found PBL to effectively promote student learning in social studies, science, math, and literacy. Research from the Buck Institute for Education suggests that PBL is a valid instructional method for all students, but particularly those who face educational disparities such as language barriers, learning difficulties, or limited access to educational resources.

For example, in a series of studies conducted by Lucas Education Research in 2021, researchers found that PBL had a significant effect on improving learning equity. These findings were particularly timely, since the COVID-19 pandemic had revealed vast inequities in American public schools, evident in learning loss, lower student engagement, stalled progress, and other indicators of student performance. The studies found that schools that implemented PBL — many with the aid of federal relief funds — experienced the following results:

• High school students who engaged in PBL in Advanced Placement (AP) courses outperformed students in traditional classrooms on AP exams, regardless of socioeconomic status.
• Students (including English language learners) in high-poverty, diverse middle schools who participated in PBL science programs performed better on multi-subject assessments than peers receiving traditional science instruction.
• Second-grade students in low-income, low performing schools made five to six more months of learning gains in social studies with PBL instruction and two to three more months of gains in informational reading for the year as compared to peers in non-PBL courses.

While further research is necessary to establish a causal relationship between PBL and student outcomes, the existing evidence supports PBL as a viable solution in improving educational equity and individual student outcomes.

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Elements of Project-Based Learning

According to the Buck Institute , project-based learning engagements alway comprise seven essential elements:

• Centers around a big and open-ended question, challenge, or problem
• Follows an inquiry-based process that stimulates curiosity and generates questions
• Incorporates concepts or skills that students should already know
• Requires using 21st-century skills such as critical thinking, communication, collaboration, and creativity
• Builds student choice into the process
• Provides opportunities for feedback and revision
• Requires students to present their problems, research processes, methods, and results

Of course, there can be many, many variations between different types of PBL engagements, and each element will manifest in unique ways depending on the classroom, subject, school, and age level.

Project-Based Learning Benefits

It is admittedly difficult to study the results of PBL, since implementation varies so widely. Nevertheless, there are numerous benefits made apparent through improved school performance, published research , and classroom observations.

Helps students build 21st-century skills: Project-based learning helps develop essential life skills such as teamwork, problem-solving, research gathering, time management, information synthesis, utilization of high-tech tools, personal and social responsibility, visualization, decision-making, and project management.

Teaches critical thinking: Arguably another essential life skill, PBL encourages students to think critically to solve complex problems and make informed decisions.

Connects students to the world beyond the classroom: PBL establishes connections between students’ learning experiences and the real world , bridging the gap between theory and practice.

Improves student attitudes toward education: Having the opportunity to apply their skills to tangible projects increases student engagement and enthusiasm for learning.

Builds motivation: By providing students with meaningful and relevant projects, PBL enhances their motivation to learn and explore new concepts and ideas—a skill they’ll carry with them for life.

Reinforces social and emotional learning (SEL): Along with building academic or “hard” skills, PBL supports the development of social and emotional skills such as collaboration, communication, empathy, and self-awareness.

Sparks creativity and curiosity: Through project-based learning, students are encouraged to think creatively, explore their interests, and pursue their curiosity, leading to a deeper level of engagement.

Supports in-depth understanding: PBL engagements encourage students to delve deeply into a subject, improving their odds of gaining and retaining a comprehensive understanding of the content.

Supports long-term retention: The hands-on nature of PBL promotes better retention of knowledge and skills as students apply what they have learned to real-world scenarios.

Empowers students: PBL supports students’ autonomy, fostering self-confidence, skills mastery, and a sense of purpose.

Encourages perseverance: Through overcoming challenges and obstacles during PBL engagements, students develop resilience, perseverance, and a growth mindset.

Allows for differentiation: The experiential nature of PBL accommodates diverse learning styles and abilities, allowing for individualized learning experiences and tailored instruction.

Promotes lifelong learning: PBL cultivates a passion for learning and equips students with the skills and mindset needed for lifelong curiosity beyond the classroom.

Bill Laurienti

Bill Laurienti is a Content Marketing Specialist at Creative Learning Systems. He holds a Bachelor of Arts in Secondary Education (English) from Colorado Mesa University and a Master of Arts in Secondary Teaching from the University of California's Rossier School of Education. Bill came to CLS after 10 years in the secondary classroom. He believes SmartLabs are important tools for engaging unengaged students and helping them access careers they might not otherwise have imagined.

Increasing Student Outcomes With Project-Based Learning

With project-based learning (PBL), students tackle problems that connect them to their lived experience, thereby increasing their classroom engagement. And when students actively participate in their learning, their knowledge retention expands. 

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Because Project Based Learning engages students in learning that is deep and long-lasting, and inspires for them a love of learning and personal connection to their academic experience.

Impact on students

PBL blends content mastery, meaningful work, and personal connection to create powerful learning experiences, in terms of both academic achievement and students’ personal growth.

PBL can be transformative for students, especially those furthest from educational opportunity. Now more than ever, we need young people who are ready, willing, and able to tackle the challenges of their lives and the world they will inherit - and nothing prepares them better than Project Based Learning.

Here are just some of the ways that PBL transforms students' educational experiences:

– Engaged hearts and minds

Students actively engage with PBL projects that provide real-world relevance for learning. Students can solve problems that are important to them and their communities.

– Deeper learning

PBL leads to deeper understanding and greater retention of content knowledge. Students are better able to apply what they know to new situations.

– Exposure to adults and careers

Students interact with adults, businesses and organizations, and their community, and can develop career interests.

– A sense of purpose

A great project can be transformative for students. Seeing a real-world impact gives them a sense of agency and purpose.

– Success skills

Students gain skills valuable in today’s workplace and in life, such as how to take initiative, work responsibly, solve problems, collaborate in teams, and communicate ideas.

– Rewarding teacher relationships

Teachers work closely with active, engaged students doing meaningful work, and share in the rediscovered joy of learning.

– Creativity and technology

Students enjoy using a spectrum of technology tools from research and collaboration through product creation and presentation.

See Stories of PBL Success

Research Studies

Research confirms that PBL can help students be successful in today’s rapidly changing and complex world, by developing a broader set of knowledge and skills as well as improving academic achievement.

Impact of PBL on Student Achievement

Researchers in Michigan show effectiveness of Project Based Learning in high-poverty communities.

See the study (Edutopia)

PBL Helps Students Become Better Decision Makers

Rigorous study demonstrates PBL's power to develop students' reasoning and decision-making about unfamiliar issues.

Read the brief

PBL and 21st Century Success Skills

PBL helps students gain competence in critical thinking, problem solving, and collaboration.

Download the summary (pdf)

Stories of PBL Success

Project Based Learning in Hawaii

PBL Engaging the Disengaged

Superintendent supporting PBL across 80,000-student district

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10 Benefits of Project-Based Learning

• By Tina Shaffer
• July 11, 2018
• Blog , Educators , Parents

Project-based learning (PBL) is more than just a teaching method; it’s an immersive, hands-on experience that ignites curiosity, nurtures creativity, teaches life and career skills, and prepares us for the challenges of the real world. According to research conducted by The Autodesk Foundation , studies have shown that project-based learning is linked to significant improvements in student test scores, attendance and classroom engagement. It also gives teachers the opportunity to build stronger relationships with their students by acting as their hands-on learning facilitator. In essence, PBL is an instructional method where students collaborate with others and “learn by doing.” The same skills learned through PBL are also many of the skills sought by employers.

Check out 10 benefits of project-based learning and how it can better prepare our kids with life skills that set them up for future success.

• Collaboration: Relationships formed during collaboration is a huge part of PBL. Not only do students learn how to work better in groups—providing their own input, listening to others, and resolving conflicts when they arise—they build positive relationships with teachers, which reinforces how great learning is. Students also form relationships with community members when working on projects, gaining insight for careers and beyond.
• Problem Solving: Students learn how to solve problems that are important to them, including real community issues, more effectively—even learning from failure and possibly starting over.
• Creativity: Students apply creative thinking skills to innovate new product designs and possibilities for projects.
• In-Depth Understanding: Students build on their research skills and deepen their learning of applied content beyond facts or memorization.
• Self-Confidence: Students find their voice and learn to take pride in their work, boosting their agency and purpose.
• Critical Thinking: Students learn to look at problems with a critical thinking lens, asking questions and coming up with possible solutions for their project.
• Perseverance: When working on a project, students learn to manage obstacles more effectively, often learning from failure and making adjustments until they’re satisfied with their work.
• Project Management: Students learn how to manage projects and assignments more efficiently.
• Curiosity: Students get to explore their curiosities, ask questions and form a new love for learning.
• Empowerment: Students take ownership over their projects, reflecting on and celebrating their progress and accomplishments.

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Powerful Learning: Studies Show Deep Understanding Derives from Collaborative Methods

Cooperative learning and inquiry-based teaching yield big dividends in the classroom. And now we have the research to prove it.

Today's students will enter a job market that values skills and abilities far different from the traditional workplace talents that so ably served their parents and grandparents. They must be able to crisply collect, synthesize, and analyze information, then conduct targeted research and work with others to employ that newfound knowledge. In essence, students must learn how to learn, while responding to endlessly changing technologies and social, economic, and global conditions.

But what types of teaching and learning will develop these skills? And, just as important, do studies exist that support their use?

A growing body of research demonstrates that students learn more deeply if they have engaged in activities that require applying classroom-gathered knowledge to real-world problems. Like the old adage states, "Tell me and I forget, show me and I remember, involve me and I understand."

Research shows that such inquiry-based teaching is not so much about seeking the right answer but about developing inquiring minds, and it can yield significant benefits. For example, in the 1995 School Restructuring Study, conducted at the Center on Organization and Restructuring of Schools by Fred Newmann and colleagues at the University of Wisconsin, 2,128 students in twenty-three schools were found to have significantly higher achievement on challenging tasks when they were taught with inquiry-based teaching, showing that involvement leads to understanding. These practices were found to have a more significant impact on student performance than any other variable, including student background and prior achievement.

Similarly, studies also show the widespread benefits of cooperative learning, in which small teams of students use a variety of activities to more deeply understand a subject. Each member is responsible not only for learning what is taught but also for helping his or her teammates learn, so the group become a supportive learning environment.

What follows is a summary of the key research findings for both inquiry-based and cooperative learning. First, let's look at three inquiry-based approaches: project learning (also called project-based learning), problem-based learning, and design-based instruction.

Project-Based Pathways

Project learning involves completing complex tasks that result in a realistic product or presentation to an audience. "A Review of Research on Project-Based Learning," prepared by researcher John Thomas for the Autodesk Foundation, identified five key components of effective project learning:

• Centrality to the curriculum
• Driving questions that lead students to encounter central concepts
• Investigations that involve inquiry and knowledge building
• Processes that are student driven, rather than teacher driven
• Authentic problems that people care about in the real world

Research on project learning found that student gains in factual learning are equivalent or superior to those of students in more traditional forms of classroom instruction. The goals of project learning, however, aim to take learning one step further by enabling students to transfer their learning to new kinds of situations, illustrated in three studies:

• In a 1998 study by H.G. Shepherd, fourth and fifth graders completed a nine-week project to define and find solutions related to housing shortages in several countries. In comparison to the control group, the project-learning students scored significantly higher on a critical-thinking test and demonstrated increased confidence in their learning.
• A more ambitious, longitudinal comparative study by Jo Boaler and colleagues in England in 1997 and 1998 followed students over three years in two schools similar in student achievement and income levels. The traditional school featured teacher-directed whole-class instruction organized around texts, workbooks, and frequent tests in tracked classrooms. Instruction in the other school used open-ended projects in heterogeneous classrooms. The study found that although students had comparable learning gains on basic mathematics procedures, significantly more project-learning students passed the National Exam in year three than those in the traditional school. Although students in the traditional school "thought that mathematical success rested on being able to remember and use rules," according to the study, the project-learning students developed more flexible and useful mathematical knowledge.
• A third study, in 2000, on the impact of multimedia projects on student learning, showed similar gains. Students in the Challenge 2000 Multimedia Project , in California's Silicon Valley, developed a brochure informing school officials about problems homeless students face. The students in the multimedia program earned higher scores than a comparison group on content mastery, sensitivity to audience, and coherent design. They performed equally well on standardized test scores of basic skills.

Other short-term, comparative studies demonstrated benefits from project learning, such as increases in the ability to define problems, reason with clear arguments, and plan projects. Additional research has documented improvements in motivation, attitude toward learning, and work habits. Students who struggle in traditional instructional settings have often excelled when working on a project, which better matches their learning style or preference for collaboration.

Students as Problem Solvers

Problem-based-learning approaches are a close cousin of project learning, in which students use complex problems and cases to actively build their knowledge. Much of the research for this approach comes from medical education. Medical students are given a patient profile, history, and symptoms; groups of students generate a diagnosis, conduct research, and perform diagnostic tests to identify causes of the pain or illness. Meta-analyses of multiple studies have found that medical students in problem-based curricula score higher on clinical problem solving and performance.

Use of problem-based cases in teacher education has helped student teachers apply theory and practical knowledge to school contexts and classroom dilemmas; these cases, for example, have enabled teachers to take alternative perspectives to better appreciate cultural diversity.

Studies of problem-based learning suggest that it is comparable, though not always superior, to more traditional instruction in teaching facts and information. However, this approach has been found to be better in supporting flexible problem solving, reasoning skills, and generating accurate hypotheses and coherent explanations.

Learning Through Design

Design-based instruction is based on the premise that children learn deeply when they create products that require understanding and application of knowledge. Design activity involves stages of revisions as students create, assess, and redesign their products. The work often requires collaboration and specific roles for individual students, enabling them to become experts in a particular area.

Design-based approaches can be found across many disciplines, including science, technology, art, engineering, and architecture. Design competitions for students include the FIRST robotics competitions and Thinkquest , for which student teams design and build Web sites on topics including art, astronomy, computer programming, foster care, and mental health.

Thinkquest teams are mentored by a teacher who gives general guidance throughout the design process, leaving the specific creative and technical work to the students. Teams offer and receive feedback during a peer review of the initial submissions and use this information to revise their work. To date, more than 30,000 students have created more than 7,000 Web sites through this competition.

Few studies have used a control group to evaluate the impact of the learning-by-design model, but in a 2000 study by researchers C.E. Hmelo, D.L Holton, and J.L. Kolodner, sixth-grade students designed a set of artificial lungs and built a partially working model of the respiratory system. The learning-by-design students viewed the respiratory system more systemically and understood more about the structures and functions of the system than the control group.

Hmelo and colleagues argued that design challenges need to be carefully planned, and they emphasized the importance of dynamic feedback. They also determined that teachers working on design projects must pay particular attention to finding a balance between students' work on design activities and reflection on what they are learning; that balance allows teachers to guide students' progress, especially in recognizing irrelevant aspects of their research that may take them on unproductive tangents, and in remaining focused on the whole project rather than simply on its completion.

Shifting Ideas, Shifting Roles

A significant challenge to implementing inquiry approaches is the capacity and skill of teachers to undertake this more complex form of teaching. Teachers may think of project learning or problem-based teaching as unstructured and may fail to provide students with proper support and assessment as projects unfold.

When students have no prior experience with inquiry learning, they can have difficulty generating meaningful driving questions and logical arguments and may lack background knowledge to make sense of the inquiry. Students can neglect to use informational resources unless explicitly prompted. They can find it hard to work together, manage their time, and sustain motivation in the face of setbacks or confusion.

One of the principal challenges for teachers, then, is to learn how to juggle a host of new responsibilities -- from carving out the time needed for extended inquiry to developing new classroom-management techniques. They must also be able to illuminate key concepts, balance direct instruction with inquiry teaching, facilitate learning among groups, and develop assessments to guide the learning process. That's a tall order for even the most experienced teacher.

To address these problems, Alice D. Gertzman and Janet L. Kolodner, of the Georgia Institute of Technology, introduced the concept of a design diary in 1996 to support eighth-grade science students in creating a solution for coastal erosion on a specific island off the coast of Georgia. Students had access to stream tables, as well as resources on videotape and the Internet.

In a first study conducted by Gertzman and Kolodner, learning outcomes were disappointing but instructive: The researchers noted that the teacher missed many opportunities to advance learning because she could not listen to all small-group discussions and decided not to have whole-group discussions. They also noted that the students needed more specific prompts for justifying design decisions.

In a second study, the same researchers designed a broader system of tools that greatly improved the learning outcomes. These tools included more structured diary prompts asking for design explanations and the use of whole-class discussions at strategic moments. They also required students to publicly defend their designs earlier in the process. Requiring students to track and defend their thinking focused them on learning and connecting concepts in their design work.

Talented Teams

Inquiry-based learning often involves students working in pairs or groups. Cooperative small-group learning -- that is, students working together in a group small enough that everyone can participate on a collective task -- has been the subject of hundreds of studies. All the research arrives at the same conclusion: There are significant benefits for students who work together on learning activities.

In one comparison by Zhining Qin, David Johnson, and Roger Johnson, of four types of categories for problems presented to individuals and cooperative teams, researchers found that teams outperformed individuals on all types and across all ages. Results varied by how well defined the problems were (a single right answer versus open-ended solutions, such as writing a story) and how much they relied on language. Several experimental studies have shown that groups outperform individuals on learning tasks and that individuals who work in groups do better on later individual assessments.

Cooperative group work benefits students in social and behavioral areas as well, including improvement in student self-concept, social interaction, time on task, and positive feelings toward peers. Researchers say these social and self-concept measures were related to academic outcomes and that low-income students, urban students, and minority students benefited even more from cooperative group work, a finding repeated over several decades.

But effective cooperative learning can be difficult to implement. Researchers identify at least three major challenges: developing group structures to help individuals work together, creating tasks that support useful cooperative work, and introducing discussion strategies that support rich learning.

Productive Collaboration

A great deal of work has been done to specify the kinds of tasks, accountability, and roles that help students collaborate well. In a summary of forty years of research on cooperative learning, Roger and David Johnson, at the University of Minnesota, identified five important elements of cooperation across multiple classroom models:

• Positive interdependence
• Individual accountability
• Structures that promote face-to-face interaction
• Social skills
• Group processing

Cooperative-learning approaches range from simply asking students to help one another complete individually assigned problem sets to having students collectively define projects and generate a product that reflects the work of the entire group. Many approaches fall between these two extremes.

Credit: Thomas Reis

In successful group learning, teachers pay careful attention to the work process and interaction among students. As Johns Hopkins University's Robert Slavin argues, "It is not enough to simply tell students to work together. They must have a reason to take one another's achievement seriously." Slavin developed a model that focuses on external motivators, such as rewards and individual accountability established by the teacher. He found that group tasks with individual accountability produce stronger learning outcomes.

Stanford University's Elizabeth Cohen reviewed research on productive small groups, focusing on internal group interaction around tasks. She and her colleagues developed Complex Instruction , one of the best-known approaches, which uses carefully designed activities requiring diverse talents and interdependence among group members. Teachers pay attention to unequal participation, a frequent result of status differences among peers, and are given strategies to bolster the status of infrequent contributors. Roles are assigned to encourage equal participation, such as recorder, reporter, materials manager, resource manager, communication facilitator, and harmonizer.

Studies identified social processes that explain how group work supports individual learning, such as resolving differing perspectives through argument, explaining one's thinking, observing the strategies of others, and listening to explanations.

Evidence shows that inquiry-based, collaborative approaches benefit students in learning important twenty-first-century skills, such as the ability to work in teams, solve complex problems, and apply knowledge from one lesson to others. The research suggests that inquiry-based lessons and meaningful group work can be challenging to implement. They require changes in curriculum, instruction, and assessment practices -- changes that are often new for teachers and students.

Teachers need time and a community to organize sustained project work. Inquiry-based instruction can help teachers deepen their repertoire for connecting with their peers and students in new and meaningful ways. That's powerful teaching and learning -- for students and teachers alike.

The Takeaway: Research Findings

A growing body of research has shown the following:

• Students learn more deeply when they can apply classroom-gathered knowledge to real-world problems, and when they take part in projects that require sustained engagement and collaboration.
• Active-learning practices have a more significant impact on student performance than any other variable, including student background and prior achievement.
• Students are most successful when they are taught how to learn as well as what to learn.

Adapted from Powerful Learning: What We Know About Teaching for Understanding, a new book reviewing research on innovative classroom practices, by Linda Darling-Hammond, Brigid Barron, P. David Pearson, Alan H. Schoenfeld, Elizabeth K. Stage, Timothy D. Zimmerman, Gina N. Cervetti, and Jennifer L. Tilson, published in 2008 by Jossey-Bass. Published with support from The George Lucas Educational Foundation. Available at amazon.com .

The collaborative classroom: social and emotional learning.

Traditional academic approaches — those that employ narrow tasks to emphasize rote memorization or the application of simple procedures — won’t develop learners who are critical thinkers or effective writers and speakers. Rather, students need to take part in complex, meaningful projects that require sustained engagement and collaboration.

Listen to education expert Linda Darling-Hammond’s insights on cooperative teaching in the Edutopia video The Collaborative Classroom: An Interview with Linda Darling-Hammond . Darling-Hammond, a professor of education at Stanford University and former director of the National Commission on Teaching and America’s Future, was chosen in 2006 by Education Week as one of the nation’s ten most influential people affecting education policy over the last decade.

She and article coauthor Brigid Barron are two of the coauthors of Powerful Learning: What We Know About Teaching for Understanding , a review of research on the most effective K-12 teaching practices. In the book, copublished by Jossey-Bass and The George Lucas Educational Foundation, the authors explore the ways in which project learning, cooperative learning, and performance-based assessment generate meaningful student understanding in the classroom. Available for puchase at amazon.com .   [[{“type”:”media”,”view_mode”:”content_image”,”fid”:”68802″,”link_text”:null,”fields”:{},”attributes”:{“height”:”12″,”width”:”11″,”class”:”media-image media-element file-content-image”}}]] Download an expanded version of this article adapted from the book (PDF 7.6MB ).

• My learning

Research Summary on the Benefits of PBL

This summary of research on Project Based Learning also appears in the BIE book PBL for 21st Century Success. It provides a quick look at key studies showing PBL's positive effects on student academic achievement, mastery of 21st century competences such as problem-solving and critical thinking, addressing the needs of diverse learners and closing achievement gaps, and increasing students' motivation to learn.

To view or download this resource, log in here.

15 Best Machine Learning Projects With Source Code (2024)

• Sairam Uppugundla
• April 20, 2024

Whether you’re a final-year student or a college fresher, you need to develop and demonstrate practical skills if you want to clear your interviews and get a high-paying machine learning job.

And, doing machine learning projects is one of the best ways to achieve that.

At codegnan, we have trained 5,000+ students and helped them accomplish machine learning hands-on projects with one-on-one mentor guidance.

This is why we have created this list of top project ideas with source code that will help you complete unique machine learning projects.

⭐ Enroll in our classroom training or online courses: 

• Machine learning training in Hyderabad (1 month)
• Machine learning training institute in Vijayawada (1 month)
• Online machine learning course using Python (120+ video lessons)

Machine learning projects at a glance

• 1. Instagram Reach Analysis 
• 2. Scraping laptop data from Amazon 
• 3. Video Game Sales Prediction 

4. Heart disease detection

• 5. Food order prediction 
• 6. Contact tracing system 
• 7. Sarcasm detection 
• 8. Medical insurance price prediction 
• 9. Credit card clustering 
• 10. MNIST Data 
• 11. Real time sentiment analysis 
• 12. News recommendation system 
• 13. Calories Burnt Prediction 
• 14. Online Payment Fraud Detection 
• 15. Rainfall Prediction system 

Benefits of doing Machine Learning projects for final year students

Become a machine learning expert in the next 30 days with codegnan, machine learning projects for cse final-year students.

Below, we have shared the list of top machine learning project ideas and instructions to complete them. 

👉 If you want to learn about machine learning, check out our complete machine learning course syllabus .

1. Instagram Reach Analysis 

Instagram reach analysis is a vital topic for social media marketing. This project aims at teaching learners how to use data to analyze their Instagram reach. It involves collecting data on the reach of your past posts and using Python to understand how different factors affect the number of people who see your posts.

Learning outcomes : 

• Understanding of Machine Learning concepts : Have a solid grasp of the fundamentals of machine learning, its workflow, and common data preprocessing techniques 
• Data Exploration :  Use of Python libraries like Pandas and NumPy for data manipulation and analysis
• Data Visualization : Knowledge of Python in-built tools like Matplotlib, Seaborn, and Plotly for creating charts and tables and improved data analysis 

What it takes to execute this project :

• Manually gather ‘reach’ data from Instagram insights about past posts
• Import them from CSV file into Python using libraries like Pandas, and clean and organize the data for analysis 
• Use Python libraries like Matplotlib, Seaborn, and Plotly to calculate different metrics, create data visualization, and identify patterns 
• Depending on your data analysis, draw conclusions on which factors influence your Instagram reach 

Real world applications : 

• Marketers can use this tool to identify what attracts consumers
• Businesses can use them to see how people welcome their products
• Content creators use it to understand which kind of post goes viral and attract followers 

👉 Find the source code

2. Scraping laptop data from Amazon 

This project aims to use Python libraries to scrape and extract data on laptop models, features, and pricing from Amazon. You will learn how to automate the process of collecting product data from Amazon which will help you in price comparison, market research, and data-driven decision making. 

• Basic knowledge of Machine Learning: Get hands-on training on the working of machine learning techniques 
• Web scraping : Learn using Python libraries like BeautifulSoup or requests to parse and extract data from HTML web pages and handle different types of data on websites, including tables and forms
• URL handling : Understand how to construct and manipulate URLs to navigate through different product pages on Amazon
• Data cleaning and transformation : Practice cleaning and organizing scraped data into a structured format such as CSV and JSON files or databases
• Data preprocessing and storage : Knowledge of saving the cleaned data to a local file or a cloud storage 
• Set up the development environment by installing Python and its necessary libraries
• Identify the target Amazon laptop product URL that you want to scrape 
• Develop the scraping code using Python, extract the required data, and store it in a structured format like a CSV file
• Run your code and resolve any bugs within it, like issues with handling dynamic content or working with website changes
• Website scraping helps businesses monitor product prices and other features of their competitors 
• Helps market researchers to collect data on multiple topics for developing a case study or research work
• Assists marketers in understanding customer preferences and upcoming trends for making strategic decisions 

3. Video Game Sales Prediction 

This Video Game Sales Prediction project aims at using a supervised data learning technique, regression. Learners will work with previous data of video game sales and build a model that can predict future trends and sales depending on various game attributes like genre, platform, etc. 

• Basic Machine Learning knowledge: Learn the basic concepts of machine learning and know how it works in the real world 
• Data manipulation : Knowledge of data manipulation and analysis techniques using Python libraries Pandas and NumPy to summarize, filter, and transform video game sales data
• EDA (Exploratory Data Analysis) : Practice analyzing and visualizing data using Python libraries like Matplotlib, Seaborn, and Plotly 
• Data engineering and pre-processing: Learn how to select and transform relevant data features to improve the prediction performance of your regression model
• Web scraping: Implement knowledge of web scraping to collect video game sales data from multiple web pages
• Supervisor learning-Regression : Learn the use of regression algorithms like linear regression, polynomial regression, and Lasso and Ridge regression
• Regression model evaluation : Understanding of how to use different evaluation metrics for the regression model, including Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE) to measure the model performance 
• Model deployment: Learn how to deploy trained models to make sales predictions on new and unseen video game data

What it takes to execute this project:

• Set up the development environment by installing Python and its libraries like Pandas, NumPy, Matplotlib, Seaborn, and Plotly 
• Use web scraping to obtain video game sales data 
• Implementation of data engineering methods to clean the data, handle missing values, use feature engineering techniques for creating new features, handle data and time variables, and encode categorical variables 
• Split the data into two parts, one for training and the other for testing 
• Train the regression model with a suitable algorithm like linear regression, polynomial regression, or Lasso and Ridge regression 
• Assess the performance of the regression model using different evaluation metrics 
• Optimize the model using different feature engineering techniques and model to improve the prediction performance of the model 
• Use the trained model to make predictions of future video game sales
• Video game companies can use the model to predict their sales for new game releases and think about their production, marketing, and pricing 
• Marketers can use it to forecast future sales and make informed decisions about their marketing campaigns 
• Video game publishers can use it to identify which game they should invest in and which one to stop for increased revenue and profitability 

The heart disease detection project aims to build a tool that will help users detect the presence of heart disease. It uses Python and the supervised learning technique of classification to accurately predict the presence of a heart disease based on different medical factors.

• Knowledge of machine learning basics : Understanding how the entire machine learning process works
• Data manipulation and analysis : Learn the use of Python libraries Pandas and NumPy for data manipulation and analysis, data cleaning, handling missing values, and dealing with outliers 
• Data visualization : Use of Python libraries like Matplotlib, Seaborn, and Plotly for data visualization 
• Data preprocessing : Understanding of data clearing and handling missing values, feature engineering techniques, data scaling and normalization, and dealing with categorical variables 
• Supervised learning model selection and evaluation : Use of appropriate supervised learning algorithms like logistic regression, decision trees, and random forests 
• Evaluating model performance: Knowledge of different model performance evaluation metrics
• Download and install Python and its necessary libraries on your system 
• Obtain the required data from websites using web scraping 
• Preprocessing of data by cleaning data, handling missing values, and encoding categorical variables 
• Perform EDA (Exploratory Data Analysis) to understand the relationship between different features and target variables to identify the presence of a heart disease 
• Split the data into two parts, one for training the model and the other for testing the model’s performance 
• Select an appropriate supervised learning algorithm, train it with the data, and optimize its performance 
• Evaluate the performance of the model using the testing data; if it’s not up to the mark, use different feature engineering techniques to improve results 

Real world applications:  

• Doctors can use the tool to detect any hidden heart diseases in a patient during emergencies 
• Patients can use it for personal use to detect any heart disease early and take immediate actions
• Hospitals can integrate the tool with their clinical decision support system to help doctors and nurses quickly assess patient’s risk of heart disease based on their medical data

5. Food order prediction 

This food order prediction project aims to predict customers’ food orders based on their preferences, location, past orders, time of the day, etc. Learners will build a classification model using supervised learning techniques and Python libraries to make predictions. 

Learning outcomes: 

• Understanding of Machine Learning work process: Get to know how machine learning works in the real world 
• Data processing: Learn to clean, transform, and prepare data for training, along with handling missing values and encoding categorical variables 
• Exploratory Data Analysis (EDA) : Understand data visualization, learn the relationships between features, and gain insights on how to select the appropriate machine learning algorithm 
• Model selection : Experiment with multiple supervised learning algorithms, evaluating their performance and optimizing their accuracy 
• Model evaluation: Knowledge of assessing the performance of the classification model using multiple evaluation metrics 
• Collect the required dataset containing customer food order information like types of food ordered, time of order, location, etc 
• Use Python libraries like Pandas and NumPy to prepare your data, identify patterns, and clean inconsistencies
• Split the data into parts, one for training and the other for testing 
• Train your model on the training data, allowing it to learn different patterns that influence customers’ food choices
• Test the model to check for its performance and accuracy 
• Restaurants can use it to forecast customer orders and keep their stock filled, have proper staff, and be ready for the food preparation process
• Food delivery apps can use this to provide personalized food recommendations to customers based on their search or past orders
• Food producers and distributors can use the model to predict food habit changes or demands and plan for better production and distribution strategy 
• Marketers can use it to develop targeted marketing campaigns, offering personalized promotions based on customer preferences 

6. Contact tracing system 

This project aims at tracing whether an individual was in contact with an infected person. The contact tracing system can predict whether an individual has been in close contact with an infected person based on various factors. It will assist public health institutions in managing the spread of infectious disease

Learning outcomes:  

• Understanding of Machine Learning concepts: Knowledge of how machine learning techniques work in the real world 
• Data preprocessing : Learning how to clean, format, and handle missing values in a dataset 
• Exploratory Data Analysis EDA: Analyzing data to understand relationships between features and target variable
• Supervised Machine Learning algorithms : Implementing and comparing the performance of Supervised Machine Learning algorithms like SVM (Support Vector Machine), K-Nearest Neighbors (KNN), and Naive Bayes
• Use of evaluation metrics : Checking the accuracy of the trained machine using different evaluation metrics like MSE (Mean Squared Error), R-squared, and MAE (Mean Absolute Error)
• Set up the development environment by installing Python and its libraries 
• Generate the required data like location, time, duration of encounters, and personal details of individuals 
• Preprocess the data by cleaning and handling missing values, encoding categorical variables, and feature scaling 
• Split data into two parts: training and testing datasets and use of algorithms like SVM, KNN, and Naive Bayes to train the model
• Evaluate the model’s performance with different evaluation metrics 
• Use the system to make predictions on new contact tracing data

Real world applications: 

• Public health authorities can use the system to quickly identify and monitor individuals who might be exposed to a contiguous disease and prevent the spread of the outbreak
• During a pandemic, this system can be integrated with a larger healthcare system to track the movement of an infected individual and their contacts

7. Sarcasm detection 

This circles and detection project aims to help learners understand the use of natural language processing and supervised learning algorithms. This model will accurately identify sarcastic statements in the text. For that, you need to train a machine learning model to classify between sarcastic and non sarcastic statements.

• Knowledge of Python programming: Learn the use of different Python libraries and codes to implement Machine Learning projects 
• Text data preprocessing : Understanding of how to preprocess and clean text data 
• Feature engineering techniques : Use of different feature engineering techniques to create new features from text data, sentiment analysis, and encoding categorical variables
• Supervised learning algorithms : Implement different supervised learning algorithms 
• Use of evaluation metrics: Verify the performance of a machine learning model using different evaluation metrics 
• Collect data of sarcastic and non sarcastic statements using web scraping techniques and Python libraries like BeautifulSoup or requests 
• Clean and preprocess the data and handle missing values for training the model
• Perform feature engineering techniques to extract features from the preprocessed data and use appropriate Machine Learning algorithms to train the model to distinguish between sarcastic and non sarcastic statements 
• Split the dataset into training and testing sets and train the model using supervised learning algorithms like SVM, KNN, and Naive Bayes 
• Evaluate the performance of the model with appropriate metrics and optimize the system for better outcomes 
• Integrate the system into larger applications or systems to detect sarcasm in real-time text inputs
• Companies can implement this system to analyze customer sentiment on their product line and understand customer complaints expressed in a sarcastic way
• Customer care service providers can use this tool to recognize the tone and intent of customer communication and offer them personalized and empathetic responses
• Online platforms and communities can use this sarcasm detection system to identify and filter out harmful or inappropriate content and promote a positive environment 

8. Medical insurance price prediction 

This project aims to build a model that can predict medical insurance prices based on certain factors like age, body mass index, sex, number of children, etc. It will help families to identify their insurance premiums and get themselves covered for emergency health scenarios.

• Basic knowledge of Machine Learning and Python coding : Learn how Machine Learning works and how to implement Python libraries in ML projects
• Data preprocessing : Understanding how to import and clean datasets, handle missing values, and encode categorical variables 
• Exploratory Data Analysis EDA: Knowledge of analyzing datasets using Python libraries to find out relationships between the features and target variable 
• Model selection: Understanding different supervised learning algorithms like linear regression, decision trees, random forests, etc., and choosing the appropriate one
• Model performance evaluation: Use different evaluation metrics to check the model performance and optimize it for better results 
• Ensemble Methods and Boosting: Implementing different ensemble techniques like bagging and boosting to improve the predictive ability of the model
• Collect the medical insurance dataset by web scraping with Python libraries like BeautifulSoup or requests 
• Clean the data, handle missing values, encode categorical variables, and preprocess the data for training your model
• Determine the relationship between features and target variables using visualization techniques like scatter plots, histograms, etc.
• Split the data into training and testing datasets and try using different supervised learning algorithms like linear regression, decision trees, etc.
• Evaluate the performance of the model considering multiple metrics like R-squared, mean squared error, and root mean squared error
• Implement ensemble methods like bagging and boosting, including AdaBoost and Gradient Boosting, to improve the predictive ability of the model 
• Evaluate the overall performance of the model based on the test dataset and ensure it is ready for use 
• Individuals can use the tool for family or single insurance planning and estimate the cost based on multiple factors like age, chronic health conditions, etc 
• Healthcare providers can implement the tool to understand what factors influence insurance costs and optimize their pricing strategies and patient care plans
• Government can leverage this model to predict insurance costs to develop effective healthcare policies and ensure fair and affordable healthcare and medical insurance access to all 

9. Credit card clustering 

This project aims to create a cluster of customers with similar credit card spending patterns, providing valuable insights for credit card companies. It will use Python libraries and unsupervised machine-learning techniques to analyze credit card transaction data and identify customer segments.

• Basic understanding of machine learning and Python: Get handsome experience with Python libraries in machine learning projects and knowledge of basic ML concepts
• Exploratory data analysis using Python library : Learn the use of two popular Python libraries, Pandas and NumPy, and their use in data manipulation and analysis and scientific computing
• Data engineering and preprocessing: Understand how to prepare your data for analysis, handle missing values, scale features, data cleaning, and dealing with categorical variables 
• Unsupervised learning technique : Learn the use of clustering models in unsupervised learning techniques to group customers based on their spending behavior
• Model evaluation: Knowledge of evaluating a model’s performance using different evaluation metrics and techniques like the Elbow method and Silhouette Analysis 
• Data visualization: Use of multiple Python libraries like Matplotlib, Seaborn, and Plotly for data visualization and interpretation
• Gather a dataset of credit card transactions with a web scraping tool 
• Clean the data, handle missing values, and scale features to prepare the data for analysis 
• Analyze the data, identify features, and visualize data for a better understanding of hidden patterns 
• Apply clustering algorithms like K-means Clustering and DBSCAN to group customers based on their spending behavior
• Evaluate the performance of the clustering model and optimize it for better results
• Credit card companies can use the tool to identify distinct customer segments having similar spending patterns that help them provide personalized offers, targeted marketing campaigns, and customer retention strategies
• Fraud detection agencies can use it to detect anomalous spending behavior, which can be an indicator of fraudulent transactions
• Banks and financial institutions can use it to understand the spending habits of their customer segment and offer tailored rewards programs, financial planning advice, and  credit card adjustments

10. MNIST Data 

The aim of this project is to work with the MNIST dataset that contains handwritten digit images to understand its underlying data structure.

• Basic understanding of machine learning techniques and Python : Learn how machine Learning works and use different Python libraries in deploying machine learning projects
• Data preprocessing: Knowledge of how to load and preprocess the MNIST dataset along with normalizing the pixel values and reshaping the data 
• Dimensionality reduction unsupervised learning algorithm: Understanding of applying dimensionality reduction algorithms like PCA and t-SNE on the MNIST data 
• Data visualization : Learning how to create visualizations like scatter plots and t-SNE embeddings to understand the structure of the MNIST data
• Model evaluation : How to evaluate the performance of your model using multiple evaluation metrics 
• Install required Python libraries like NumPy, Scikit-learn, Pandas, Matplotlib, Seaborn, and Plotly for the project 
• Collect MNIST data by web scraping and then preprocess the data by normalizing the pixel values and reshaping the images
• Implement dimensionality reduction techniques like PCA using the Scikit-learn library 
• Build a simple classifier using a logistic regression model or a neural network  and evaluate its performance 
• MNIST data can be used in developing handwritten character recognition systems required in document processing and bank check processing 
• It can be implemented in the signature verification process to ensure security and authentication 
• MNIST dataset serves as the starting point for developing image analysis and classification techniques 

11. Real time sentiment analysis 

A sentiment analysis system will determine the emotional tone behind a text, whether it is positive, negative, or neutral, in real time. 

• Basic understanding of machine learning and Python libraries: Knowledge of implementing multiple Python libraries in completing the project and having a good understanding of machine learning concepts
• Data manipulation: Use of Python library Pandas to load, clean, and preprocess the text data for training machines 
• Natural language processing : Applying different National language processing techniques like tokenization, stemming, and lemmatization
• Sentiment analysis: Use of different sentiment analysis techniques, building a model with supervised learning, and evaluating its performance 
• Install required Python libraries for the project like Scikit-learn, Pandas, Matplotlib, NumPy 
• Data collection with known sentiment labels by web scraping techniques
• Clean and preprocess the text data using Python libraries like Pandas, remove stopwords and stemming or lemmatizing words, and convert text to numerical format for machine learning 
• Split the pre-process data into training and testing datasets and train your machine model with appropriate algorithms like SVM, KNN, and Naive Bayes to classify the sentiment of the text 
• Evaluate the performance of the trained model with appropriate model evaluation metrics like accuracy, precision, recall, and F1-score
• Integrate the trained model into the real-time application to accept user input and output the predicted sentiment using Python web frameworks like Flask and Django 
• Customer service centers can use it to analyze customer feedback and support conversations, quickly identify and address any negative sentiments, and improve customer satisfaction 
• Social media marketers can use it to track the sentiment of online conversations about a product, a brand, or an event for improved marketing and public relations strategies
• Financial analysts can use it to monitor news and social media platforms for determining market sentiment and making informed investment decisions

12. News recommendation system 

This project aims at defining user preferences and analyzing multiple news articles to provide personalized news recommendations to individual users. 

• Web scraping : Learn how to extract news articles from different websites using Python library like BeautifulSoup 
• Data preprocessing : Knowledge of cleaning and transforming text data for training machine learning models 
• Recommendation algorithms : Understanding the concept of recommendation systems and their types mainly collaborative filtering and content-based filtering
• Machine learning model training and evaluation: Knowledge of how to train recommendation models and assess their performance using multiple evaluation metrics 
• User interface development: Hands-on training on building a web-based interface to showcase your news recommendation system
• Set up the development environment by installing Python along with its necessary libraries and any web framework like Flask or Django 
• Use web scraping techniques to collect news data from various websites and online sources 
• Preprocess the data, clean the news text, extract relevant features, and encode the data for training recommendation models
• Implement a recommendation system including content-based filtering, collaborative filtering, or a hybrid model to generate personalized news  recommendations
• Split the data into training and testing sets, train the recommendation model, and evaluate its performance using different metrics like precision, recall, and F1-score 
• Create a web-based application that allows users to interact with the news recommendation system, view recommended news articles, and provide feedback
• News organizations can use this system to offer personalized news recommendations to their readers and improve user engagement 
• Social media platforms can use it to leverage news recommendations to users, keeping them informed and engaged with any brand or content creators 
• Companies can use this news recommendation system to keep their employees updated with industry news and trends and improve their knowledge and decision-making process

13. Calories Burnt Prediction 

The Calories Burnt Prediction project aims to develop a model that can predict the number of calories a person can burn depending on various factors. 

• Python programming : Learn Python programming basic concepts, including working with data structures, file handling, and data  manipulation, and the use of Python libraries 
• Data preprocessing : Understand different techniques for cleaning, handling missing values, and transforming data into formats appropriate to train machines
• Feature engineering : Knowledge of how to create new features from existing data and improve your training model performance 
• Machine learning algorithms : Knowledge of applying different algorithms like linear regression, decision trees, and random forests to solve a real-world problem 
• Model evaluation : Evaluate the performance of the train model using multiple metrics like Mean Squared Error, R-squared, etc.
• Installing Python and the required library for the project 
• Gather data from online sources with web scraping techniques 
• Clean the dataset by handling missing values, converting data types, and normalizing the features
• Analyze the data and create new features from it that can improve the model’s performance, such as body mass index or activity intensity 
• Split the data into training and testing datasets and use it to train your chosen model
• Evaluate the performance of your model using appropriate metrics 
• Optimize the model’s hyperparameters to improve its performance 
• Integrate the model with a simple web application to allow users to provide input and receive predictions on the number of calories burned
• Fitness companies can use this model with their fitness tracking apps or wearable devices to provide users with accurate calorie burn estimates during workout or daily activities
• Individuals can use the system to better understand their calorie loss and have effective weight management plans 
• Athletes and coaches can use the system to optimize their training process and monitor the calories burnt during different types of exercises

14. Online Payment Fraud Detection 

This online payment fraud detection project aims to help students learn how to build a system that can identify fraudulent online transactions.  The model will work with a dataset of previous online transactions and train machine learning models to recognize patterns that distinguish fraud activities from normal transactions.

• Basic knowledge of Python libraries : Use of different Python libraries like Pandas, NumPy, matplotlib, etc., for performing multiple tasks within machine learning projects 
• Web scraping : Learn how to extract data from different web pages using scraping techniques 
• Data handling techniques : Knowledge of how to preprocess the data before feeding them to a machine learning model using Python libraries like Pandas and NumPy 
• Feature engineering: Understanding of data cleaning and handling missing values, creating new features from existing data, and encoding categorical variables
• Model selection and evaluation : Learn how to split the data into training and testing sets, use appropriate ML algorithms, and evaluate the model’s performance using different metrics
• Install Python and its required libraries for developing the machine learning project 
• Gather data from different online transactions, learn its features, and preprocess the data for feeding machine learning models 
• Analyze the data and create new features that can improve your model’s ability to detect fraud transactions 
• Choose an appropriate machine learning algorithm like logistic regression or decision trees and train the model on the prepared data 
• Split the data into training and testing sets, evaluate the model’s performance using relevant evaluation metrics, and fine-tune the model 
• Integrate the trained model into a real-world application like a mobile payment app or e-commerce platform to detect and prevent online payment fraud
• Online retailers can integrate this model into their payment processing systems to identify potentially fraudulent transactions
• Integrate this tool with different Mobile payment apps to detect fraud in online transactions 
• Banks and credit card companies can use this system to monitor their customer transactions and identify suspicious activities in real-time

15. Rainfall Prediction system 

This project aims to develop a model capable of predicting rainfall patterns in the future based on past meteorological data. Learners will get hands-on practice collecting and preprocessing meteorological data, using different machine learning algorithms, and training a model to make accurate rainfall predictions.

• Data collection : Learn how to collect existing rainfall data along with other essential features like temperature, humidity, and atmospheric pressure from reliable web sources
• Data preprocessing: Knowledge of how to clean and preprocess the data, handle missing values, outliers, or inconsistencies, and ensure the data is suitable for training models
• Exploratory Data Analysis : Familiarity with data visualization techniques using Python libraries like Matplotlib and Seaborn to gain valuable insights from the data
• Feature engineering : Understanding of feature selection and creation to improve the predictive ability of the model
• Use of Machine Learning algorithms : Knowledge of different machine learning algorithms and evaluate their performance 
• Setup the development environment with Python and its necessary libraries
• Collect rainfall data and other relevant meteorological features from reliable sources with web scraping techniques 
• Preprocess the data before feeding it to the machine-learning model
• Train the machine learning model with appropriate algorithms for predicting rainfall 
• Assess the model’s performance using appropriate evaluation metrics and finetune the hyperparameters to improve prediction accuracy
• Integrate the model into a larger system or create a web application
• Farmers can use this tool to predict rainfall and plan their crop management activities 
• Local authorities in water management agencies can use it to forecast water availability and implement different water conservation strategies 
• Disaster management authorities can use it to understand rainfall rates and provide early warnings to prepare people for a disaster 

There are multiple benefits of doing machine learning projects, like 

• Having a clear understanding of machine learning concepts
• Developing real-world projects 
• Improving professional resume by adding these projects 
• Expand your horizon of thinking and improve your creativity skills
• Building trust and credibility among employers 
• Landing good jobs 

At Codegnan , we provide a 30-day machine learning course that covers all the basic concepts and hands-on training on multiple projects. You can learn from top mentors in the industry who are students from the best universities or are working professionals in the domain.

• Course duration : 30 days 
• Course fees : ₹7,999
• Class availability : online and offline classes 
• End-to-end training with real-time projects 
• Classroom training available in Hyderabad and Vijayawada
• Trained by mentors above 5 years of industry expertise
• Training delivery in English 
• Join the 100-day Job Accelerator Program and land top jobs
• Enrol for the internship program and join for a short-term internship 

Sairam Uppugundla is the CEO and founder of Codegnan IT Solutions. With a strong background in Computer Science and over 10 years of experience, he is committed to bridging the gap between academia and industry.

Sairam Uppugundla’s expertise spans Python, Software Development, Data Analysis, AWS, Big Data, Machine Learning, Natural Language Processing (NLP) and more.

He previously worked as a Board Of Studies Member at PB Siddhartha College of Arts and Science. With expertise in data science, he was involved in designing the Curriculum for the BSc data Science Branch. Also, he worked as a Data Science consultant for Andhra Pradesh State Skill Development Corporation (APSSDC).

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Main article content, adopting place-based learning as a pedagogical strategy in textile technology teacher education.

Changes in education globally have ushered in several modifications in the context in which students learn. To align with these changes, teacher education must also be modified. Our line of reasoning in this study is that the use of place-based learning in teaching textile technology could transform student teachers’ learning experiences and contribute to the development of 21st-century attributes. The community’s involvement in place-based learning could also contribute to refining students’ skills, consequently fostering their entrepreneurship in the textile field. A self-study genre of action research was adopted. Data was gathered through document analysis, reflective journals and focus group interviews with conveniently sampled Zimbabwean textile technology teacher education students. Thematic analysis revealed that place-based learning is an effective pedagogy for developing skills that students could utilise to create income-generating projects. We conclude that place-based learning as a teaching-learning approach contributed to students learning how to design and manage the project as well as develop transversal skills such as critical thinking, problem-solving, communication and teamwork. We suggest that place-based learning be utilised in other comparable Home Economics-related subjects to enable the fostering of similar 21st-century skills and competencies, with the bonus of benefits for the communities in which it is implemented.

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