effect of problem solving teaching method on students' achievement

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• Published: 05 September 2024

The effectiveness of training teachers in problem-based learning implementation on students’ outcomes: a mixed-method study

• Nawaf Awadh K. Alreshidi   ORCID: orcid.org/0000-0002-7934-4724 1 &
• Victor Lally 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1137 ( 2024 ) Cite this article

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The aim of this study was to understand the effect of training teachers in problem-based learning (PBL) implementation on students’ outcomes. Mixed methods were used to analyse the quasi-experimental study data. 127 students were divided into three groups: Group A ( N  = 52) was taught by a trained teacher using the PBL teaching strategy, group B ( N  = 39) was taught by an untrained teacher using traditional teaching methods, and group C ( N  = 36) was taught by an untrained teacher using the PBL teaching strategy. The results showed that students whose teachers received training in PBL implementation significantly improved in terms of applying knowledge compared with students whose teachers used traditional teaching methods. The findings also provide robust evidence to show that using PBL teaching methods significantly improves students’ attitudes towards mathematics compared with traditional teaching methods, regardless of the teacher training effect. The key element in training teachers in PBL to improve students’ application of mathematics is training teachers in using metacognitive strategies that facilitate students’ learning processes.

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Introduction.

Problem-based learning (PBL) is a teaching strategy in which a facilitator assists students to solve real-world problems as they work in small groups; the facilitator’s aim is to help the students to gain new knowledge and improve their problem-solving skills (see Barrows, 1986 ; Goodman, 2010 ). PBL aims to improve students’ knowledge application (Hmelo, 1998 ; Hmelo and Lin, 2000 ; Schmidt et al., 1996 ), and attitudes towards learning the subject (Hung, 2006 ; Westwood, 2011 ).

In mathematics, PBL is an instructional strategy that contextualises mathematics knowledge (i.e., real-life problems) in a way that helps students to understand where, when and how to apply knowledge. In PBL, when students encounter a real-life problem, they should identify what they have already learned about the problem (i.e., activating their prior knowledge) and establish what they need to know in order to solve the problem (i.e., missing information). They have to search for missing information and then combine it with what they already know (i.e., relevant prior knowledge), applying this to a new context (Bokonjic et al., 2007 ). Therefore, using a PBL teaching strategy in mathematics should reflect on students’ improvement in applying mathematics. Applying mathematics is the concept of using mathematics in real life (Mumcu, 2016 ).

Contextualising knowledge can be prepared by embedding learning opportunities in real-life contexts, which could it also be of interest for students, and it shows students the value of the function of the subject matter in the real world (Hung, 2006 ; Westwood, 2011 ). In the mathematics context, the content of PBL settings (real-life problems) shows the function of mathematics in reality and gives meaning to learning mathematics (Westwood, 2011 ). This should place value on learning mathematics for students, leading to an increase in positive attitudes towards learning mathematics. Attitudes towards mathematics is a negative or positive emotional disposition toward mathematics (Zan and Di Martino, 2007 ). In a systematic review and meta-analysis, Suparman et al. ( 2021 ) determined that PBL is one of the best teaching strategies for primary school mathematics teachers to enhance students’ mathematical abilities. However, students’ learning processes need to be facilitated by teachers in their approach to solving problems (Collins et al., 1989 ; Hmelo-Silver and Barrows, 2006 ; Hung, 2011 ). Thus, it is essential for teachers to be able to do this effectively to produce a noticeable improvement in students’ outcomes. This might require teachers to complete training in facilitation processes. To date, little is known about how the training of teachers in PBL implementation affects students’ outcomes. The results of the present study will help educational decision-makers to understand how training teachers in implementing PBL affects students’ mathematical applications and attitudes towards mathematics.

This article begins with a review of previous studies on PBL, followed by a discussion of teacher training in PBL implementation. The experiment conducted as part of this research examined the effects of training teachers on students’ knowledge application in mathematics and students’ attitudes towards mathematics.

Previous studies in problem-based learning

The overall review of empirical studies shows that PBL tends to significantly improve knowledge application (Abdalqader and Khalid, 2014 ; Primadoni et al., 2020 ; Tong et al., 2021 ; Wirkala and Kuhn, 2011 ; Wong and Day, 2009 ) and generate positive attitudes among students compared with traditional teaching methods (TTM; i.e., teacher-centred instruction) in kindergarten to 12th grade (K–12) settings (Goodnough and Cashion, 2006 ; Lou et al., 2011 ; Merritt et al., 2017 ; Nowak, 2001 ; Tong et al. 2021 ). For example, a quasi-experimental study including control groups conducted by Tong et al. ( 2021 ) examined the effectiveness of PBL on 10th-grade students’ mathematical application knowledge and their attitudes towards mathematics. The results showed that the students taught by the PBL group improved significantly in the application of knowledge and attitudes towards mathematics compared to the students taught by conventional methods. The real-life problems used with PBL are expected to drive students’ curiosity and capture their interest (Schmidt et al. 2009 ); therefore, PBL pedagogy and content could enhance students’ interest and promote their knowledge application.

Most of the literature pertaining to PBL has been conducted in the field of medicine and its allied contexts at universities. A limited number of studies have been carried out in K–12 contexts, and very few studies have been conducted in primary schools see (Alshhrany and Mohammed ( 2010 ); Eviyanti et al., 2017 ). Additional empirical research is needed to investigate the effects of PBL on the outcomes of younger students.

Training in PBL implementation

Although training teachers to implement PBL is generally viewed as critical for improving students’ achievement (Arani et al., 2023 ; Barrows, 1996 ; Fernandes, 2021 ; Hmelo-Silver and Barrows, 2006 ; Leary et al., 2009 ; Wosinski et al., 2018 ) the effects of teacher training on students’ performance are still ambiguous. The agreement on the importance of training is supported by literature outside of PBL, where reports have shown that the most effective teachers are trained in how to use facilitation skills (Leary et al., 2009 ). A meta-analysis was conducted to investigate the relationship between teacher training and students’ learning outcomes, and 94 studies were selected for inclusion in the study. The results showed a significant relationship between teacher training and students’ achievement. The study suggested that untrained teachers have similar student outcomes to those of teachers who use TTM (Leary et al., 2013 ). The researchers concluded that the facilitator may be a key factor in students’ outcomes. In another study, Tawfik and Kolodner ( 2016 ) revisited PBL’s foundations from a case-based reasoning perspective suggested that novices must be trained to facilitate scaffolding students during PBL. Maxwell et al., ( 2005 ) suggested that PBL instruction can improve learning compared with conventional methods when teachers are trained well in PBL. El-Aziz El Naggar et al., ( 2013 ) found that training was necessary to improve facilitators’ skills in collaborative learning and self-directed environments. However, there is a lack of research studies that have experimentally examined the effects of teacher training on student learning. More primary research is required to measure the effects on students’ outcomes of training teachers in PBL.

The aim of training teachers in PBL is to develop teachers in their professional role (Friedman and Woodhead, 2008 ; Villegas-Reimers, 2003 ). Both teachers and students have a role in PBL. To delineate the role of teachers, first, we have to identify the role of students. In PBL, the role of students is to go through the PBL process. Students work in small groups to understand the problem, identify and learn what they need to know and generate hypotheses to solve the problem (Hmelo-Silver, 2004 ). The role of students also involves questioning, researching and using critical thinking in an active way to solve problems (Cerezo, 2004 ). Students are required to take responsibility for their learning and engage in meaning-making in terms of their knowledge (English and Kitsantas, 2013 ). For effective engagement in PBL, students must be responsible for their learning, and they must actively participate in constructing knowledge and making meaningful processes (English and Kitsantas, 2013 ). However, many students cannot easily shift into this role because they have developed ingrained habits from the typical traditional classroom experiences, and they rely on the passive receiving of knowledge (English and Kitsantas, 2013 ; Hung, 2011 ; Ronis, 2008 ). To shift effectively to the new role, students must develop self-regulated learning (SRL) skills (English and Kitsantas, 2013 ).

SRL refers to the extent to which the learner is motivationally, metacognitively and behaviourally active in their learning processes (Zimmerman, 1989 ). Self-regulated learners can set goals and plans, identify appropriate strategies, and self-monitor and self-evaluate their learning; they are intrinsically motivated to learn. Thus, for effective learning in PBL, SRL is an essential skill (English and Kitsantas, 2013 ). In PBL, teachers can consciously activate students’ behaviours, leading to SRL. When it comes to promoting students’ skills to be able to do this, the role of teachers is to structure activities to stimulate students’ motivation, encourage reflection and facilitate their learning processes through guidance, scaffolding feedback and prompting independent thinking (English and Kitsantas, 2013 ). The role of the teacher in PBL is to facilitate collaborative knowledge construction by students, monitor learning processes, model desired behaviours and concentrate students’ efforts on critical thinking (Hmelo-Silver and Barrows, 2006 , 2008 ); this can be done by raising awareness of students’ higher cognitive thinking (Barrows, 1998 ).

Effective teachers should know how to facilitate groups’ learning processes (Dolmans et al., 2002 ; El-Aziz El Naggar et al., 2013 ). To enhance cooperation and production within groups, teachers should use intervention strategies, such as making decisions on what, when and how to intervene (Bosse et al., 2010 ). Teachers may need to be trained to implement such strategies in such a way as to facilitate tutorial processes, since it is teachers’ responsibility to guide students’ learning (Yew et al., 2011 ). In this study, we attempt to understand the effect of training in implementing PBL on students’ outcomes. We address the following questions:

How do trained and untrained teachers in PBL techniques implement PBL?

What are the effects of teacher training in implementing PBL on students’ mathematical applications?

What are the effects of teacher training in implementing PBL on students’ attitudes towards mathematics?

Study design

A quasi-experimental design was adopted in this study as the main quantitative approach to minimise bias in estimating the difference between traditional instruction and PBL classes. In addition, a qualitative approach was used during the intervention using field observation notes and after the intervention using interviews, as a secondary approach (see Fig. 1 ).

The figure illustrates the study design; mathematical test and attitudes towards mathematics were applied before and after the intervention, while during the quasi-experimental implementation, field observation notes were taken, and at the end of the intervention semi-structured interviews were conducted with the teachers.

Figure 1 illustrates the study design; during the quasi-experimental implementation, field observation notes documenting the authors’ observations were taken with the aim of observing how teachers implemented PBL, while semi-structured interviews were conducted with both types of the teachers who only implemented PBL (trained and untrained teachers) after the implementation of PBL as a supplement, with the aim of being used as part of the triangulation method for the author’s observations in how teachers implemented PBL.

School and participating students

The school was located in an urban district in a major city, Hail, which is situated in the north of Saudi Arabia. The school was randomly selected from ten private schools. Then, seven of the third-grade classes out of nine in the selected school were randomly chosen. The third grade is an important level, as it is the final grade of lower primary school. The classes were instructed by three teachers; one taught three classes, and the others taught two classes each. These classes comprised the three following groups: group A (three classes taught by a trained teacher using a PBL teaching strategy), group B (two classes taught by an untrained teacher using TTM) and group C (two classes taught by an untrained teacher using a PBL teaching strategy; see the study design in Table 1 ).

Ethical approval was obtained, and all participants signed consent forms to participate. They were informed that they could withdraw any time with no need to justify their decision, nor would there be any consequences of withdrawal.

In total, 127 pupils participated in the study, and their ages ranged from eight to nine years old. They were in the last semester of the third grade. Most of the students at the school were Saudis; in each group, two to four students had Arab backgrounds, such as from Syria, Egypt and Sudan. All students had a middle-class socioeconomic status. Academic school records and pre-test’ scores were used to ensure that the groups were similar in terms of mathematical achievement. Within each group, students showed a wide range of academic achievements; the students spanned from very low to very high achievers. There were no special education pupils within the groups.

Three teachers were randomly selected from one large primary school to take part in this study. The first teacher was randomly selected to receive training courses in using the PBL teaching strategy. The second teacher did not receive any training, but he was provided with PBL materials—specifically, design problems and guidelines for implementing PBL; he was asked to conduct self-directed learning (SDL) to implement PBL in his classrooms. The aim of including a trained and an untrained teacher using PBL was to measure the effects of training teachers on students’ outcomes. The third teacher was not trained in PBL and was asked to teach students using TTM.

The teachers had similar characteristics in terms of qualifications, experience and expertise, as well as in their beliefs and perspectives on PBL and TTM. They are all male and they believed that the aim of teaching mathematics is to conduct real-life problem solving, and they considered active learning to be important for students. They had been teaching mathematics to third-grade school students for 10 years. They all had a first degree in mathematics. They were all Egyptians and aged in their late thirties. According to the teachers and the administration of the school, the teachers had all attended the same training courses in different aspects of education, such as active learning. However, none of them had ever been trained in using PBL teaching strategies.

The topic covered in the classes was ‘data display’. It covered representation through codes, interpretation of representation through codes, representation in columns and interpretation of representation in columns. The content was new to the students. The instruction took place during ten class sessions (45 min each) comprising four sessions per week over for two and a half weeks, with a total of 7.5 h for each group. To control for the time factor, all groups, whether PBL or traditional, were given the same amount of time.

Instruments

Six multiple-choice questions, short answer questions, fill-in table questions and drawing tests were applied at the beginning of the study (pre-test) and in the final experiment (post-test). Mathematics items were selected from Trends in International Mathematics and Science Study (TIMSS) 2003 , 2007 and 2011 (see Mullis et al., 2012 ). The TIMSS items that were selected matched the objectives of lessons for knowledge application exactly; they had already been examined for the purpose of the test. We chose TIMSS mathematics items because they were verified as appropriate for the students’ ages. The students had nearly finished the third grade, and the curriculum for that grade contained many TIMSS topics (see TIMSS, n.d. ). Each item on the test received a score of either one or zero. An example of the items is given in Appendix A . The measure ‘attitudes towards mathematics’ of TIMSS 2007 (Mullis et al., 2008 ) contains four items, as follows:

I would like to take more mathematics in school

I enjoy learning mathematics.

Mathematics is boring (reverse-coded).

I like mathematics.

This measure was adopted and assumed to meet the standard of a valid and reliable test (see, Mullis et al., 2008 ). Attitudes were assessed using four items applied twice as pre- and post-measures; four items with 4-point Likert scales (disagree a lot, disagree a little, agree a little, and agree a lot) were presented. Each item score ranged from 1 to 4. The total marks ranged from the number of items of the measure to multiply them by 4; the measure consisted of four items, so the total scores ranged from 4 to 16. Some items were reverse-coded; for example, for ‘mathematics is boring’, ‘disagree a lot’ would receive a score of 4, whereas ‘agree a lot’ would receive a score of 1.

The face validity method was used to assess the validity of the tests and attitude measures. Eight arbitrators checked and gave their opinions on the adequacy, clarity, and relevance of the items’ content. The opinions of the arbitrators were considered and included in the preparation of the final image of the tests and attitudes. However, no changes were reported, and face validity confirmed the tests’ validity. In addition, test-retest reliability was used to assess the reliability of the tests and attitude measures. The levels of reliability were acceptable, with a score of 0.86 for the mathematics test and 0.88 for the attitude measure. For further reliability, Cronbach’s alpha was used for each scale of the test and attitudes and for the whole test and attitudes. The results show that all items correlated with a good degree of total scales (no items scored less than 0.3), and the reliability for the test was 0.747, whereas that for attitude was 0.808. Therefore, the measures became high valid for the purposes of this study.

In qualitative methods, filed observation and semi-structured interview were used to assess teachers’ performance in PBL implementation. After filed observations completed, post- semi-structured interviews were conducted for the teachers to confirm the results of author observations of how teachers implemented PBL as a supplement for the methodological triangulation of the filed observations. Methodological triangulation involves a researcher using more than one method, such as interviews and observations, for collecting data to understand a phenomenon deeply (Flick et al., 2004 ; Neuman, 2000 ). The teachers’ responses to the questions in the semi-structured interviews were analysed and compared with the analysed observation data to enhance the validity of the study and to gain a deeper understanding of social events. As Neuman ( 2000 ) commented, “Looking at something from several different points gives a more accurate view of it” (p. 521).

The data obtained from qualitative methods were deductively analysed. Prior to conducting data collection from filed work. A structured categorisation matrix was developed by the authors based on a literature review (see Barrows, 1998 ; English and Kitsantas, 2013 ; Hmelo-Silver and Barrows, 2006 , 2008 ). It aimed to assess PBL implementation conducted by teachers and consisted of two main categories: understanding the problem and using metacognitive strategies (see Appendix B ). Field observation notes were used to describe how the teachers implemented PBL. In this study, field observation notes consisted of two parts: descriptive and reflective information (Patton, 1990 ). The descriptive part involved documenting the factual data obtained from inside the classroom. The main author moved between groups to make sure everything was proceeding well; the intention was to monitor the implementation of the study, and the authors had a diary that was used to document any observations, particularly the observations that took place during lessons and were made inside mathematics classrooms. The main focus was on teachers’ performance, particularly with respect to teacher intervention, individual and collective student practices, student responses, group interaction and PBL processes. In the reflective section, the authors reflected on the meaning of the observations outside of the classroom (see Appendix C ). At the end of the experiment, ten lessons by each teacher were observed.

Semi-structured interview questions were developed according to analysed data of class observations which includes: The three main questions:

How was PBL implemented in your teaching strategies?

How did you assess your students in relation to understanding the problem?

How did you support your students to solve the problem?

In semi-structured interview, tape recordings were used for the interviews with each teacher, which ranged from 13 to 23 min in length. The interviews were conducted in Arabic, transcribed and subsequently translated into English by the authors.

The data were deductively coded (i.e., both the interview and observation) by the main author, and according to the identified categories mentioned above. When a deductive content analysis is used, a categorisation matrix is developed; following this, the data are coded according to the categories (Polit and Beck, 2004 ). In addition, if a structured matrix is chosen, only aspects that fit the matrix are selected from the data (Patton, 1990 ).

Professional development

The PBL programme used in this study aimed to train teachers by focusing on how to implement PBL in mathematics classrooms. The programme continued to provide feedback during the implementation after each session, taking advantage of the literature recommendations. Therefore, the trained teacher learned how to facilitate groups’ learning processes and guide students’ learning by adopting strategies such as posing meta-cognitive questions and focusing on the process of learning to model students’ learning strategies. The teacher was trained in intervention strategies, such as making decisions based on what, when and how intervention should occur to enhance cooperation. The programme included examples of PBL implementations. Teacher training lasted for one week (8–10 h), and daily meetings took place during the course of the training to provide an opportunity to present feedback and resolve unexpected problems. The programme for training the teacher to implement PBL in his class was developed by the author. It was expected that, following the teacher’s completion of the programme, the teacher would be able to do the following:

provide scaffolding and feedback as needed

prompt independent thinking

facilitate collaborative knowledge construction for students

monitor learning processes

model desired behaviours

concentrate students’ efforts on critical thinking.

use intervention strategies, such as making decisions on what, when and how to intervene

The programme included three real-life sessions, each lasting 45 min. The teacher was asked to implement the PBL strategy using an ill-structured problem, which was taken from a mathematics textbook and related to the topics that the students had been studying. A group of students from outside the study sample was selected to assess the teacher’s performance and establish whether he was able to implement PBL effectively. This was followed by providing the teacher with extensive feedback, which lasted more than an hour for each session.

The students were trained in two sessions in how to deal with the PBL teaching strategy.

Problem-based learning implementation

Problems were presented to the students. Students worked in small groups of four to six members. They discussed their understanding of the problems, and then the teacher discussed the understanding of the problem with the whole class. This was followed by students solving the problems. Finally, the teacher discussed the solution with all the students.

In this study, the six core characteristics of PBL mentioned by Barrows ( 1996 ) were adopted. These are as follows:

The student is the centre of the learning.

Learning occurs in small groups of students.

At the beginning of the learning, the students are presented with authentic problems.

The problems are used as a means of developing problem-solving skills.

New knowledge is gained through SDL. (Barrows, 1996 )

From the literature review (see Barrows, 1986 ; Gallagher and Stepien, 1996 ; Hung et al., 2008 ), six characteristics were adopted in the problems after reviewing the literature related to the problem of PBL. These were as follows:

the role of students as stakeholders

ill-structured problems

real-life problems

age-appropriate problems

clear and short problems

not too difficult problems

Statistical analysis (quantitative analysis)

The study used mixed-factor analysis of variance (ANOVA) models (Field, 2013 ; Howell, 2012 ) within one factor (time: pre- and post-tests and between). Tukey’s post hoc test (Field, 2013 ; Howell, 2012 ) was applied when appropriate and where significant results were observed—that is, an effect size (partial eta squared [η p 2 ]). The effect size, classified as Cohen suggested, could be small 0.01; medium, 0.06; or large, 0.14. All analyses were performed on IBM SPSS v22 and at a 5% (0.05) level of significance.

A quasi-experimental design was adopted in this study as the main quantitative approach, while a qualitative approach was used during the intervention using class observation notes and interviews, as a secondary approach. In total, 127 pupils participated in the study. They were in the last semester of the third grade. Ethical approval was obtained, and all participants signed consent forms to participate. Three teachers were randomly selected from one large primary school to take part in this study. The first teacher was randomly selected to receive training courses in using the PBL teaching strategy. The second teacher was not trained and asked to conduct SDL to implement PBL in his classrooms. The third teacher was not trained in PBL and was asked to teach students using TTM. The topic covered in the classes was ‘data display’. The content was new to the students. The instruction took place during 10 class sessions. Instruments of the study include mathematics test and attitudes towards mathematics were prepared and verified. Applying a pre-test (a measure of attitudes towards mathematics and an exam to measure mathematics application). Conducting the study took about 2 and a half weeks. Applying for a post-test (a measure of attitudes towards mathematics and an exam to measure mathematics application). During the intervention, class observations were carried out for each lesson.

Problem-based learning implementation of trained and untrained teachers

Unlike the untrained teacher, the trained teacher properly implemented PBL. The differences between their performances lay in differences in ‘giving students sufficient time to understand the problem’ and ‘using more metacognitive strategies to coach students in relation to their thinking skills’.

Table 2 and Fig. 2 summarise the difference between trained and untrained teachers after analysing both the teachers’ interviews and the author’s observations. The two following themes were extracted from the data analyses: ‘understanding the problem’ and ‘using meta-cognitive teaching skills’. These themes are detailed below.

This figure illustrates the difference between trained and untrained teachers' performances in PBL implementation.

Understanding the problem

The trained teacher did not allow students to solve the problem until they demonstrated their understanding of it. The author frequently noted that the trained teacher prevented the students from solving the problem until they demonstrated their understanding of it. When the trained teacher was asked how he knew that the students understood the problem, he replied, ‘I frequently asked random students… : ‘could you please explain to us the problem in your own words?’ If they did not do very well, I asked them how they could understand the problem more deeply? I waited longer … for them to solve the problem and gave them more time to reflect on their understanding and discuss with their group to deeply understand the problem’. The author observed that the teacher frequently and asked ransom students the following question: ‘Could [you] explain the problem [to us in] your own words’. Some students could, while others could not. Then, he encouraged them to understand the problem by asking them the following questions: ‘How can you understand the problem deeply? and Could you identify the obstacles and discuss [them] with your [respective] groups?’ Later, he again asked them whether they could explain the problem. However, the untrained teacher’s students had been given a shorter amount of time to understand the problem than those who were with the trained teacher (author’s observation).

In all lessons, the untrained teacher asked students whether they understood the problem; he often proceeded after hearing anyone shout ‘yes’ (author’s observation). The untrained teacher confirmed this when he was asked how he knew that his students had understood the problem before carrying on: ‘I always ask my students, if they do not understand the problem, to stop me any time and feel free to ask’. He did not ask his students to explain the problem in their own words (author’s observation). It was noted that the trained teacher gave more time for understanding the problem and questioned his students’ understanding more than the untrained teacher did.

Using meta-cognitive teaching skills

The trained teacher used more metacognitive strategies than the untrained teacher. Throughout all the lessons, the author observed that the trained teacher facilitated his students’ learning processes via PBL by using meta-cognitive strategies. He confirmed this in stating:

They [the students] work within groups to solve the problem, and I monitor them and coach their thinking with meta-cognitive questions …. For example, I ask students: what they did so far, and what next, did they consider this or that … and so on…. Sometimes, I think aloud and model right behaviours to let them engage in learning processes.

It was observed that students gradually began to depend on their own selves to solve the problems when they found their teacher pushed them to be independent. The trained teacher confirmed the following:

I did not want my students to depend on me. I never give them the solution, but encouraged them to depend on their own effort … And I found coaching their thinking improved their independence.

In contrast, the untrained teacher showed less ability to use meta-cognitive strategies through implementing PBL (author’s observation). The untrained teacher said: ‘They [the students] worked with their groups to solve the problem, and I helped them to solve the problem by indirectly explaining any difficulties, for example, by giving them some examples’. He explained the difficulties and led his students to solve the problem. He did not explain the solution directly, but he gave similar examples, which led them to the correct answer (author’s observation). In some ways, this strategy may be considered a metacognitive activation strategy.

The author observed that students frequently asked their teachers to give them more examples to understand how to solve the problems. The untrained teacher confirmed this: ‘My students are allowed to ask me to give examples to solve the problems, and I always meet their needs’.

Knowledge application in mathematics

From Table 3 , it can be seen that the improvement in the ‘applying achievement’ mean scores increased in all groups. From the mixed-measures ANOVA, as shown in Table 4 , it was found that a statistically significant improvement occurred for the average of students’ scores in knowledge application, F (2, 121) = 76.795, p  = 0.000, with a large effect size at 0.388 (see row 1). However, when time was interacted with the groups (PBL with trained teacher, PBL with untrained teacher and TTM) the result showed a statistically significant effect, F (3, 121) = 4.333, p  = 0.015. The partial eta squared effect size for this statistically significant result was medium, at 0.067 (see row 2). This effect shows that there was an effect on at least one group, but further analysis was needed to identify which group(s) might be affected. Tukey’s post hoc test was applied to determine which of the groups was statistically significantly different from the others. This test found that the mean scores of the group of students taught using the PBL teaching strategy by the trained teacher were statistically significantly different only from the scores of the students taught using TTM, p  = 0.009 (see row 3). This indicates that the average of the PBL group’s scores with the trained teacher significantly improved more than the average of the traditional group’s scores did in ‘applying mathematics’.

Attitudes towards mathematics

From Table 5 , it can be seen that the mean score for ‘attitudes towards mathematics’ increased in groups A and C, while the scores of group B, the traditional group, decreased.

From the mixed-measures ANOVA analysis, as shown in Table 6 , there was no statistically significant improvement occurring for the average of students’ scores in attitudes towards mathematics, F (2, 121) = 0.480, p  = 0.490 (see row 1). However, when time was interacted with groups (PBL with trained teacher, PBL with untrained teacher, and TTM), the result showed a statistically significant effect, F (3, 121) = 12.486, p  = 0.000. The partial eta squared effect size for this statistically significant result was large, at 0.171 (see row 2). Tukey’s post hoc test was applied to determine which of the groups was significantly different from the others in attitudes towards mathematics. This test showed that using PBL with the trained teacher group was significantly different from using TTM, p  = 0.000; using PBL with the untrained teacher group was also significantly different from using TTM, p  = 0.008. However, there was no statistically significant difference between using PBL with the trained and untrained teachers (see row 3). This means that there was a statistically significant difference between the groups attributed to the types of treatment (PBL and TTM) in ‘attitudes towards mathematics’ and in favour of the PBL group, regardless of the different abilities of teachers in PBL implementation.

The study aimed to assess the effect of teacher training on students’ knowledge application and attitudes towards mathematics. The trained teacher demonstrated his ability to facilitate his students’ learning processes by using more metacognitive strategies than the untrained teacher. This result was expected, as many scholars think that training teachers on PBL implementation is critical for success (Barrows, 1996 ; Hmelo-Silver and Barrows, 2006 ; Leary et al., 2009 ; Wosinski et al., 2018 ). The results of the analyses of the interview data and the class observations were convergent. No noticeable difference was identified between the data analyses of class observation and the teachers’ interviews. Below, we consider how the teacher training affected student outcomes. Below, we consider how the teacher training affected student outcomes.

The current study’s quantitative results suggest that when PBL is taught by a teacher who can facilitate the students’ learning processes by using more meta-cognitive strategies, this could improve the application of mathematical knowledge of third-grade students’ significantly more than when they are taught using TTM (see Table 4 ). PBL theorists claim that, when compared with TTM, PBL is more successful in improving knowledge application (Hmelo-Silver, 2004 ; Hmelo-Silver and Barrows, 2008 ). This is because, with PBL, students engage in SDL by using their meta-cognitive learning strategies to solve real-life and ill-structured problems as a way of learning (Chin and Chia, 2006 ). This should reflect some improvement in the students’ ‘application’ ability over TTM (Fogarty, 1994 ). However, for such a method to be effective, skilled teachers who are also able to effectively use meta-cognitive strategies must be present to activate students’ meta-cognitive learning strategies. The trained teacher in PBL is better able to do so.

The role of the teacher in PBL is to facilitate learning processes (Hmelo-Silver and Barrows, 2006 , 2008 ). The shift to PBL requires new teaching roles and skills (Wilkerson and Hundert, 1997 ). Teachers can facilitate PBL processes if they are using meta-cognitive strategies, such as ‘thinking aloud with students’ and ‘modelling behaviours’ (Delisle, 1997 ). In the current study, these skills were shown effectively by the trained teacher; consequently, such strategies were reflected in the improvements to the students’ ‘application’ achievements. However, when students were taught by an untrained teacher, their learning processes were less facilitated. He only responded to difficulties they were experiencing by explaining similar situations (i.e., an example). Even though this approach is considered a metacognitive activation strategy, the students’ solutions were led by these examples. Thus, the teacher’s performance is an important factor that will affect the application of mathematical knowledge among third-grade students.

In terms of teacher training, the findings of the present study are supported by the results of the meta-analysis conducted by Leary et al. ( 2013 ), which showed a statistically significant positive relationship between teacher training and student achievement. The study also suggested that untrained teachers resulted in student outcomes similar to those attained by teachers who use TTM. This is also supported by the results of the current study. Moreover, this study’s findings are in line with those of Maxwell et al. ( 2005 ); these researchers’ conclusion suggests that PBL instruction can improve learning more than TTM can when teachers are well trained in using the PBL strategy. However, the results of the current study support the conclusions of several studies that found students taught via PBL outperformed students taught via TTM in terms of application knowledge (see Tong et al., 2021 ; Wirkala and Kuhn, 2011 ; Wong and Day, 2009 ).

The current study’s results suggested that PBL could significantly improve third-grade students’ attitudes towards mathematics compared with TTM (see Table 6 ). This is supported by the findings of (Lou et al., ( 2011 ) and Tong et al. ( 2021 ). For example, Tong et al. ( 2021 ) suggested that students taught via PBL improved their attitudes towards mathematics more significantly than those taught via TTM. The reason for this is that the students liked active learning and working in groups. This idea was supported by Goodnough and Cashion ( 2006 ), who suggested that young students like this strategy because it encourages active learning, supports working in groups and provides students with a variety of learning approaches and methods. In addition, real-life problems that interest students can be used to motivate students to engage deeply in learning processes when students fully understand them. These kinds of problems are expected to drive students’ curiosity and capture their interest, resulting in more effective student engagement in SDL in order to solve the problems (Schmidt et al., 2009 ).

In this study, the role of the problem was to motivate the students in all lessons taught by teachers trained in implementing PBL. Students became intrinsically motivated when they worked on tasks that stimulated their interests and sense of satisfaction or that challenged them (Hmelo-Silver, 2004 ). The possible reason for this is that the untrained teachers did not give students sufficient time to understand the problem, in contrast with the trained teacher (teachers’ interview and author’s observations).

In sum, PBL could be an effective teaching strategy for improving students’ attitudes towards learning mathematics; this effect is probably due to PBL content (i.e., real-life problems) and the nature of the PBL environment (i.e., eliciting active learning). In addition, PBL could be an effective teaching strategy for improving students’ mathematics application when students’ processes are effectively facilitated; without such facilitation, the effect of PBL instruction will not differ from that of TTM.

Limitations of the study

This study had several limitations. Because of the study design, results could be generated only for young students and for learning mathematics. The sample selection was not completely random, which could also decrease the opportunity to generalise the results of this study. Because of the gender segregation system that is currently operational in Saudi Arabia, the study participants were all male students. Therefore, the results of this study should be generalised with caution, taking these contextualising factors into account.

This study attempted to assess how training teachers in PBL implementation affects student outcomes, including knowledge application and students’ attitudes towards learning mathematics compared with TTM. Overall, the third-grade students who were taught using PBL showed more positive attitudes towards learning mathematics, regardless of whether they were taught by trained or untrained teachers. The study provides evidence that supports the necessity of training teachers to implement PBL effectively, as this will improve students’ mathematics application.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Alreshidi, N.A.K., Lally, V. The effectiveness of training teachers in problem-based learning implementation on students’ outcomes: a mixed-method study. Humanit Soc Sci Commun 11 , 1137 (2024). https://doi.org/10.1057/s41599-024-03638-6

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Research Article

Academic achievement prediction in higher education through interpretable modeling

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliation School of Foreign Languages, Wuhan Business University, Wuhan, Hubei, People’s Republic of China

Roles Investigation, Software, Writing – review & editing

* E-mail: [email protected]

• Sixuan Wang, 

• Published: September 5, 2024
• https://doi.org/10.1371/journal.pone.0309838
• Reader Comments

Student academic achievement is an important indicator for evaluating the quality of education, especially, the achievement prediction empowers educators in tailoring their instructional approaches, thereby fostering advancements in both student performance and the overall educational quality. However, extracting valuable insights from vast educational data to develop effective strategies for evaluating student performance remains a significant challenge for higher education institutions. Traditional machine learning (ML) algorithms often struggle to clearly delineate the interplay between the factors that influence academic success and the resulting grades. To address these challenges, this paper introduces the XGB-SHAP model, a novel approach for predicting student achievement that combines Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP). The model was applied to a dataset from a public university in Wuhan, encompassing the academic records of 87 students who were enrolled in a Japanese course between September 2021 and June 2023. The findings indicate the model excels in accuracy, achieving a Mean absolute error (MAE) of approximately 6 and an R-squared value near 0.82, surpassing three other ML models. The model further uncovers how different instructional modes influence the factors that contribute to student achievement. This insight supports the need for a customized approach to feature selection that aligns with the specific characteristics of each teaching mode. Furthermore, the model highlights the importance of incorporating self-directed learning skills into student-related indicators when predicting academic performance.

Citation: Wang S, Luo B (2024) Academic achievement prediction in higher education through interpretable modeling. PLoS ONE 19(9): e0309838. https://doi.org/10.1371/journal.pone.0309838

Editor: Shahid Akbar, Abdul Wali Khan University Mardan, PAKISTAN

Received: May 30, 2024; Accepted: August 20, 2024; Published: September 5, 2024

Copyright: © 2024 Wang, Luo. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: Fund recepient:Sixuan Wang Funder name: Hubei Provincial Department of Education Grant No: 2022GB087 Project name: A Study on the Curriculum Connection between College Japanese and High School Japanese from the Perspective of Core Literacy. https://jyt.hubei.gov.cn/ The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Context and motivation.

Academic achievement is of paramount importance in educational contexts, serving as a key indicator of both learning ability and the effectiveness of school administration and teaching standards [ 1 ]. The prediction of academic achievement is a continuously evolving topic in educational management. The integration of predictive models in education empowers educators to make well-informed choices, offer specific support, and enhance teaching strategies, thereby improving student learning outcomes [ 2 ].

Previous research on achievement prediction primarily utilized statistical analysis methods to process data and forecast outcomes, with data mainly derived from educational management systems, student identification cards, or surveys [ 3 ]. ML techniques, known for their ability to tackle complex, nonlinear problems without presuppositions, are adept at identifying connections between various parameters [ 4 ]. The state-of-the-art ML techniques for prediction [ 5 ] include K-Nearest Neighbors (KNN), Decision Trees, Random Forests (RF), Support Vector Machines (SVM), Neural Networks, and Naive Bayes. Recent scholarly efforts, both domestically and internationally, have been geared towards increasing the precision of student achievement predictions through technological innovations in algorithms [ 6 – 8 ].

Despite these developments, challenges remain in the domain of achievement prediction. A primary issue is the limited alignment between the outcomes produced by ML algorithms and the foundational principles of education and instruction, leading to hesitancy among educators in relying on these models. Additionally, there is a gap in thorough data analysis, examination of relationships, and investigation into variables that impact student academic performance patterns.

Contribution of the study

In addressing these challenges, our study delivers distinctive contributions to the field of interpretable machine learning within the context of higher education. We delineate these contributions as follows:

• Theoretical contribution: this study introduces ML models coupled with game theory-based SHAP analysis which aims to develop and validate the XGB-SHAP model, a novel approach for interpreting machine learning-based predictions of student achievement, and explore its efficacy across various teaching modalities.
• Practical contributions: It evaluates the significance of different indicators and their positive or negative impacts on prediction outcomes, thus shedding light on the educational implications of achievement prediction models. The findings of this study provide empirical data support for teachers and educators, facilitating the refinement of their instructional strategies.
• Comparative analysis: It explores student achievement prediction models in three distinct educational settings: online, offline, and blended teachings. This exploration reveals variances in teaching patterns across these modes, yielding practical advice for educators in applying these prediction models.

Structure of the article

This paper is organized as follows: Section ‘Literature review’ presents a review of related literatures, providing a comprehensive review of the existing literature on student achievement prediction, examines the prevailing issues and identifies the gaps within the current body of research. Section ‘Methodology’ details the methodology employed in this study, introduces the interpretable performance prediction framework and the indicators system used in this paper and outlines the methodology used to conduct the data analysis for this paper. The findings and their implications are discussed in Sections ‘Case study’ and ‘Results’ respectively. The paper concludes with a summary of our key findings in the final Section ‘Discussion and Conclusions’. Table 1 illustrates the list of abbreviations.

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https://doi.org/10.1371/journal.pone.0309838.t001

Literature review

Previous research, student achievement prediction indicators..

Prediction accuracy largely depends on the careful selection of indicators. The initial and most critical step is the selection of appropriate input data. Previous research has identified three key groups of student-related features as pertinent input parameters: historical student performance, student engagement, and demographic data (Tomasevic et al., 2020).

Historical student performance has been consistently identified as a reliable predictor. For instance, DeBerard et al. [ 9 ] demonstrated that high school GPA is a strong predictor of college academic success. Similarly, Shaw et al. [ 10 ] found that combined SAT scores explain about 28% of the variance in first-year college GPA. Moreover, test scores have been used to predict future academic performance in various studies [ 11 ].

Regarding student engagement, a notable correlation with academic achievement has been observed [ 12 ]. Hussain et al. [ 13 ] identified a moderately strong positive correlation between student engagement and academic achievement. With evolving teaching formats like Massive Open Online Courses and the flipped classrooms, several studies have developed predictive models by analyzing student behaviors in learning management systems, such as video interactions, assignment submissions, and forum discussions [ 14 ]. With the innovation of modern educational technology tools, including artificial intelligence tools (such as ChatGPT) and virtual reality, significant roles have been played in enhancing student learning outcomes by integrating with educational theories like constructivism, experiential learning, and collaborative learning. These technologies, by offering immersive and interactive learning experiences, have increased student engagement, motivation, and critical thinking skills, thereby positively impacting academic performance [ 15 , 16 ].

Studies have also considered demographic factors. Research indicates that demographic factors play a moderate role in predictive accuracy, with relevance around 60% in some studies, while others suggest that these variables have a limited impact on prediction precision [ 5 , 17 ]. Additional indicators, such as student collaboration, teacher-student communication, and psychological factors like motivation and attitude, have also been explored. Recent studies emphasize the importance of considering learners’ psychological well-being and cognitive processes in educational settings [ 18 , 19 ].These motivational and coping strategies remarkably influence students’ learning approaches and overall educational outcomes [ 20 ].

The above discussion shows that student achievement is a composite of cognitive, behavioral, skill-based, and emotional outcomes derived from educational experiences [ 21 ]. Although there is a consensus on the selection of certain important indicators, the selection of the dataset for student achievement prediction varies from study to study. Selecting the most suitable dataset depends largely on the specific goals and objectives of the researchers, with no universally accepted guidelines.

Student achievement prediction models.

Originally, conventional statistical methods such as Discriminant Analysis and Multiple Linear Regression were the predominant approaches in the early stages of educational research [ 22 ]. Furthermore, Structural Equation Modeling (SEM) has been widely adopted in the social sciences. However, these traditional methods have often fallen short of delivering consistent and precise predictions or classifications [ 23 ].

Recently, an array of machine learning algorithms has been employed, including Multiple Regression, Probabilistic and Logistic Regression, Neural Networks, Decision Trees, Random Forests (RF), Genetic Algorithms, and Bayesian algorithms. These have shown varied levels of success in achieving high predictive accuracy [ 24 ]. Comparative studies of machine learning methods have been conducted, with Caruana et al. [ 25 ] exploring the performance evaluation of these models. Their research underscores a fundamental point: no single model or method universally excels across all problems and metrics. Tomasevic et al. [ 5 ] used the Open University Learning Analytics Dataset for a regression problem, finding that Artificial Neural Networks (ANN) and Decision Trees were the most effective, while KNN, SVM, and Bayesian linear regression were less successful.

While previous approaches using machine learning models for predicting student achievement have focused on model optimization [ 26 ], there are growing concerns regarding the opaque nature of complex models, which may hinder their broader application [ 27 ].

Interpretable machine learning models.

Nowadays, with the rapid development of artificial intelligence (AI) technology, ML models are being applied in many critical fields, such as education [ 28 , 29 ], healthcare [ 30 – 32 ]. However, as the number of parameters soars, the ’black-box’ nature of neural networks has raised concerns. Interpretable machine learning is a promising tool to alleviate concerns regarding the opacity of machine learning models. It equips ML models with the capability to articulate their processes in a manner comprehensible to humans [ 33 ].

Broadly, interpretable machine learning methods are divided into two categories: self-interpretation models and post-hoc interpretation methods [ 34 ]. Self-interpreting models typically have a simpler structure and include Linear models, Logistic Regression, and Decision Trees. Post-hoc interpretation methods involve either model-independent or model-specific techniques, applicable to various models but may require additional computational resources and analytical expertise.

Post-hoc or model-independent interpretation methods are extensively used in different scenarios. These include Partial Dependence Plot [ 35 ], Individual Conditional Expectation [ 36 ], Permutation Feature Importance [ 37 ], Local Interpretable Model-agnostic Explanations, and the SHAP method. The survey in the field of information resource management revealed that 83.7% of explainable ML applications utilize post-hoc explanation methods, with SHAP (51.2%) and feature importance analysis (34.1%) being the most common. Unlike traditional feature importance which indicates the significance of features without clarifying their impact on predictions, SHAP offers detailed explanations on both sample and feature levels through various visualizations like waterfall diagrams and feature dependency diagrams.

These interpretative approaches have been applied in diverse fields such as medicine, policymaking, and science, aiding in auditing predictions under circumstances like regulatory pressures and the pursuit of fairness [ 35 ]. However, the critical aspect of interpretability in machine learning models within the domain of educational management research remains underexplored.

Research gap

Given the aforementioned limitations, the interpretability of ML is a contentious issue. The various ML algorithms employed often fail to effectively elucidate the relationship between factors influencing students’ academic performance and their grades. Additionally, they struggle to quantify the impact of each feature on the target value and to determine the positive or negative influence of each characteristic. To address these gaps in the literature, our study delves into the following areas:

• Feature Importance Analysis: Our research will quantify the influence of each feature on the prediction of student performance. This involves a detailed examination of the weight and significance of various factors in determining academic outcomes.
• Impact Assessment: We will assess the positive or negative impact of each feature on the target variable. This is crucial for understanding not only the magnitude of the influence but also its direction.
• Model Comparison: By comparing the interpretability and performance of different ML models, our study seeks to identify the most effective approaches for student achievement prediction.
• Practical Implications: We will discuss the practical implications of our findings, focusing on how increased interpretability can enhance educational practices and inform policy-making.

Through this comprehensive approach, our study seeks to bridge the gap in the current research by providing a clearer understanding of the mechanisms behind student achievement prediction models and their implications for educational stakeholders.

Methodology

Development of an interpretable performance prediction framework.

As shown in Fig 1 , we have developed an interpretable framework for performance prediction. The framework’s core involves extracting five key features: academic factors, student engagement, demographic factors, psychological aspects, and self-directed learning abilities. These features form an input vector that accurately represents factors relevant to achievement prediction. The data for this study is sourced from three main systems: the Education Administration System (EAS), the Chaoxing Xuexitong System, and various questionnaires.

https://doi.org/10.1371/journal.pone.0309838.g001

The methodology progresses in three phases. The initial phase involves creating an indicator system from these features. In the subsequent phase, we focus on constructing and elucidating performance prediction models. Four different ML algorithms are applied to our “learning” dataset. Their effectiveness is evaluated using two standard ML metrics: Mean Absolute Error (MAE) and R-squared ( R 2 ). The optimal model is then selected based on these evaluations. The final stage of our methodology is the model interpretability phase, which accounts for the educational significance of the model by analyzing the importance and directional influence of the indicators. This phase aims to provide educators with insights to refine their teaching strategies.

Development of the indicator system

As mentioned in ‘Literature review’ section, prior research insights advocate categorizing student-related features into historical student performance, engagement, and demographic data [ 5 ]. To capture a holistic view of learner characteristics, we have expanded this system to include psychological factors and self-directed learning capabilities to form a student achievement prediction indicator system, as shown in Table 2 . Considering the minimal variation in age, gender, and other demographic factors in our case study, we have chosen to focus solely on the major as the demographic data point.

https://doi.org/10.1371/journal.pone.0309838.t002

Model training

As SHAP is a model-agnostic interpretation framework, which enables it to be applied across a spectrum of common predictive models. This versatility allows SHAP to provide insights into the decision-making process of these models by quantifying the contribution of each feature to the prediction, thereby enhancing our understanding of the model’s behavior regardless of its underlying structure or algorithmic approach. Commonly used ML models for academic achievement prediction include RF, BPNN, SVM, and XGboost. The rationale for selecting these four models is their proficiency as data-driven prediction methods. RF, an ensemble learning technique, amalgamates numerous decision trees, thereby reducing variance relative to individual trees. It is known for its superior average prediction performance. BPNN, a supervised learning algorithm, builds multi-layer neural networks inspired by biological neurons and employs a back-propagation algorithm for training, excelling in handling non-linear relationships and high-dimensional data. SVM has gained recognition for its effectiveness in classification, regression, and time-series prediction. XGBoost, enhancing the Gradient Boosting Decision Tree algorithm, stands out for its accuracy and flexibility.

In this research, a 5-fold cross-validation approach was implemented to fine-tune the hyperparameter to avoid overfit, optimizing them according to the mean value derived from each test set.

Model interpretability

Addressing the opaque nature of ML models, our research employs the SHAP method for interpretability. Developed by Lundberg and Lee in 2017 [ 39 ], SHAP merges various existing approaches to provide a reliable and intuitive explanation of model predictions. It does so by illustrating how predictions shift when certain variables are omitted. The Python SHAP package ( https://github.com/slundberg/shap ), enables the calculation of SHAP values for any selected model, and it is extensively utilized due to its versatility.

SHAP is characterized by three fundamental properties: local accuracy (the sum of feature attributions equals the model output), missingness (zero attribution for non-present features), and consistency (no decrease in feature attribution despite an increased marginal contribution). A notable advantage of SHAP is its model-agnostic nature, making it applicable to any machine learning model.

Data for this study was obtained from the EAS of a Wuhan-based public university. This system provided access to students’ personal information, such as majors and academic grades. In addition, we gathered course-related learning data from the Chaoxing Xuexitong system, a widely used online education platform in China. To obtain data on self-study hours, learning attitudes, and self-directed learning indicators, we employed questionnaires as the methodological instrument. The learning attitude questionnaire adapted from the English-learning Motivation Scale developed by a Chinese scholar Meihua Liu [ 40 ] who is from Tsinghua University, a tool commonly utilized in in EFL teaching and learning in the Chinese context. For assessing self-directed learning capabilities, we used a questionnaire adapted from Jinfen Xu ‘s [ 41 ] self-directed learning capability scale. These questionnaires were administered in class under instructor supervision and lasted approximately 10 minutes each, aiming to evaluate students’ learning attitudes and their aptitude for independent learning. The surveys were conducted midway through each semester. Our dataset encompasses data from 87 students enrolled in the Japanese course for the class of 2021, spanning three different learning modes. It includes nine indicators linked to student grades, amounting to a total of 2349 data entries. Table 3 shows the types of nine indicators.

https://doi.org/10.1371/journal.pone.0309838.t003

While analyzing the datasets, an imbalanced data pattern was noted. To address this, we grouped students into three broad specialty categories: Arts, Science and Technology, and Arts and Sports. This categorization reduced data sparsity by assigning discrete values (1, 2, 3) to these groups.

Ethical considerations

The study was approved by the institutional review board, and the study runs from September 2021 to June 2023. All participants were not at risk if they chose or declined to participate. Parental consent is not required for undergraduate students participating in the study. Additionally, we explained the purpose of the study in the questionnaire, clarified that it was their right to participate or not to participate in the study, and informed all the participants that ‘submitting answers’ is considered informed consent for researchers to use their questionnaire responses and related data retrieved from EAS and Chaoxing platform in publications of the research.

Experimental setup

In this study, we conducted experiments employed PyCharm version 2022.3.3 as the compilation software, and implemented the algorithmic model using Python. The dataset was randomly partitioned into training and test sets in a 4:1 ratio for robust training and evaluation.

As state in the Methodology Section, we employ four classic ML models as our predictive model for academic performance. Table 4 presents the pseudo-code outlining the experimental procedures.

https://doi.org/10.1371/journal.pone.0309838.t004

Comparison of models

To obtain the optimal model parameters, the hyperparameters of the aforementioned four models were optimized separately. Table 5 displays the optimal hyperparameter combinations for the aforementioned four models.

https://doi.org/10.1371/journal.pone.0309838.t005

Table 6 presents the comparison of the task performance of four models. Both BPNN and XGBoost show higher task performance compared to RF, while SVM lags in terms of task performance. The comparison indicates that XGBoost slightly surpasses BPNN, establishing XGBoost as the model with the best predictive performance. Therefore, this study selects the XGBoost model to fit all the data. SHAP values are used for interpretation.

https://doi.org/10.1371/journal.pone.0309838.t006

Exploratory analysis utilizing XGBoost and SHAP

Given the effectiveness of the XGBoost model, it was selected for further analysis using SHAP to explore teaching patterns within the model across various teaching modes. SHAP offers insights into the influence of each indicator per sample, highlighting both positive and negative effects. In the associated figures, color coding is used to represent the magnitude of eigenvalues, with red indicating high values and blue representing low values.

Figs 2 and 3 shows the importance of indicators and a summary plot for offline teaching. The average SHAP value (horizontal axis) indicates the significance of each indicator, with their order of importance shown on the vertical axis in Fig 2 . Key findings include classroom performance, previous exam grades, and student major as the most influential indicators. The impact of eigenvalues on each sample is depicted in Fig 3 , where each row represents an indicator, each dot signifies a sample, and the SHAP value is plotted on the horizontal axis. Further analysis revealed a positive relationship between prior exam grades, self-directed learning ability, learning attitudes, and their effect on academic achievement predictions. Interestingly, occasional absences did not show a substantial negative influence on predicted grades, hinting at a divergence in the dynamics of college classrooms from high school settings. This might be attributed to the independent learning skills prevalent among college students. Moreover, it was noted that students majoring in Arts and Sports tend to have a slightly negative impact on predicted grades.

https://doi.org/10.1371/journal.pone.0309838.g002

https://doi.org/10.1371/journal.pone.0309838.g003

Analysis of online teaching using XGBoost and SHAP

Figs 4 and 5 presents the indicator importance and summary plot for online teaching. A key observation is the increased influence of previous exam grades on the predicted values in comparison to offline settings. This suggests that students with a strong academic foundation tend to be more self-directed, thereby enhancing their predicted performance more remarkably. The disparity in self-directed learning abilities is more evident in online courses, highlighting the detrimental effect of inadequate self-learning skills on performance. Students struggling with self-learning might not receive timely support, leading to poorer outcomes. In this context, classroom performance becomes a less critical predictor, and the influence of a student’s major on predicted scores also diminishes. Interestingly, self-study time shows a positive correlation with predicted grades, while the relationship between quiz scores and performance prediction remains insignificant.

https://doi.org/10.1371/journal.pone.0309838.g004

https://doi.org/10.1371/journal.pone.0309838.g005

Blended teaching: Insights from XGBoost and SHAP

Figs 6 and 7 examines the indicator importance and summary plot for blended teaching. In this teaching mode, the impact of self-directed learning skills is more notable compared to other teaching methods, possibly due to the adoption of flipped classroom techniques. Self-directed learning shows a stronger positive correlation with both previous exam grades and quiz scores. Furthermore, the relevance of attitude towards learning is accentuated, suggesting its growing importance in blended learning environments where independent study is emphasized.

https://doi.org/10.1371/journal.pone.0309838.g006

https://doi.org/10.1371/journal.pone.0309838.g007

Discussion and conclusions

The prediction of academic achievement in higher education has become an increasingly prominent topic within the field of education [ 42 ]. In today’s information age, the tremendous growth of educational institutions’ electronic data “…can be utilized for discovering unknown patterns and trends” [ 43 ].Recent researches on predicting student performance are frequently spearheaded by educators identifying as "AI" educators to identify features that can be used to make predictions [ 44 ], to identify algorithms that can improve predictions [ 45 ], and to quantify aspects of student performance. However, analyzing performance, providing high-quality education strategies for evaluating the students’ performance from these abundant resources are among the prevailing challenges universities face [ 46 ].

In this research, we have developed the XGB-SHAP model, integrating XGBoost with SHAP, to systematically explore the relationship between grade prediction and diverse indicators across various teaching methods. Focused on university Japanese language classes, our study demonstrated XGBoost’s superior performance over other models, as evidenced by R 2 and MAE metrics. The integration of SHAP offered a clear visual representation, highlighting the mode and directional influence of each indicator and sheds light on the educational implications of ML structures in pedagogy. The study also supported that the XGB-SHAP model can be effectively used in the field of educational management research.

The results reveal that, the study of student achievement prediction, using student-related features, such as student historical achievement, student engagement and demographic data, which have been used as important input features in the previous literature, is not sufficient. With the development of society and the diversification of teaching and learning modes, the importance of self-directed learning skills in the prediction of university students’ performance has been demonstrated in this study. Psychological factors such as attitude towards learning should also be taken into account. The impact of a student’s major on foreign language learning is considerable, which indicate differences in learning environments, cultural factors, motivation to learn foreign languages. While classroom response accuracy and attendance appeared less critical. This suggests a potential shift in focus within higher education classrooms, advocating for a tailored approach to characteristic selection based on teaching modes. This methodology provides educators with a quantitative view of how educational processes affect student achievement.

Our study also shows that the factors influencing student performance vary: offline teaching values classroom performance, while online teaching and blended teaching emphasize independent learning. In blended teaching, quiz scores have a remarkable positive impact, differing from the trends in other modes. This could be attributed to quizzes acting as formative assessments in blended learning, enhancing student participation and providing continual feedback. Consequently, teaching strategies and support systems should be adapted to meet the distinct needs of each teaching mode to optimize learning outcomes.

Acknowledging the formidable technical challenges associated with interpretable machine learning models in practical educational contexts, it is imperative to recognize their substantial contributions in enhancing our comprehension and utility of achievement prediction models. Additionally, they play a pivotal role in mitigating the skepticism harbored by educators towards machine learning models deployed for achievement prediction. Moving forward, there exist several promising avenues for exploration within the realm of interpretable machine models that merit thorough investigation: first, expand the dataset to cover more academic areas, different institutions, and varied student groups. This will test the model’s effectiveness in diverse settings. Second, the refinement and augmentation of existing interpretable models to enhance their accuracy and utility. These directions offer promising avenues for furthering the application and acceptance of interpretable machine learning in educational settings.

Supporting information

S1 file. original data..

https://doi.org/10.1371/journal.pone.0309838.s001

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The Effect of Problem-Solving Instructional Strategies on Students ’ Learning Outcomes in Senior Secondary School Chemistry

This study investigated the use of problems-solving and its effect on student achievement in the mole concept. Ninety six (96) senior secondary II students were randomly selected form Demonstration Secondary School, College of Education Azare. The instrument for data collection was 30-item chemistry achievement test (CAT). The instrument was validated and its reliability determined to be 0.81. Two research questions and two hypotheses guided the study. The data collected were analyzed using mean and standard deviation to answer the research questions, while t-test statistics was used to answer the hypotheses at 0.05 level of significance. The results revealed that student taught using problem-solving performed significantly better than those taught through lecture method. From the findings chemistry teachers are encouraged to attend seminars/workshops on problem -solving in order to facilitate the teaching and learning of chemistry in schools.

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The study investigated the effect of problem solving instructional technique on students' interest in chemistry in Anambra State. Two research questions and two hypotheses guided the study. The study adopted a quasi-experimental specifically pretest-posttest non-equivalent control group design. The population of the study consisted of 541 senior secondary school two (SS2) chemistry students in Awka South local government area. A sample consisting of 87 chemistry students from the two sampled schools was used for the study. The design of the study was quasi-experimental. Chemistry Interest Scale was used as instrument for data collection. Reliability estimate of 0.87 was obtained on CIS using Cronbach's Alphs formula. Mean and standard deviation were used to answer the research questions while analysis of co-variance (ANCOVA) was used to test the null hypotheses at 0.05 level of significance. The result revealed that problem solving method is more effective in enhancing the students' interest in chemistry than the conventional lecture method. There was no significant difference on students' interest in chemistry due to gender. Based on the findings of this study, it was recommended that secondary school teachers should be given adequate training through workshops, symposia, conferences and seminars to help them in update their knowledge on the new teaching techniques and apply or used them in their teaching and learning processes.

The study investigated the effect sequential usage of four teaching methods and its effects on secondary school chemistry students' acquisition of science process. Two research questions guided the study and three null hypotheses were tested at 0.05 level of significance. The pretest posttest non-equivalent control group quasi-experimental design was used in the study. The population of the study was 1, 440 SS2 chemistry in Uvwie local government area of Delta State. The sample size for the study was 216 senior secondary year two (SS 2) chemistry students. The instruments for data Science Process Skills Acquisition Test (SPSAT) validated by two lecturers in Departments of Science Education and Educational Foundations, from Nnamdi Azikiwe University, Awka and one experienced secondary school chemistry teacher. The reliability of the instrument was established using Kuder-Richardson Formula 20 to be 0.60. The data obtained were analyzed using mean and standard deviation to answer the research questions and analysis of covariance was used to test the hypotheses. The findings of the study revealed that there was significant difference between the mean science process skills scores of students taught using different sequence of four teaching methods in favour of the discussion-problem solving-lecture-laboratory sequence of methods. Also, gender had significant influence on the students' acquisition of science process skills. There was a significant ordinal interaction of sequences of teaching methods and gender on students' achievement. The study recommended that effort should be made by chemistry teachers to always conduct a laboratory verification of the chemistry contents taught at the end of the lesson to enable them acquire science process skills necessarily to conduct science experiments.

Task Hierarchy Analysis Model in Cooperative Learning Strategy and Chemistry Students' Performance were investigated in Nwangele Local Government Area of ImoState. Two objectives, two research questions and two hypotheses guided the study. Quasi-experimental design involving pre-test and post-test non-equivalent group was adopted for the study. Out of the total population of four hundred and fifty (450) SS2 Chemistry students, one hundred and six (106) students in two intact classes purposively selected were used as the sample size. Chemistry Performance Test (CPT) which contained twenty (20) multiple choice questions drawn from the topic taught (Acid-Base Reactions) was used for data collection. The instrument was validated with the reliability index of 0.76 using Pearson Product Moment Correlation (PPMC) to determine students' performance. Mean, Standard deviation and Analysis of Covariance (ANCOVA) were used to analyze the data collected. The result revealed that students taught using Task Hierarchy Analysis Model in Cooperative Learning Strategy performed better than those taught using lecture method. The result further showed that a significant difference existed between the academic performance of students taught Chemistry using Task Hierarchy Analysis Model in Cooperative Learning Strategy and lecture method. However, there was no significant variance between the performance of male and female students taught Chemistry using Task Hierarchy Analysis Model in Cooperative Learning Strategy. It was recommended that Chemistry teachers should adopt Task Hierarchy Analysis Model in Cooperative Learning Strategy in this 21 st century classroom to enhance students' performance.

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Emmanuel Achor

The study aimed at analysing the impact of problem solving teaching approach towards gender differences in mathematics self-concept in Kenyan secondary schools. Students from one hundred and nine (109) schools from Vihiga County formed the population of the study. Stratified random sampling was used to select twelve schools from the 109 schools. The sample of the study was 1459 students constituting 742 males and 717 females, purposively and randomly drawn from the twelve schools. Solomon Four-Group design was adopted in the study. The students were assigned to two experimental groups and two control groups. Both groups were taught Commercial Arithmetic. The experimental groups were taught using PSA treatment while control groups were taught by conventional methods. One experimental group and one control group were pre-tested before the implementation of the treatment. Mathematics Self-Concept Questionnaire (MSCQ), validated by the researcher and mathematics education experts was used in data collection. It yielded a reliability coefficient of 0.739 by using Cronbach's alpha. After the treatment, the four groups were post-tested. The study lasted for three weeks. The findings revealed that; there was no statistical difference between mathematics self-concept of boys and girls taught using PSA, though it enhanced their self-concept the most in comparison to the conventional methods. Hitherto, gender had no impact on students' self-concept in mathematics when taught by PSA. It was concluded that PSA positively enhances mathematics self-concept of both genders. It was recommended that the content of PSA should be included in the regular in-service courses organised by the Ministry of Education for practicing teachers to enable them to acquire competencies needed in the use of PSA so as to militate against the low self-concept of students in mathematics.

This study investigated the effects of chemistry-based puzzles on senior secondary school chemistry students' achievement and gender in chemical periodicity. The quasi-experimental design with a pre-test and post-test was adopted in the study. A sample of 129 students from a population of 4369 was used in the study. Students were classified to control and experimental groups. Students in the experimental groups were instructed with chemistry-based puzzles while students in the control groups were instructed with the demonstration method. An achievement test called Chemical Periodicity Concepts Achievement Test (CPCAT), constructed by the researchers and validated by experts from three universities in Nigeria, was used in the study. Reliability coefficient of the instrument was found to be 0.96. Three research questions and three hypotheses were raised in the study. The research questions were answered using mean and standard deviation while hypotheses were tested at 0.05 confidence level with Analysis of covariance (ANCOVA). Findings showed that students in the experimental group who were taught with Puzzle-Based Strategy (PBS) achieved higher scores than those in the control group who were taught with the Demonstration Method. Further analysis with ANCOVA showed that there was significant difference in achievement in the two groups. There was no significant difference in gender by achievement. Text writers and publishers were advised to write texts which should include puzzles as exercises. It was recommended that teachers should incorporate puzzle-based instructional strategies in their teaching as a variety to curb boredom in the classroom due to monotony of the conventional methods, among others.

IOSR Journals publish within 3 days

This study investigated the effect of mastery-based learning approach on the performance and retention of junior secondary students in change of subject formula. Three objectives guided the investigation. The quasi experimental research design which presented one experimental and one control group was employed. A sample of 108 was randomly selected from a population 7,428 junior secondary three students in Port Harcourt Local Government Area of Rivers State Nigeria. An instrument titled "Change of Subject Formula Mathematics Achievement Test" (CSFMAT) was used to collect pretest, posttest and post posttest data from the sample. The reliability of the validated CSFMAT was established to be 0.83 using the test retest reliability method. The mean, standard deviation and Analysis of Covariance statistical tool were used for analysis at .05 probability level. The result showed that the students in the experimental group who were taught with the Mastery-based Learning Approach (MLA) had a higher achievement and retention than the students in the control group who were taught using the non mastery-based approach. Subjecting the hypotheses to statistical test revealed that there was a significant difference in both group with respect to achievement and retention. The finding also revealed that the male students achieved higher than their female counterpart in the experimental group with no significance. It was recommended that Mathematics teachers should identify the mathematics concepts that are bulky so as to break them down into smaller manageable teaching units in order to teach for mastery.

The study investigated the effects of Computer-Aided Concept and Vee-Mapping Strategies on Senior Secondary School Students' Learning Outcomes in Organic Chemistry In Niger State, Nigeria. The study has two objectives, answered two research questions and tested two null hypotheses. The study adopted quasiexperimental research design where an experimental and a control group were used. Two secondary schools were selected and designated as Experimental Group I and Experimental II with a sample size of 57 (26 Males and 27 Female). Reliability coefficient of 0.86 was obtained when Organic Chemistry Achievement Test was administered on the students. The research questions were answered using mean and standard deviation while Analysis of Variance (ANOVA) was used to test the hypotheses using Statistical Package for Social Sciences (SPSS) Version 20 to conduct the statistical analysis. The study revealed that students taught Organic Chemistry using Vee-Mapping Strategy performed better than those taught using Computer-Aided Concept-Diagram. Also, female students performed better than the male students in the same group of Vee-Mapping Strategy when taught Organic Chemistry. Based on the findings, it was recommended that Vee-Mapping Strategy should be used for teaching and learning processes at all levels of education in Nigerian schools.

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Game-Based Learning in Problem Solving Method: The Effects on Students’ Achievement

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• Published: 10 September 2024

Integration of 3D printing and case-based learning in clinical practice for the treatment of developmental dysplasia of the hip

• Shuo Feng 1   na1 ,
• Ying-Jin Sun 1   na1 ,
• Qi-Rui Zhu 1 ,
• Si-Feng Shi 1 ,
• Yong-Shuo Zhang 1 &
• Feng Yuan 1  

BMC Medical Education volume  24 , Article number:  986 ( 2024 ) Cite this article

Metrics details

Case-based learning (CBL) utilizing three-dimensional (3D) printed hip joint models is a problem-solving teaching method that combines the tactile and visual advantages of 3D-printed models with CBL. This study aims to investigate the impact of integrating 3D printing with CBL on learning developmental dysplasia of the hip (DDH).

We conducted a prospective study from 2022 to 2023, including 120 fourth-year clinical medical students at Xuzhou Medical University. Students were randomly allocated into two groups of 60 participants each. The CBL group received conventional CBL teaching methods, while the 3D + CBL group utilized 3D-printed models in conjunction with CBL. Post-teaching, we analyzed and compared the theoretical and practical achievements of both groups. A questionnaire was designed to assess the impact of the educational approach on orthopedic surgery learning.

The theory scores of the CBL group (62.88 ± 7.98) and 3D + CBL group (66.35 ± 8.85) were significantly different (t = 2.254, P  = 0.026); the practical skills scores of the CBL group (57.40 ± 8.80) and 3D + CBL group (63.42 ± 11.14) were significantly different (t = 3.283, P  = 0.001). The questionnaire results showed that the 3D + CBL group was greater than the CBL group in terms of hip fundamentals, ability to diagnose cases and plan treatments, interesting teaching content, willingness to communicate with the instructor and satisfaction.

Conclusions

The integration of 3D printing with case-based learning has yielded positive outcomes in teaching DDH, providing valuable insights into the use of 3D-printed orthopedic models in clinical education.

Peer Review reports

Introduction

Developmental dysplasia of the hip (DDH) is a condition characterised by abnormal development of the hip joint, including dysplasia of the femoral head and acetabulum, subluxation of the hip joint, and dislocation of the hip joint [ 1 , 2 ]. Surgical intervention is a viable treatment option, but the complexity of surgery and the variability of hip joint anatomy pose challenges for both teaching and learning. Knowledge of the three-dimensional (3D) structure of the hip joint requires medical students to have certain clinical practice experience and spatial construction ability, while the traditional teaching of hip diseases mainly relies on textbooks, courseware and cadaveric specimens, which have limitations in understanding the pathological structure of the hip joint and surgical procedures [ 3 , 4 , 5 ]. 3D-printed models visually represent the local anatomical structure of the lesion and have obvious advantages in diagnosing complex diseases and facilitating simulated surgeries [ 6 , 7 , 8 ]. An increasing number of educators are turning to emerging technologies such as 3D printing to bridge the gap between theory and practice in medical education [ 5 , 8 , 9 ].

Case-based learning (CBL) is a pedagogical approach that emphasises the practical application of knowledge in a real-world context [ 10 ]. It entails presenting students with clinical cases and guiding them through the problem-solving process, thereby fostering critical thinking and problem-solving skills as well as promoting long-term retention of knowledge [ 11 , 12 ]. 3D printing and CBL have been widely used in orthopedic teaching to promote students’ understanding of orthopedic surgery and to provide new ideas for orthopedic clinical teaching [ 13 , 14 , 15 ]. The integration of 3D printing technology and CBL methods has enabled the creation of accurate 3D joint models and the simulation of surgical procedures, enhancing the understanding and visualisation of pathological structures and surgical procedures in DDH patients.

The aim of this study was to investigate the impact of the integration of 3D printing and CBL in learning about DDH and to provide insights into future teaching practices. We hypothesise that the integration of 3D-printed models with case-based learning in DDH will have positive effects on grounded theory and practical skills, increasing student interest and satisfaction.

Research design

We selected 120 fourth-year undergraduate university students enrolled in clinical programmes from January 2022 to March 2023 for the study. The students were randomly divided into a CBL group and a CBL + 3D group by a random number table with 60 students in each group. Both groups received a 6-month orthopaedic course. The 3D + CBL group received instructions on 3D-printed models combined with case-based learning (CBL), and the CBL group received only traditional CBL instruction. The content of knowledge covered in the instructions was identical in the 3D + CBL and CBL groups. All students signed an informed consent form prior to participation, making it clear that their participation was voluntary and that they could withdraw from the study at any time. We anonymized all participants’ personal information, using separate study numbers instead of any identifiable information to ensure maximum protection of patient privacy. This study was approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (XYFY2023-KL146-01) and was exempt from review.

Sample size calculation

Sample size calculations were performed using G*Power software (version 3.1.9.6, Dusseldorf, Germany). The determination of the sample size relied on the main outcome indicator of the study: students’ understanding of orthopaedic surgery. A statistical power of 80% and a two-tailed ɑ = 0.05 were assumed. The necessary sample size was estimated to be 50 students per group, based on an effect size of 0.5 (Cohen’s d) derived from the data reported by Zhao et al. [ 16 ]. Considering a potential dropout rate of 20%, a total of 120 students will be recruited.

Teaching methods

A database of adult patients with hip dysplasia was established. Data on patients who underwent total hip arthroplasty for DDH in our department between 2020 and 2021 were collected. The patients had good compliance and could be followed up on time. Among them, there were two cases of Crowe type IV and one case of type III [ 17 ]. Patient data included the general condition, routine preoperative blood tests, CT imaging of the hip joint, diagnosis, treatment plan, diagnostic and therapeutic process, intraoperative photographs and postoperative X-rays. All typical cases were collected with the informed consent of the patients.

A 3D model of the hip joint was created. Preoperative images of the hip joint were imported into the 3D imaging software Mimics Research 21.0 (Materialise, Belgium), Geomagic Studio 2017 (Geomagic, USA) and Solidworks 2018 (Dassault Systemes, USA) in DICOM file format. The preoperative 3D model of the hip joint was built in an orderly manner by combining and utilising the functions of the different modules in the software. On the basis of 3D reconstruction, anatomical measurements, prosthesis design and printing can be performed (Fig.  1 and Figs.  2 ).

Anatomical measurements and prosthesis design on 3D models. Figure legends: a and b . Measurement of transverse and upper and lower acetabular diameters; c and d . Frontal and lateral views of the femur and measurement of the femoral neck stem angle

3D printed model of DDH patients. Figure legends: a , Overall view of the 3D printed model of 2 patients with DDH b , 3D printed right hip joint of DDH patient; c , d printing left hip joint of DDH patient; d , DDH patient left acetabulum e , The left femur portion of the DDH patient contained the femoral head

Comparison of pre- and postclass scores between the CBL + 3D group and CBL group. Figure legends: A&D, B&E and C&F show comparisons of the theoretical, practical, and total scores of the CBL + 3D group and the CBL group before and after class, respectively. *** indicates p  < 0.001

Group and implement teaching. Each group was divided into 6 subgroups of 10 participants, and the class was scheduled for 4 class hours. The lecturers were led by two senior orthopedic surgeons (attending physicians for 5 years) and used the same CBL teaching cases and lectures. The focus of the course was on the anatomy and pathology of DDH and options for surgical treatment. Prior to the lesson, subjects in both groups were allowed to watch instructional videos on hip anatomy and DDH surgery and to prepare for the lesson. The scenario introduced a case of adult DDH for group discussion, and only a 3D-printed model of this patient in the 3D + CBL group and a brief description were provided. Students in both groups will work in groups to search for information and discuss the development of a treatment plan based on the patient’s history, specialist examination and ancillary tests. Afterwards, the students will design a CBL case based on the surgical procedure of the target orthopedic surgery. Students will be divided into groups, and each group will be given a new case for DDH. They will need to analyse and discuss the case, develop a diagnosis and treatment plan, and present their results to the other members and the lecturers. During the discussion, students in the 3D + CBL group still received a 3D-printed model of the patient. At the end of the discussion, the lecturers commented on the results of the students’ discussions in each group, provided detailed summaries and answers to common and controversial questions, and finally emphasised the importance of deepening the students’ practical mastery of the knowledge related to the anatomy, pathology and surgical treatment of DDH.

Evaluating teaching effectiveness

The primary outcome measure was the improvement in knowledge scores between the pretest and posttest. Prior to the start of the course, both groups of students were asked to complete the same test papers and single-choice and multiple-choice questions before the course. At the end, the teacher assessed the students’ theoretical and practical performance and designed a questionnaire to evaluate the effectiveness of the teaching. The theoretical knowledge assessment included single-choice questions, multiple-choice questions, short-answer questions, and case analysis questions involving basic knowledge points related to hip dysplasia, diagnosis, and surgical methods, totaling 100 points. The practical assessment included history taking, medical record writing, imaging reading and choosing the surgical plan, with a total of 100 points.

At the end of the course, the two groups of students were given the same questionnaire for investigation. The questionnaire consists of 5 parts, and five aspects were scored: mastery of basic hip joint knowledge, ability to diagnose cases and formulate treatment plans, interesting teaching content, willingness to communicate with teachers, and teaching satisfaction. Students were asked to answer the questions based on these items with a total possible score of 10 and a minimum possible score of 2. Higher scores indicate higher student satisfaction. The secondary outcome measures will be surgical skill scores and surgical outcomes.

Statistical analyses

All the statistical analyses were performed using the statistical software SPSS version 26.0 (IBM Corp, Armonk, NY, USA). Independent samples t tests were used to compare continuous variables between two groups. The data are expressed as the mean and standard deviation (SD). The chi-square test was used for discontinuous variables. Differences with P  < 0.05 were considered to indicate statistical significance.

A total of 120 students were included in the experiment. The experimental group included 22 males and 38 females aged between 21 and 23 years, with an average age of 22.03 ± 0.88 years. The control group consisted of 26 males and 34 females aged 21 to 23 years, with a mean age of 21.97 ± 0.66 years; all the students were trainees. There was no significant difference in age (t = 0.468, P  = 0.641) or gender ( \(\:x\) ² = 0.556, P  = 0.456) between the two groups (Table  1 ).

Theoretical and practical performance

There was no significant difference in the performance of the two groups of students on the precourse test. The results of the postcourse assessment showed that the participants in the 3D + CBL group performed better than did those in the CBL group. The theory scores of the CBL group (62.88 ± 7.98) and 3D + CBL group (66.35 ± 8.85) were significantly different (t = 2.254, P  = 0.026); the practical skills scores of the CBL group (57.40 ± 8.80) and 3D + CBL group (63.42 ± 11.14) were significantly different (t = 3.283, P  = 0.001); and the total scores of the CBL group (120.18 ± 12.01) and 3D + CBL group (129.72 ± 14.02) were significantly different (t = 4.001, P  < 0.001) (Table  2 ). In addition, in the comparison of theory scores and total scores before and after the course, the scores of students in both groups improved, and the difference was statistically significant. However, in the practical skills test, there was no difference in the scores of the two tests in the CBL group (Fig.  3 ).

Questionnaire

After teaching, a total of 120 questionnaires were distributed to the students, and the effective recovery rate was 100%. The results showed that the 3D + CBL group was greater than the CBL group in terms of hip fundamentals, ability to diagnose cases and plan treatments, interesting teaching content, willingness to communicate with the instructor and satisfaction (Table  3 ).

Our study showed that physicians in the 3D-printed hip model combined with CBL group outperformed those in the traditional teaching group in both theoretical and clinical practice skills assessment, which mainly included understanding and mastery of imaging features, diagnosis and treatment of osteoarthritic disorders, and surgical protocols. Chen et al [ 18 ] conducted a study investigating the use of 3D printing in combination with PBL in the teaching of surgical skills for the Henle torso variant. The experimental group received traditional classroom instruction supplemented with 3D-printed models, while the control group received a 2D image of the henle trunk plus surgical video. The results showed that the experimental group performed significantly better than the control group in both theoretical knowledge and practical skills. A systematic review by Asif [ 19 ] revealed that patient-specific 3D-printed (3DP) models have been used for clinical training in the UK, especially for rarer and more complex conditions, and that 3DP models are associated with greater user satisfaction and good short-term teaching outcomes. Another study by Sun et al [ 20 ] investigated the application of 3D visualisation combined with project-based learning (PBL) in teaching about spinal disease. The study involved 106 medical students who were randomised into two groups: a group incorporating PBL instruction and a traditional lecture-based classroom group. The researchers concluded that the combination of 3D visualisation and PBL was effective in improving learning outcomes in spine surgery. Studies have shown that the use of 3D-printed models of patient joints can help surgeons better plan and perform surgery [ 21 , 22 ].

Our findings are consistent with previous studies emphasising the pedagogical benefits of incorporating 3D-printed models into surgical training [ 18 , 23 , 24 ]. The tactile and visual advantages provided by these models allow students to have physical access to anatomical structures, greatly improving their understanding of complex joint surgeries [ 25 , 26 ]. As our study demonstrates, this hands-on learning approach leads to better mastery of theoretical knowledge and practical skills. CBL is a teaching methodology that uses cases as a basis and puts empty theories into the context of specific cases for exposition. The use of 3D printing in CBL enables students to solve real-world problems [ 27 ]. In addition, the integration of 3D-printed hip models with CBL promotes active learning and critical thinking while enhancing students’ mastery of orthopedic theoretical knowledge and basic orthopedic operative skills, expanding their clinical thinking, promoting interest and motivation in orthopedic clerkships, and improving their satisfaction with traditional teaching. The interactive nature of CBL encourages students to deal with real-world surgical scenarios and develop problem-solving abilities that are critical problem-solving skills [ 28 ]. This combination of teaching methods not only improves student understanding but also contributes to improved surgical outcomes, consistent with the broader goals of medical education. The findings presented in this paper are consistent with those of previous studies exploring the integration of 3D printing and CBL in medical education. Similar positive results have been reported in studies of related areas such as spinal surgery and rare clinical conditions [ 20 ]. All of these results suggest that if the technology is extended to medical schools, it could improve teaching and learning outcomes and enhance quality to some extent.

A CBL teaching method that incorporates 3D-printed models can have implications for traditional teaching and learning. This interactive learning method can be used as a complementary tool to enhance traditional classroom instruction, especially in understanding complex medical and surgical concepts [ 5 , 8 , 29 ]. Students’ engagement and interest can be increased through the combination of visual and tactile use of 3D-printed models and dynamic learning experience in the CBL teaching method. At the same time, instructors can introduce active learning and critical thinking into the traditional classroom, guiding students to analyse problems and explore solutions in a more holistic manner and improving overall teaching and learning outcomes [ 26 ]. There are many advantages to using triple 3D-printed models as an alternative to actual anatomical specimens in medical education. First, 3D models provide more vivid and clearer visualisations, helping students understand human structures in a more intuitive way [ 25 ]. Second, compared to actual anatomical specimens, 3D models are not limited by time and place, simplifying the teaching process. Educators can also customise different scenarios and content to meet the learning needs and teaching goals of different students, which is especially applicable in personalised case teaching, helping to discuss personalised treatment plans. In contrast, the use of real anatomical specimens involves ethical issues and difficulties, especially for rare lesion specimens from patients with DDH [ 30 ]. Moreover, anatomical specimens may deteriorate, rot or deform over time, making maintenance difficult. In summary, the use of 3D-printed models helps to compensate for the scarcity of anatomical specimens, avoids ethical issues, improves teaching efficiency, and promotes research innovation. This alternative method provides new possibilities and opportunities for medical education and research.

Limitations of the study include the following: ① The sample size and scope limitations include the fact that the study included only 120 students, which may limit the generalizability of the results. In addition, all participants were from the same educational institution, which may have biased their geographical and educational backgrounds. ② Study design: Although randomised grouping was used, it was not clear whether there was a blinded design, i.e., whether the raters and the participants were aware of the group to which they were assigned, which may have affected the objectivity of the results. ③ Validation of the assessment tool: The study used a self-administered questionnaire to conduct the assessment, and the degree of standardisation and validation was not specified. The degree of standardisation and validation was not described in detail, which may have affected the accuracy and reliability of the results. ④ Unknown long-term effects: This study focused mainly on teaching and learning effects in the short term and failed to assess long-term learning outcomes and skill retention. Future research needs to delve deeper into the use of different types of 3D-printed models in orthopedic education and expand beyond the current focus on DDH to other orthopedic areas, such as knee disorders and spinal disorders, to explore the effectiveness of CBL teaching methods that incorporate 3D printing. Future research will also require long-term follow-up studies to assess the lasting impact of the teaching approach on students’ clinical skills and knowledge retention. These studies will provide new insights and innovations to the field of orthopaedic education.

The combination of 3D printing and case-based learning has yielded positive results in treating DDH, providing valuable insights into the use of 3D-printed orthopedic molds in clinical teaching.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Case-Based Learning

Project-Based Learning

Three-Dimensional

developmental dysplasia of the Hip

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The authors received financial support from the Jiangsu Hospital Association Hospital Management Innovation Research Project (JSYGY-3-2023-215), Jiangsu Province Education Science Planning Project (D/2021/01/105), Jiangsu Provincial Department of Science and Technology (BE2022708), Jiangsu Commission of Health (ZD2022064) and Jiangsu Provincial Traditional Chinese Medicine Science and Technology Development Plan (MS2021102).

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Shuo Feng and Ying-Jin Sun contributed equally to this work.

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Department of Orthopedic Surgery, Affiliated Hospital of Xuzhou Medical University, No. 99, Huaihai West Road, 221002, Xuzhou , Jiangsu, P.R. China

Shuo Feng, Ying-Jin Sun, Qi-Rui Zhu, Si-Feng Shi, Yong-Shuo Zhang & Feng Yuan

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F.S. and S.YJ. wrote the main manuscript text and analysed the data. Z.QR. and S.SF. prepared the figures and tables. Z.YS. was responsible for collecting and collating data. Y.F. was responsible for the research design and article evaluation.

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Correspondence to Shuo Feng or Feng Yuan .

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Feng, S., Sun, YJ., Zhu, QR. et al. Integration of 3D printing and case-based learning in clinical practice for the treatment of developmental dysplasia of the hip. BMC Med Educ 24 , 986 (2024). https://doi.org/10.1186/s12909-024-05934-w

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Received : 22 March 2024

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DOI : https://doi.org/10.1186/s12909-024-05934-w

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