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Stressors and coping strategies in the new normal: a case study of teachers in a higher education setting.

Janine Marie Balajadia , Ateneo de Manila University Maria Micole Veatrizze Dy , Ateneo de Manila University Lukas Pariñas , Ateneo de Manila University Christine Leila Taguba , Ateneo de Manila University Alessandra Grace Tan , Ateneo de Manila University Maxine Therese Tuazon , Ateneo de Manila University Jerome Patrick Uy , Ateneo de Manila University Genejane M. Adarlo , Ateneo de Manila University Follow

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Conference Proceeding

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When governments restricted holding in-person classes to contain the spread of COVID-19, many higher education institutions turned to digital technology to continue the education of their students. This abrupt change in the delivery of teaching and learning posed pedagogical and technological challenges to the teachers. And as governments have gradually allowed the return of students to physical classrooms with the decline in COVID-19 cases and the rollout of vaccines, teachers must adapt once more to a different arrangement for teaching and learning. Using the Job Demands-Resources Model as a theoretical framework, this case study examined the stressors (i.e., job demands) encountered by teachers in a higher education setting as students have returned to physical campuses. It also explored their coping strategies (i.e., job resources) that helped them adjust to the demands of using a different arrangement for teaching and learning in the new normal. Thematic analysis of responses to open-ended questions in a survey of 100 teachers in an institution of Catholic higher education in the Philippines showed demands related to teaching as a job and other competing concerns were brought up as stressors when in-person classes resumed after two years of fully online teaching. It also revealed seeking social support, focusing on teaching and research, and practicing self-care as their ways of coping with the demands of the new normal. Findings from this study can contribute to policies that can cater to faculty development.

Recommended Citation

Balajadia, J. M., Dy, M. M. V., Pariñas, L., Taguba, C. L., Tan, A. G., Tuazon, M. T., Uy, J. P., Adarlo, G. (2023). Stressors and Coping Strategies in the New Normal: A Case Study of Teachers in a Higher Education Setting. In N. Callaos, J. Horne, B. Sánchez, M. Savoie (Eds.), Proceedings of the 17th International Multi-Conference on Society, Cybernetics and Informatics: IMSCI 2023, pp. 92-98. International Institute of Informatics and Cybernetics. https://doi.org/10.54808/IMSCI2023.01.92

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What Students Are Teaching the Rest of Us about the New Normal

With the COVID-19 pandemic taking a toll on students, personally and academically, many of them are modeling how to respond to the new normal.

So many of us in higher education, and across the world, are exhausted, frustrated, and anxious. Two years ago, everything started to shut down—initially for only two weeks, maybe three—so that we could "bend the curve" to get the COVID-19 pandemic under control and return to normal life.

In those first weeks, which became months, we learned that technology could enable virtual learning in many remarkable ways. But even with all this technology, students still struggled to make real connections with their professors and with each other. And everywhere, all the time, was the risk that the coronavirus was out there, ready to get you if you were not careful or if you were just unlucky. Now, even with vaccines, COVID-19 surges continue as we enter year three.

On another level, however, something interesting is happening in higher education. With the COVID-19 pandemic also taking a toll on students, personally and academically, many of them are modeling, for the rest of us, how to respond to the new normal. In the last week of my class in December 2021, I asked 110 undergraduates:

"How would you say the COVID-19 pandemic has changed you as a student and as a person?"

Mixed Responses

Students stated a variety of ways in which they had matured during the pandemic. Many offered some version of "I've learned how to manage stress and independence." They reported becoming much better at learning independently and developing important life skills. They noted that they are now more aware of the impact of their actions on other people and that they have new appreciation for friends, family, school, and being in nature.

Other students were much less positive. Academically, they reported: "Online learning is really hard—and I am not good at it." They're delighted to be on campus and in person, but they also had to adjust to going back to the classroom. Some students replied more personally: "I'm anxious and sad." "I'm pessimistic and cynical." "I'm burned out." "I'm stressed out." "I have new and worse mental health issues."

Still other students offered a mixed response. Several said something like: "I am a better person but a worse student." Although they considered themselves to be sophisticated social media users, they know that they spend too much time with it. One insightful student reflected that the impact of the pandemic on students was "huge and may not be fully understood for years."

How are students managing all this? Overall, students are coping with their exhaustion, frustration, and anxiety in some healthy ways: through self-care, openness, and empathy.

Students are making a deliberate choice for self-care. For many students, "self-care" may have been considered an indulgent luxury before the pandemic. They heard people telling them: "You should be in school full-time and doing an internship and working at a job and conducting research and volunteering and and and. . . . If you are resting, if you are not interested, don't worry, there is always someone else, ready to work harder, work faster, get ahead. They will win and you will lose—so you better keep going."

But students seem to have made a conscious decision that go-go-go is no longer the only route. Students are saying "no" to things—things that are not essential or not rewarding, things that might advance their careers but not their lives. Students are focusing on healthier relationships, nutrition and exercise, and positive attitudes. Some of my students, for example, began taking—or teaching—Zoom yoga classes. Others incorporated long walks in green spaces into their busy schedules.

Flowing from self-care, students are now being more open with their instructors. They'll say: "I didn't have time to do the assignment"—offering no excuses or apologies (or drama) and willing to accept any consequences. They speak candidly about their own health. "I didn't come to class with my cough. I didn't want to put anyone at risk." Or: "I got tested. It's strep, not Covid." In response, instructors don't need doctors' notes: we are more trusting, with bonds forged from our common threats and from our students' trust in us.

Students are increasingly open about their mental health as well. Sometimes this goes back to self-care: "I just needed a day off." Other times students offer unapologetic candor about specific mental health diagnoses—information that instructors don't need and that students might once have thought carried real stigma. "I am being treated for anxiety"—or depression or ADHD or bipolar disorder. "I'm a recovering alcoholic and drug addict." "My therapist said . . ."

Students are also more open about other parts of their lives: "My dad lost his job." "I came out to my Mom." "My boyfriend dumped me," "I'm failing chemistry." Expecting no judgment or special treatment, they simply think it's now normal to share.

Finally, students are responding to the new normal with empathy. Instead of scrutinizing the decisions of others—especially on social media—they accept each other's decisions more generously and with support. Students know that there is a lot they don't know about each other: lives, backgrounds, struggles. Students may not understand Alex's priorities or Sasha's choices, and that's okay. Let Alex be Alex and let Sasha be Sasha.

We can't know whether these lessons will last beyond the pandemic. The next cadre of college and university students—struggling today through their own middle and high school journeys—will have been through pandemic-era online learning at a much younger age. But for now, all of us working in higher education can learn from how students are responding to the new normal with self-care, openness, and empathy.

Jim Quirk teaches in the School of Public Affairs at American University in Washington, DC.

© 2022 Jim Quirk. The text of this work is licensed under a Creative Commons BY-NC-ND 4.0 International License.

• Research article
• Open access
• Published: 15 February 2018

Blended learning: the new normal and emerging technologies

• Charles Dziuban 1 ,
• Charles R. Graham 2 ,
• Patsy D. Moskal   ORCID: orcid.org/0000-0001-6376-839X 1 ,
• Anders Norberg 3 &
• Nicole Sicilia 1  

International Journal of Educational Technology in Higher Education volume  15 , Article number:  3 ( 2018 ) Cite this article

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This study addressed several outcomes, implications, and possible future directions for blended learning (BL) in higher education in a world where information communication technologies (ICTs) increasingly communicate with each other. In considering effectiveness, the authors contend that BL coalesces around access, success, and students’ perception of their learning environments. Success and withdrawal rates for face-to-face and online courses are compared to those for BL as they interact with minority status. Investigation of student perception about course excellence revealed the existence of robust if-then decision rules for determining how students evaluate their educational experiences. Those rules were independent of course modality, perceived content relevance, and expected grade. The authors conclude that although blended learning preceded modern instructional technologies, its evolution will be inextricably bound to contemporary information communication technologies that are approximating some aspects of human thought processes.

Introduction

Blended learning and research issues.

Blended learning (BL), or the integration of face-to-face and online instruction (Graham 2013 ), is widely adopted across higher education with some scholars referring to it as the “new traditional model” (Ross and Gage 2006 , p. 167) or the “new normal” in course delivery (Norberg et al. 2011 , p. 207). However, tracking the accurate extent of its growth has been challenging because of definitional ambiguity (Oliver and Trigwell 2005 ), combined with institutions’ inability to track an innovative practice, that in many instances has emerged organically. One early nationwide study sponsored by the Sloan Consortium (now the Online Learning Consortium) found that 65.2% of participating institutions of higher education (IHEs) offered blended (also termed hybrid ) courses (Allen and Seaman 2003 ). A 2008 study, commissioned by the U.S. Department of Education to explore distance education in the U.S., defined BL as “a combination of online and in-class instruction with reduced in-class seat time for students ” (Lewis and Parsad 2008 , p. 1, emphasis added). Using this definition, the study found that 35% of higher education institutions offered blended courses, and that 12% of the 12.2 million documented distance education enrollments were in blended courses.

The 2017 New Media Consortium Horizon Report found that blended learning designs were one of the short term forces driving technology adoption in higher education in the next 1–2 years (Adams Becker et al. 2017 ). Also, blended learning is one of the key issues in teaching and learning in the EDUCAUSE Learning Initiative’s 2017 annual survey of higher education (EDUCAUSE 2017 ). As institutions begin to examine BL instruction, there is a growing research interest in exploring the implications for both faculty and students. This modality is creating a community of practice built on a singular and pervasive research question, “How is blended learning impacting the teaching and learning environment?” That question continues to gain traction as investigators study the complexities of how BL interacts with cognitive, affective, and behavioral components of student behavior, and examine its transformation potential for the academy. Those issues are so compelling that several volumes have been dedicated to assembling the research on how blended learning can be better understood (Dziuban et al. 2016 ; Picciano et al. 2014 ; Picciano and Dziuban 2007 ; Bonk and Graham 2007 ; Kitchenham 2011 ; Jean-François 2013 ; Garrison and Vaughan 2013 ) and at least one organization, the Online Learning Consortium, sponsored an annual conference solely dedicated to blended learning at all levels of education and training (2004–2015). These initiatives address blended learning in a wide variety of situations. For instance, the contexts range over K-12 education, industrial and military training, conceptual frameworks, transformational potential, authentic assessment, and new research models. Further, many of these resources address students’ access, success, withdrawal, and perception of the degree to which blended learning provides an effective learning environment.

Currently the United States faces a widening educational gap between our underserved student population and those communities with greater financial and technological resources (Williams 2016 ). Equal access to education is a critical need, one that is particularly important for those in our underserved communities. Can blended learning help increase access thereby alleviating some of the issues faced by our lower income students while resulting in improved educational equality? Although most indicators suggest “yes” (Dziuban et al. 2004 ), it seems that, at the moment, the answer is still “to be determined.” Quality education presents a challenge, evidenced by many definitions of what constitutes its fundamental components (Pirsig 1974 ; Arum et al. 2016 ). Although progress has been made by initiatives, such as, Quality Matters ( 2016 ), the OLC OSCQR Course Design Review Scorecard developed by Open SUNY (Open SUNY n.d. ), the Quality Scorecard for Blended Learning Programs (Online Learning Consortium n.d. ), and SERVQUAL (Alhabeeb 2015 ), the issue is by no means resolved. Generally, we still make quality education a perceptual phenomenon where we ascribe that attribute to a course, educational program, or idea, but struggle with precisely why we reached that decision. Searle ( 2015 ), summarizes the problem concisely arguing that quality does not exist independently, but is entirely observer dependent. Pirsig ( 1974 ) in his iconic volume on the nature of quality frames the context this way,

“There is such thing as Quality, but that as soon as you try to define it, something goes haywire. You can’t do it” (p. 91).

Therefore, attempting to formulate a semantic definition of quality education with syntax-based metrics results in what O’Neil (O'Neil 2017 ) terms surrogate models that are rough approximations and oversimplified. Further, the derived metrics tend to morph into goals or benchmarks, losing their original measurement properties (Goodhart 1975 ).

Information communication technologies in society and education

Blended learning forces us to consider the characteristics of digital technology, in general, and information communication technologies (ICTs), more specifically. Floridi ( 2014 ) suggests an answer proffered by Alan Turing: that digital ICTs can process information on their own, in some sense just as humans and other biological life. ICTs can also communicate information to each other, without human intervention, but as linked processes designed by humans. We have evolved to the point where humans are not always “in the loop” of technology, but should be “on the loop” (Floridi 2014 , p. 30), designing and adapting the process. We perceive our world more and more in informational terms, and not primarily as physical entities (Floridi 2008 ). Increasingly, the educational world is dominated by information and our economies rest primarily on that asset. So our world is also blended, and it is blended so much that we hardly see the individual components of the blend any longer. Floridi ( 2014 ) argues that the world has become an “infosphere” (like biosphere) where we live as “inforgs.” What is real for us is shifting from the physical and unchangeable to those things with which we can interact.

Floridi also helps us to identify the next blend in education, involving ICTs, or specialized artificial intelligence (Floridi 2014 , 25; Norberg 2017 , 65). Learning analytics, adaptive learning, calibrated peer review, and automated essay scoring (Balfour 2013 ) are advanced processes that, provided they are good interfaces, can work well with the teacher— allowing him or her to concentrate on human attributes such as being caring, creative, and engaging in problem-solving. This can, of course, as with all technical advancements, be used to save resources and augment the role of the teacher. For instance, if artificial intelligence can be used to work along with teachers, allowing them more time for personal feedback and mentoring with students, then, we will have made a transformational breakthrough. The Edinburg University manifesto for teaching online says bravely, “Automation need not impoverish education – we welcome our robot colleagues” (Bayne et al. 2016 ). If used wisely, they will teach us more about ourselves, and about what is truly human in education. This emerging blend will also affect curricular and policy questions, such as the what? and what for? The new normal for education will be in perpetual flux. Floridi’s ( 2014 ) philosophy offers us tools to understand and be in control and not just sit by and watch what happens. In many respects, he has addressed the new normal for blended learning.

Literature of blended learning

A number of investigators have assembled a comprehensive agenda of transformative and innovative research issues for blended learning that have the potential to enhance effectiveness (Garrison and Kanuka 2004 ; Picciano 2009 ). Generally, research has found that BL results in improvement in student success and satisfaction, (Dziuban and Moskal 2011 ; Dziuban et al. 2011 ; Means et al. 2013 ) as well as an improvement in students’ sense of community (Rovai and Jordan 2004 ) when compared with face-to-face courses. Those who have been most successful at blended learning initiatives stress the importance of institutional support for course redesign and planning (Moskal et al. 2013 ; Dringus and Seagull 2015 ; Picciano 2009 ; Tynan et al. 2015 ). The evolving research questions found in the literature are long and demanding, with varied definitions of what constitutes “blended learning,” facilitating the need for continued and in-depth research on instructional models and support needed to maximize achievement and success (Dringus and Seagull 2015 ; Bloemer and Swan 2015 ).

Educational access

The lack of access to educational technologies and innovations (sometimes termed the digital divide) continues to be a challenge with novel educational technologies (Fairlie 2004 ; Jones et al. 2009 ). One of the promises of online technologies is that they can increase access to nontraditional and underserved students by bringing a host of educational resources and experiences to those who may have limited access to on-campus-only higher education. A 2010 U.S. report shows that students with low socioeconomic status are less likely to obtain higher levels of postsecondary education (Aud et al. 2010 ). However, the increasing availability of distance education has provided educational opportunities to millions (Lewis and Parsad 2008 ; Allen et al. 2016 ). Additionally, an emphasis on open educational resources (OER) in recent years has resulted in significant cost reductions without diminishing student performance outcomes (Robinson et al. 2014 ; Fischer et al. 2015 ; Hilton et al. 2016 ).

Unfortunately, the benefits of access may not be experienced evenly across demographic groups. A 2015 study found that Hispanic and Black STEM majors were significantly less likely to take online courses even when controlling for academic preparation, socioeconomic status (SES), citizenship, and English as a second language (ESL) status (Wladis et al. 2015 ). Also, questions have been raised about whether the additional access afforded by online technologies has actually resulted in improved outcomes for underserved populations. A distance education report in California found that all ethnic minorities (except Asian/Pacific Islanders) completed distance education courses at a lower rate than the ethnic majority (California Community Colleges Chancellor’s Office 2013 ). Shea and Bidjerano ( 2014 , 2016 ) found that African American community college students who took distance education courses completed degrees at significantly lower rates than those who did not take distance education courses. On the other hand, a study of success factors in K-12 online learning found that for ethnic minorities, only 1 out of 15 courses had significant gaps in student test scores (Liu and Cavanaugh 2011 ). More research needs to be conducted, examining access and success rates for different populations, when it comes to learning in different modalities, including fully online and blended learning environments.

Framing a treatment effect

Over the last decade, there have been at least five meta-analyses that have addressed the impact of blended learning environments and its relationship to learning effectiveness (Zhao et al. 2005 ; Sitzmann et al. 2006 ; Bernard et al. 2009 ; Means et al. 2010 , 2013 ; Bernard et al. 2014 ). Each of these studies has found small to moderate positive effect sizes in favor of blended learning when compared to fully online or traditional face-to-face environments. However, there are several considerations inherent in these studies that impact our understanding the generalizability of outcomes.

Dziuban and colleagues (Dziuban et al. 2015 ) analyzed the meta-analyses conducted by Means and her colleagues (Means et al. 2013 ; Means et al. 2010 ), concluding that their methods were impressive as evidenced by exhaustive study inclusion criteria and the use of scale-free effect size indices. The conclusion, in both papers, was that there was a modest difference in multiple outcome measures for courses featuring online modalities—in particular, blended courses. However, with blended learning especially, there are some concerns with these kinds of studies. First, the effect sizes are based on the linear hypothesis testing model with the underlying assumption that the treatment and the error terms are uncorrelated, indicating that there is nothing else going on in the blending that might confound the results. Although the blended learning articles (Means et al. 2010 ) were carefully vetted, the assumption of independence is tenuous at best so that these meta-analysis studies must be interpreted with extreme caution.

There is an additional concern with blended learning as well. Blends are not equivalent because of the manner on which they are configured. For instance, a careful reading of the sources used in the Means, et al. papers will identify, at minimum, the following blending techniques: laboratory assessments, online instruction, e-mail, class web sites, computer laboratories, mapping and scaffolding tools, computer clusters, interactive presentations and e-mail, handwriting capture, evidence-based practice, electronic portfolios, learning management systems, and virtual apparatuses. These are not equivalent ways in which to configure courses, and such nonequivalence constitutes the confounding we describe. We argue here that, in actuality, blended learning is a general construct in the form of a boundary object (Star and Griesemer 1989 ) rather than a treatment effect in the statistical sense. That is, an idea or concept that can support a community of practice, but is weakly defined fostering disagreement in the general group. Conversely, it is stronger in individual constituencies. For instance, content disciplines (i.e. education, rhetoric, optics, mathematics, and philosophy) formulate a more precise definition because of commonly embraced teaching and learning principles. Quite simply, the situation is more complicated than that, as Leonard Smith ( 2007 ) says after Tolstoy,

“All linear models resemble each other, each non nonlinear system is unique in its own way” (p. 33).

This by no means invalidates these studies, but effect size associated with blended learning should be interpreted with caution where the impact is evaluated within a particular learning context.

Study objectives

This study addressed student access by examining success and withdrawal rates in the blended learning courses by comparing them to face-to-face and online modalities over an extended time period at the University of Central Florida. Further, the investigators sought to assess the differences in those success and withdrawal rates with the minority status of students. Secondly, the investigators examined the student end-of-course ratings of blended learning and other modalities by attempting to develop robust if-then decision rules about what characteristics of classes and instructors lead students to assign an “excellent” value to their educational experience. Because of the high stakes nature of these student ratings toward faculty promotion, awards, and tenure, they act as a surrogate measure for instructional quality. Next, the investigators determined the conditional probabilities for students conforming to the identified rule cross-referenced by expected grade, the degree to which they desired to take the course, and course modality.

Student grades by course modality were recoded into a binary variable with C or higher assigned a value of 1, and remaining values a 0. This was a declassification process that sacrificed some specificity but compensated for confirmation bias associated with disparate departmental policies regarding grade assignment. At the measurement level this was an “on track to graduation index” for students. Withdrawal was similarly coded by the presence or absence of its occurrence. In each case, the percentage of students succeeding or withdrawing from blended, online or face-to-face courses was calculated by minority and non-minority status for the fall 2014 through fall 2015 semesters.

Next, a classification and regression tree (CART) analysis (Brieman et al. 1984 ) was performed on the student end-of-course evaluation protocol ( Appendix 1 ). The dependent measure was a binary variable indicating whether or not a student assigned an overall rating of excellent to his or her course experience. The independent measures in the study were: the remaining eight rating items on the protocol, college membership, and course level (lower undergraduate, upper undergraduate, and graduate). Decision trees are efficient procedures for achieving effective solutions in studies such as this because with missing values imputation may be avoided with procedures such as floating methods and the surrogate formation (Brieman et al. 1984 , Olshen et al. 1995 ). For example, a logistic regression method cannot efficiently handle all variables under consideration. There are 10 independent variables involved here; one variable has three levels, another has nine, and eight have five levels each. This means the logistic regression model must incorporate more than 50 dummy variables and an excessively large number of two-way interactions. However, the decision-tree method can perform this analysis very efficiently, permitting the investigator to consider higher order interactions. Even more importantly, decision trees represent appropriate methods in this situation because many of the variables are ordinally scaled. Although numerical values can be assigned to each category, those values are not unique. However, decision trees incorporate the ordinal component of the variables to obtain a solution. The rules derived from decision trees have an if-then structure that is readily understandable. The accuracy of these rules can be assessed with percentages of correct classification or odds-ratios that are easily understood. The procedure produces tree-like rule structures that predict outcomes.

The model-building procedure for predicting overall instructor rating

For this study, the investigators used the CART method (Brieman et al. 1984 ) executed with SPSS 23 (IBM Corp 2015 ). Because of its strong variance-sharing tendencies with the other variables, the dependent measure for the analysis was the rating on the item Overall Rating of the Instructor , with the previously mentioned indicator variables (college, course level, and the remaining 8 questions) on the instrument. Tree methods are recursive, and bisect data into subgroups called nodes or leaves. CART analysis bases itself on: data splitting, pruning, and homogeneous assessment.

Splitting the data into two (binary) subsets comprises the first stage of the process. CART continues to split the data until the frequencies in each subset are either very small or all observations in a subset belong to one category (e.g., all observations in a subset have the same rating). Usually the growing stage results in too many terminate nodes for the model to be useful. CART solves this problem using pruning methods that reduce the dimensionality of the system.

The final stage of the analysis involves assessing homogeneousness in growing and pruning the tree. One way to accomplish this is to compute the misclassification rates. For example, a rule that produces a .95 probability that an instructor will receive an excellent rating has an associated error of 5.0%.

Implications for using decision trees

Although decision-tree techniques are effective for analyzing datasets such as this, the reader should be aware of certain limitations. For example, since trees use ranks to analyze both ordinal and interval variables, information can be lost. However, the most serious weakness of decision tree analysis is that the results can be unstable because small initial variations can lead to substantially different solutions.

For this study model, these problems were addressed with the k-fold cross-validation process. Initially the dataset was partitioned randomly into 10 subsets with an approximately equal number of records in each subset. Each cohort is used as a test partition, and the remaining subsets are combined to complete the function. This produces 10 models that are all trained on different subsets of the original dataset and where each has been used as the test partition one time only.

Although computationally dense, CART was selected as the analysis model for a number of reasons— primarily because it provides easily interpretable rules that readers will be able evaluate in their particular contexts. Unlike many other multivariate procedures that are even more sensitive to initial estimates and require a good deal of statistical sophistication for interpretation, CART has an intuitive resonance with researcher consumers. The overriding objective of our choice of analysis methods was to facilitate readers’ concentration on our outcomes rather than having to rely on our interpretation of the results.

Institution-level evaluation: Success and withdrawal

The University of Central Florida (UCF) began a longitudinal impact study of their online and blended courses at the start of the distributed learning initiative in 1996. The collection of similar data across multiple semesters and academic years has allowed UCF to monitor trends, assess any issues that may arise, and provide continual support for both faculty and students across varying demographics. Table  1 illustrates the overall success rates in blended, online and face-to-face courses, while also reporting their variability across minority and non-minority demographics.

While success (A, B, or C grade) is not a direct reflection of learning outcomes, this overview does provide an institutional level indication of progress and possible issues of concern. BL has a slight advantage when looking at overall success and withdrawal rates. This varies by discipline and course, but generally UCF’s blended modality has evolved to be the best of both worlds, providing an opportunity for optimizing face-to-face instruction through the effective use of online components. These gains hold true across minority status. Reducing on-ground time also addresses issues that impact both students and faculty such as parking and time to reach class. In addition, UCF requires faculty to go through faculty development tailored to teaching in either blended or online modalities. This 8-week faculty development course is designed to model blended learning, encouraging faculty to redesign their course and not merely consider blended learning as a means to move face-to-face instructional modules online (Cobb et al. 2012 ; Lowe 2013 ).

Withdrawal (Table  2 ) from classes impedes students’ success and retention and can result in delayed time to degree, incurred excess credit hour fees, or lost scholarships and financial aid. Although grades are only a surrogate measure for learning, they are a strong predictor of college completion. Therefore, the impact of any new innovation on students’ grades should be a component of any evaluation. Once again, the blended modality is competitive and in some cases results in lower overall withdrawal rates than either fully online or face-to-face courses.

The students’ perceptions of their learning environments

Other potentially high-stakes indicators can be measured to determine the impact of an innovation such as blended learning on the academy. For instance, student satisfaction and attitudes can be measured through data collection protocols, including common student ratings, or student perception of instruction instruments. Given that those ratings often impact faculty evaluation, any negative reflection can derail the successful implementation and scaling of an innovation by disenfranchised instructors. In fact, early online and blended courses created a request by the UCF faculty senate to investigate their impact on faculty ratings as compared to face-to-face sections. The UCF Student Perception of Instruction form is released automatically online through the campus web portal near the end of each semester. Students receive a splash page with a link to each course’s form. Faculty receive a scripted email that they can send to students indicating the time period that the ratings form will be available. The forms close at the beginning of finals week. Faculty receive a summary of their results following the semester end.

The instrument used for this study was developed over a ten year period by the faculty senate of the University of Central Florida, recognizing the evolution of multiple course modalities including blended learning. The process involved input from several constituencies on campus (students, faculty, administrators, instructional designers, and others), in attempt to provide useful formative and summative instructional information to the university community. The final instrument was approved by resolution of the senate and, currently, is used across the university. Students’ rating of their classes and instructors comes with considerable controversy and disagreement with researchers aligning themselves on both sides of the issue. Recently, there have been a number of studies criticizing the process (Uttl et al. 2016 ; Boring et al. 2016 ; & Stark and Freishtat 2014 ). In spite of this discussion, a viable alternative has yet to emerge in higher education. So in the foreseeable future, the process is likely to continue. Therefore, with an implied faculty senate mandate this study was initiated by this team of researchers.

Prior to any analysis of the item responses collected in this campus-wide student sample, the psychometric quality (domain sampling) of the information yielded by the instrument was assessed. Initially, the reliability (internal consistency) was derived using coefficient alpha (Cronbach 1951 ). In addition, Guttman ( 1953 ) developed a theorem about item properties that leads to evidence about the quality of one’s data, demonstrating that as the domain sampling properties of items improve, the inverse of the correlation matrix among items will approach a diagonal. Subsequently, Kaiser and Rice ( 1974 ) developed the measure of sampling adequacy (MSA) that is a function of the Guttman Theorem. The index has an upper bound of one with Kaiser offering some decision rules for interpreting the value of MSA. If the value of the index is in the .80 to .99 range, the investigator has evidence of an excellent domain sample. Values in the .70s signal an acceptable result, and those in the .60s indicate data that are unacceptable. Customarily, the MSA has been used for data assessment prior to the application of any dimensionality assessments. Computation of the MSA value gave the investigators a benchmark for the construct validity of the items in this study. This procedure has been recommended by Dziuban and Shirkey ( 1974 ) prior to any latent dimension analysis and was used with the data obtained for this study. The MSA for the current instrument was .98 suggesting excellent domain sampling properties with an associated alpha reliability coefficient of .97 suggesting superior internal consistency. The psychometric properties of the instrument were excellent with both measures.

The online student ratings form presents an electronic data set each semester. These can be merged across time to create a larger data set of completed ratings for every course across each semester. In addition, captured data includes course identification variables including prefix, number, section and semester, department, college, faculty, and class size. The overall rating of effectiveness is used most heavily by departments and faculty in comparing across courses and modalities (Table  3 ).

The finally derived tree (decision rules) included only three variables—survey items that asked students to rate the instructor’s effectiveness at:

Helping students achieve course objectives,

Creating an environment that helps students learn, and

Communicating ideas and information.

None of the demographic variables associated with the courses contributed to the final model. The final rule specifies that if a student assigns an excellent rating to those three items, irrespective of their status on any other condition, the probability is .99 that an instructor will receive an overall rating of excellent. The converse is true as well. A poor rating on all three of those items will lead to a 99% chance of an instructor receiving an overall rating of poor.

Tables  4 , 5 and 6 present a demonstration of the robustness of the CART rule for variables on which it was not developed: expected course grade, desire to take the course and modality.

In each case, irrespective of the marginal probabilities, those students conforming to the rule have a virtually 100% chance of seeing the course as excellent. For instance, 27% of all students expecting to fail assigned an excellent rating to their courses, but when they conformed to the rule the percentage rose to 97%. The same finding is true when students were asked about their desire to take the course with those who strongly disagreed assigning excellent ratings to their courses 26% of the time. However, for those conforming to the rule, that category rose to 92%. When course modality is considered in the marginal sense, blended learning is rated as the preferred choice. However, from Table  6 we can observe that the rule equates student assessment of their learning experiences. If they conform to the rule, they will see excellence.

This study addressed increasingly important issues of student success, withdrawal and perception of the learning environment across multiple course modalities. Arguably these components form the crux of how we will make more effective decisions about how blended learning configures itself in the new normal. The results reported here indicate that blending maintains or increases access for most student cohorts and produces improved success rates for minority and non-minority students alike. In addition, when students express their beliefs about the effectiveness of their learning environments, blended learning enjoys the number one rank. However, upon more thorough analysis of key elements students view as important in their learning, external and demographic variables have minimal impact on those decisions. For example college (i.e. discipline) membership, course level or modality, expected grade or desire to take a particular course have little to do with their course ratings. The characteristics they view as important relate to clear establishment and progress toward course objectives, creating an effective learning environment and the instructors’ effective communication. If in their view those three elements of a course are satisfied they are virtually guaranteed to evaluate their educational experience as excellent irrespective of most other considerations. While end of course rating protocols are summative the three components have clear formative characteristics in that each one is directly related to effective pedagogy and is responsive to faculty development through units such as the faculty center for teaching and learning. We view these results as encouraging because they offer potential for improving the teaching and learning process in an educational environment that increases the pressure to become more responsive to contemporary student lifestyles.

Clearly, in this study we are dealing with complex adaptive systems that feature the emergent property. That is, their primary agents and their interactions comprise an environment that is more than the linear combination of their individual elements. Blending learning, by interacting with almost every aspect of higher education, provides opportunities and challenges that we are not able to fully anticipate.

This pedagogy alters many assumptions about the most effective way to support the educational environment. For instance, blending, like its counterpart active learning, is a personal and individual phenomenon experienced by students. Therefore, it should not be surprising that much of what we have called blended learning is, in reality, blended teaching that reflects pedagogical arrangements. Actually, the best we can do for assessing impact is to use surrogate measures such as success, grades, results of assessment protocols, and student testimony about their learning experiences. Whether or not such devices are valid indicators remains to be determined. We may be well served, however, by changing our mode of inquiry to blended teaching.

Additionally, as Norberg ( 2017 ) points out, blended learning is not new. The modality dates back, at least, to the medieval period when the technology of textbooks was introduced into the classroom where, traditionally, the professor read to the students from the only existing manuscript. Certainly, like modern technologies, books were disruptive because they altered the teaching and learning paradigm. Blended learning might be considered what Johnson describes as a slow hunch (2010). That is, an idea that evolved over a long period of time, achieving what Kaufmann ( 2000 ) describes as the adjacent possible – a realistic next step occurring in many iterations.

The search for a definition for blended learning has been productive, challenging, and, at times, daunting. The definitional continuum is constrained by Oliver and Trigwell ( 2005 ) castigation of the concept for its imprecise vagueness to Sharpe et al.’s ( 2006 ) notion that its definitional latitude enhances contextual relevance. Both extremes alter boundaries such as time, place, presence, learning hierarchies, and space. The disagreement leads us to conclude that Lakoff’s ( 2012 ) idealized cognitive models i.e. arbitrarily derived concepts (of which blended learning might be one) are necessary if we are to function effectively. However, the strong possibility exists that blended learning, like quality, is observer dependent and may not exist outside of our perceptions of the concept. This, of course, circles back to the problem of assuming that blending is a treatment effect for point hypothesis testing and meta-analysis.

Ultimately, in this article, we have tried to consider theoretical concepts and empirical findings about blended learning and their relationship to the new normal as it evolves. Unfortunately, like unresolved chaotic solutions, we cannot be sure that there is an attractor or that it will be the new normal. That being said, it seems clear that blended learning is the harbinger of substantial change in higher education and will become equally impactful in K-12 schooling and industrial training. Blended learning, because of its flexibility, allows us to maximize many positive education functions. If Floridi ( 2014 ) is correct and we are about to live in an environment where we are on the communication loop rather than in it, our educational future is about to change. However, if our results are correct and not over fit to the University of Central Florida and our theoretical speculations have some validity, the future of blended learning should encourage us about the coming changes.

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The authors acknowledge the contributions of several investigators and course developers from the Center for Distributed Learning at the University of Central Florida, the McKay School of Education at Brigham Young University, and Scholars at Umea University, Sweden. These professionals contributed theoretical and practical ideas to this research project and carefully reviewed earlier versions of this manuscript. The Authors gratefully acknowledge their support and assistance.

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Dziuban, C., Graham, C.R., Moskal, P.D. et al. Blended learning: the new normal and emerging technologies. Int J Educ Technol High Educ 15 , 3 (2018). https://doi.org/10.1186/s41239-017-0087-5

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• Blended learning
• Higher education
• Student success
• Student perception of instruction

ORIGINAL RESEARCH article

Learning curves in covid-19: student strategies in the ‘new normal’.

• Auckland University of Technology, School of Sport and Recreation, Auckland, New Zealand

In New Zealand, similar to the rest of the world, the COVID-19 pandemic brought unprecedented disruption to higher education, with a rapid transition to mass online teaching. The 1st year (and 1st semester in particular) of any University degree presents unique challenges for students. Literature suggests these students have significant learning concerns as they adjust to University teaching and assessment requirements. These challenges may be exacerbated with the rapid introduction of online learning environments as they are increasingly disconnected from their peers, and, at a greater risk of struggling with web-based learning technologies.

This study investigated online learning strategies employed by 1st year students and examined the association between these strategies and student achievement. The University’s learning management system (LMS; Blackboard) was used to collect deidentified data related to students’ engagement with online content. The number of times content was clicked was recorded each day for the student’s three courses. These data were collected over a nine-week period for all students ( N = 170) enrolled in the 1st semester of their degree. This nine-week period spanned from the commencement of COVID-19 online learning to the week of final assessments. The relationship between assessment date and online engagement was investigated and linear mixed models were used to determine if engagement with online learning was associated with final course grades.

The results suggested that students adopted a learning strategy that coordinated their online LMS engagement with course assessment due date. Students had a 388% (SD 58%) greater specific engagement with the LMS on the assessment due date and the day prior, than throughout the remainder of their course. A further trend was observed whereby when an assessment was due in one course the students used an ‘online bundle learning’ strategy of increased engagement with the two other courses which has positive practical implications for the timing of uploading new teaching material. Finally, a clear relationship between the level of student LMS engagement and student course grade existed. For every additional week of zero LMS engagement, the odds of a student achieving. a grade lower than B were 1.67 times higher (95% CI 1.24, 2.26; p < 0.001), regardless of the course.

The rapid transition to online learning, as a consequence of COVID-19, has highlighted the risks of student disengagement, and the subsequent impact on lower student achievement across multiple courses. In addition, the authors investigated an ‘online learning bundling’ strategy that emerged; where students engaged more with a course when they were online submitting an assessment in a different course. These results emphasize the need for a university to implement greater cross-faculty coordination with reference to course design, uploading of information to LMS and timing of assessments. Improved coordination would provide a more effective online learning environment that maximizes student engagement and therefore achievement.

Introduction

The transition to higher education (HE) is often a complicated and difficult time for students ( Kember, 2001 ). Many new HE students have moved directly from secondary education to HE and are not used to the typical HE environment. This is characterized by less structured class time per week, less direct contact with peers and teachers, and a greater expectation for independent learning. New HE students need to adjust quickly to these different styles of teaching and assessments, while adapting to the demands of a self-directed and independent approach to their academic work. Successfully adjusting to this increased level of independence in the first year is important, as it has a strong influence on total student effort and level of achievement, as well as increasing the likelihood of the student completing the whole course ( Krause, 2001 , 2005 ). Ultimately, it is each students’ ability to adjust and engage in the HE environment that becomes a strong determinant of their level of engagement and achievement.

The HE environment has several non-academic factors that are related to student's success, time management, engagement and participation. Students must learn to cope with the new and often competing demands of the HE environment. For example, the juggle between work-life balance, and the peaks and troughs of workload. Research by Scherer et al. (2017) found that effective time management was a significant predictor of tertiary academic outcomes, as those with poor time management found it hard to plan and were often rushed at the end of a course or at assessment time. Literature highlights that in HE, there is a significantly positive relationship between students with who do manage their time effectively and academic performance ( Khan et al. (2020) ).

Snyder (1971) often referred to the concept of students understanding the ‘hidden curriculum’ (i.e., students knowing which key assessment points they need to attend and when, in order to achieve). This concept is important when trying to understand how students best strategize or allocate their attention and their time and has been discussed as a potential time-management issue ( Miller and Parlett, 1974 ). However, the concept that is under-researched is the balance between strategic use of time and potentially a miss-management of time, especially for 1st year HE students.

The second non-academic factor associated with academic success is student engagement, which is defined as ‘the quality of effort devoted to educationally purposeful activities that contribute directly to desired outcomes’ ( Chickering and Gamson, 1987 ). One way to consider engagement is that it is a gauge of the strength of the relationship between students and their HE institution. The HE institutions aim is to create an environment that affords learning to happen, but ultimately the final act of engagement lies with the student actions. Understanding and measuring student engagement in HE is a challenge, as it has multi-dimensional mechanisms, such as educational challenge, active learning, student-staff interaction, and support on campus, to name a few.

One weakness of traditionally measuring engagement in HE has been the lack of tools to objectively understand student engagement. The most commonly used tool is the National Survey of Student Engagement (NSSE) which relies on self-reporting survey data. However, ‘active learning’ (i.e., frequency of class participation; Carini et al., 2006 ) has been used in previous research to provide an understanding of HE engagement level. Traditionally, this has been recorded during face-to-face HE program delivered on-campus that typically feature content taught in a classroom at a prescribed time, and supplemented with prescribed readings and assessment ( Broadbent, 2017 ). One of the more recent advancements in trying understanding student's interaction with the virtual environment in is the evolving area of HE is learning analytics (LA). In particular the use of large scale educational data about learners and their contexts. In this area, researchers have presented information about learners and their environment, with an attempt to provide models for future behavior ( Ranjeeth et al., 2020 ). However, it appears that with advances in LA there is still little recorded improvement to student learning, or learning support for students (e.g., Viberg et al., 2018 ). This raises the question about how insights from LA can help facilitate the transfer into learning and teaching practices.

Understanding engagement in online HE learning environments has shown mixed results when compared to face-to-face measures. Research has shown that students that have chosen their University course specifically because it is online are likely to be have been attracted by the high level of flexibility and independence it offers ( Bernard et al., 2004 ). They are confident they have the skills to excel, they enjoy the learning style and have the time management skills required to succeed in the online environment. Indeed, HE students have reported that time management and regular interaction with content and other students were the top skills needed to be successful with online learning ( Roper, 2007 ).

The impact of COVID-19 led to a rapid transition for most HE institutions from face-to-face teaching to online learning environments. While a few HE institutions had online courses or blended courses in place, the majority were not prepared for this rapid change to online delivery and therefore had minimal time to re-design course delivery for this new environment. Unfortunately, there is a paucity of research examining engagement with online learning tools, particularly for those who, due to COVID-19, are suddenly forced to transition from a face-to-face to online environment which was not their initial learning style choice. Many HE institutions use Learning Management Systems (LMS) and this provides an opportunity to explore student engagement via their online learning behaviors. While there are many inter-related factors that influence student engagement, the authors have attempted to respond to the call from Viberg et al. (2018) of combing the science of learning analytics with pedagogical knowledge. Therefore, in order to better support student achievement and enhance the understanding of student engagement behaviors the aims of this study are to; (1) to understand the online learning strategy of 1st year HE students (forced) into an online environment, and (2) to examine how the strategy adopted influences student achievement.

Materials and Methods

Participants.

One hundred and seventy students who were enrolled in three courses as part of the first semester of their undergraduate degree participated in this study. As a response to COVID-19 these students, that were originally enrolled in face-to-face courses, were transferred to online delivery from week three.

Two courses had two assessment points across the semester; one mid-term assessment, and one assessment at the end of semester. While the third course had three assessments. For each course the structure included live online lectures, pre-recorded video content, and weekly online tutorials. The online delivery for the three courses was completed over nine-week period.

Online Engagement

Online engagement and activity was defined as the log data collected by the LMS, e.g., time spent or number of interactions students had with the LMS ( Henrie et al., 2018 ). In this study online engagement was defined as the number of clicks per student recorded on the LMS. For each course online engagement data were extracted from the Blackboard Learning Management System using the in-built reporting feature. For each student, every time content was clicked (e.g., announcements, course materials, assessments) this information was recorded and stored within the LMS. While some engagement research uses log data of time spent logged into a page (e.g., Henrie et al., 2018 ), the authors found that this measure can give a false reading if a page was left open and not attended; thus giving the impression of a very long ‘engagement’ time with the LMS. Retrospective data covering the nine-week period were exported to an Excel spreadsheet, for each of the three courses separately. These data contained a daily breakdown of engagement information for each student (total number of clicks each day), for each of the three courses, across the nine-week period.

Student Achievement

Student achievement was measured using the final course grades that students received at the end of semester. The grading system ranged from 0 to 9, where 9 represented an ‘A+’ grade, 8 represented an ‘A’ grade, and 7 represented an ‘A−’. The lowest passing grade is 1 which represented a ‘C−’, while a 0 was a failure to pass. The final course grade was calculated by averaging the mid-term and final assessment grades.

In the first instance, student online engagement with each of the three courses were summarized using descriptive statistics (mean ± SD). The descriptive analysis was stratified by assessment days, non-assessment days, and the day prior to assessment day. The relationship between an assessment due date and change in online engagement in other courses was examined by calculating the difference between engagement on the due date and the days prior. These differences were presented as Cohen’s D effect sizes with the following thresholds: 0.2 = small effect, 0.5 = medium effect, 0.8 = large effect ( Cohen, 1988 ). All achievement and engagement data was de-identified in order that appropriate ethical standards were maintained.

Lastly, generalized linear mixed models were used to examine if the level of engagement with online content was associated with final course grades. The final grades were dichotomized into ‘B grade or higher’ and ‘Lower than a B grade’ (B grade = 5), as this was the middle grade. This was treated as the outcome variable. Student engagement data was summarized for each student as the number of weeks throughout the nine-week period where students recorded no engagement with the online LMS. This variable, along with the course (three levels) were added as fixed effects, while each student was added as a random effect to account for the repeated measures. These models were specified with a binomial distribution and logit link function and were fit in R software (v 4.0.0) using the lme4 package.

The results section present data to answer the two research aims; (1) To understand the 1st year student’s online learning strategy and engagement and, (2) to examine how the strategy adopted influences student achievement.

The mean number of online interactions per day, along with the assessment dates for each course, is shown below in Figure 1 . The spikes in student online engagement generally coincide with either the actual course assessment date ( Figure 1 , black vertical lines) or the uploading of key information related to an assessment onto the LMS ( Figure 1 , course 1(red) early June and course 2 (green) mid-May).

FIGURE 1 . The distribution of online engagement across the semester, for each course. The black vertical bars represent the assessment dates for each course.

The values in Table 1 represent student engagement strategy through the mean number of interactions with the LMS per student per day per course and course grades.

TABLE 1 . LMS use on assessment and non-assessment days for 3 courses.

The strategy showed the use of a low level of mean daily engagement during the semester (i.e., 3.11–3.94) with relatively high levels of engagement when an assessment was due (i.e., 10.5–15.6). There was a large difference between engagement levels on assessment due dates and ‘day-proceeding assessment due date’ compared to non-assessment days. Student strategy led to 312 and 453% more online interactions when assessments were due. Interactions with the LMS were higher around assessment due dates, however, it is also worth noting that a small part of this increase was caused by students submitting assessment; i.e., on average 3–4 interactions per course to submit an assessment. It is worth noting that each week included online lectures, workshops, discussion boards and readings, so to have a daily use of only 2–3 interactions per day would be considered quite low in relation to the staff expectations of the course demands.

A key part of this study was to understand the learning curves of students in a COVID-19 environment and the link to achievement. It is important to consider the potential achievement implications for the students that adopted a ‘low or no online engagement’ strategy, as across the nine weeks of the three courses, approximately 34% ( n = 53) of all students had two weeks of zero engagement with all of their three courses.

The relationship between final course grades and the number of weeks with no online engagement is presented in Figure 2 below. All three courses displayed a similar trend; as the number of weeks with no online engagement increased, the probability of achieving a B grade or higher significantly decreased. On average, for every additional week of no online engagement, the odds of achieving a B grade or better were 0.60 (95% CI 0.44, 0.81; p < 0.001), regardless of course. The inverse of this ratio can be interpreted as: the odds of achieving a grade lower than B are 1.67 times higher for every additional week of no online engagement.

FIGURE 2 . Relationship between student achievement and the number of weeks with no online engagement. Estimates obtained from a generalized linear mixed model (binomial distribution, logit link). The shaded regions represent 95% confidence intervals.

The final data presented in this study explored learning curves of students online engagement during COVID-19 when an assessment was due in one of the three courses. Table 2 below demonstrates the effect size differences between the online LMS engagement level in one course, coinciding with an assessment due in another course. This measure was determined by comparing the LMS values (mean and SD) on the day of assessment in one course to LMS values of the day before in another course. Findings showed that when an assessment was due in one course, for 80% of the time the students subsequent online engagement increased in one or both of the other two courses, despite those other two courses not having assessments due at that time. For the majority of the cases there were small to moderate effect size differences between an assessment due date and an increase in online engagement in the other courses. This strategy could be described as a ‘bundling effect’ of cross-course online engagement occurring due to assessment deadlines. The two exceptions to the ‘bundling effect’ were, (1) at the start of the semester, when online use was high across all courses as students were adjusting to a new online environment and (2) when an assessment in another course had occurred two days earlier.

TABLE 2 . Effect sizes difference of online engagement in one course, when an assessment is due in another.

This study aimed to understand the student learning curves of 1st semester, 1st year HE students in a COVID-19 enforced online environment, and the relationship with achievement. In order to explore these topical questions, a mixed method modeling was used of daily engagement data from the University LMS and end of semester grades. The clear result from this study has been gaining an understanding of the student engagement strategy and it’s significant connection with the timing of assessments. Specifically, student online engagement displayed large peaks and troughs that correlated with assessment due dates. For many students, they had prolonged periods of little or no engagement with an online course, until close to an assessment due date. The ‘heart-beat’ graphic of Figure 1 that represented the level of online engagement with the LMS during the 56 days of the course, and the assessment due dates for the 3 analyzed courses demonstrated a clear interrelatedness between student online engagement and assessment due dates. This strategy of selective interrelated behavior of ‘when to engage’ online can be in part explained by Snyder (1971) and Miller and Parlett (1974) research of the ‘hidden curriculum’. ‘Hidden curriculum’ research demonstrated that students can be strategic about their use of time and energy in relation to course work and to assessment, and the study approaches in this paper supports this i.e., students spent more effort on tasks relating to assessment. What is uniquely demonstrated in Figure 1 , is just how selective and strong the student behavior is toward assessment timing, but also worryingly the low levels of engagement between assessment dates, in particular the 53 students who had two weeks of no online engagement with their three courses.

Most HE literature links sustained effort and engagement to students’ success. However, this is strategy has not been demonstrated by the students in this study, where students were forced (quickly) to move to the online learning style. Figure 1 , highlighted student engagement was low between assessment due dates, and thus not sustained evenly over the course. Table 1 also showed that the level of daily engagement on the day of and including the day before an assessment was due, was on average 388% (SD 58%) higher than the average of all the other days during the semester. These numbers clearly represent a learning curve strategy where students have focused their engagement with the LMS predominately toward assessment dates; consequently, creating a peaks and troughs approach. This strategy appears to be contrary to HE literature that demonstrates higher engagement, i.e., sustained, and more dedicate time to a subject, the more success a student has ( Carini et al., 2006 ). Having high levels of engagement in learning, but also sustained effort has strong links to building the foundation of skills needed not only for success in HE, but also post HE ( Kuh, 2003 ). In an online learning environment, where a lack of face-face interaction occurs, exceptional online engagement is needed in order to be successful ( Bryson, 2014 ).

While one view of the results in Table 1 and Figure 1 , might support a selective approach to the use of time engaged with the LMS in relation to assessments: a contrasting view of potential concern, for these students in these trough periods. In this study, the authors investigated the peaks and troughs approach, to see if low levels of LMS engagement was a disadvantage for students. The results shown in Figure 2 demonstrated that it was a disadvantage, and that for every additional week of no LMS engagement, the odds of achieving a grade lower than B were almost twice as high. This result unfortunately illustrated that students who implemented a strategy of no LMS engagement for a period, such as a week or more, had a strong negative impact on their final grade. This finding is in line with literature, which links sustained effort and engagement, to a student's success ( Chickering, and Gamson, 1987 ), instead of a peaks and troughs engagement approach as highlighted in this study.

An unexpected result to arise from the analysis of LMS interactions with this research was presented in Table 2 . Here the authors identified that the act of working on one course for a student assessment coincided with increasing engagement in one or both other courses. That is, when a student was online working on one course assessment, they also appeared to use that opportunity to bundle their LMS time and log on and to another course. This could be considered an ‘online bundle learning’ strategy. This strategy has been evidenced in other online environments, for example when the viewing or the sale of one product is bundled to that of another, in order to get greater sales and/or views ( Jiang et al., 2018 ). The results in Table 2 showed effect size differences and ‘online bundle learning’ occurred 80% of the time a student was online for a course with an assessment due, they also had increased levels of LMS engagement in one or both of their other courses. The implications for the HE course leaders is to recognize the positive engagement ‘bundle’ effect when they plan the time to upload new material to their online course so that the engagement of the students is maximised.

A concluding point from Figure 1 is the impact on engagement of the timing of the final course assessments in relation to each other. While the timing of assessments is a challenge in HE, with multiple courses all needing to schedule assessments, having a short space between assessments due dates, may put substantiable pressure on students to complete these assessments. The timing of assessments is a key topic that students in HE cite as a major source of stress ( Divaris, et al., 2008 ). The timing of assessments is an area where there needs to be greater cross faculty integration, to assist with student stress management and well-being ( Divaris et al., 2008 ). Especially with 1st year students, where most courses are the same for students, there is the opportunity for faculty staff to work together and space the assessments more.

In summary, this research aimed to firstly understand the student learning strategy in the enforced COVID-19 environment and the learning curves used by 1st year students. This large cohort of students was a particularly important group to understand, as the strategies developed in the 1st semester of a degree can have an impact on overall HE achievement ( Khan et al.. (2020) . This study revealed that during COVID-19 student online learning engagement followed a strong pattern of peaks and troughs, where their engagement was almost 400% greater when an assessment was due, compared to other times during the semester.

The second research question considered the influence of learning strategy on achievement. The results indicated that if students implemented a compounding none-engagement strategy with a course, then their grades significantly decrease. Success in HE is traditionally linked to sustained engagement, in a course of study, but the learning curves observed did not support this traditional strategy. An alternative ‘online bundle learning’ strategy emerged that occurred across multiple courses. Recognition of this ‘bundle strategy’ in cross faculty communication is an area that needs future investigation. Not only to improve the timing of assessments for students, but to also upload material online to all courses at a time when a student is likely to be submitting an assessment in another course as the student is likely to engage more with the uploaded material at this time.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

This article has been written by the four authors listed. The first two authors have contributed equally to this work and share the first authorship. The third and fourth authors were primarily focused on the data collection and analysis and editing, while the first two authors wrote the 1st draft and final edits.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: online bundle learning, engagment, higher education, COVID-19, Learning Management Systems

Citation: Millar S-K, Spencer K, Stewart T and Dong M (2021) Learning Curves in COVID-19: Student Strategies in the ‘new normal’?. Front. Educ. 6:641262. doi: 10.3389/feduc.2021.641262

Received: 13 December 2020; Accepted: 16 February 2021; Published: 19 March 2021.

Reviewed by:

Copyright © 2021 Millar, Spencer, Stewart and Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sarah-Kate Millar, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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The new normal – a literature review

Covid-19 and the subsequent closure of universities around the world have meant that we all quickly had to learn how to teach remotely. Educators all over the world have gathered experiences and evaluated them, and talk about the present as a "new normal" where both teachers and students expect universities to take advantage of knowledge and skills acquired during the pandemic.

Photo: Christin Hume on Unsplash.com.

This situation with teaching taking place both on campus and online, often called "blended learning", is perhaps better described as the extended classroom. Now that we are seeing changes in our teaching, it is important that we use our common pedagogical experience and keep the focus on our courses’ intended learning outcomes. This article provides a brief overview of research in this area from the past year, drawn from different countries and disciplines.

Students 

Courses that are in part given online provide us with a good opportunity to support students with how to study. This is especially important in beginner courses as it can help lead the students towards greater independence during their education (Nkomo &Nat, p.809). Students who make good use of learning platforms usually have better study results (Nkomo  &Nat, p.813).  However, there is a risk that students from non-academic backgrounds will find it more difficult to use our online resources; for example, they do not always have sufficient equipment at home, or they may have trouble finding a good place to sit when attending virtual meetings (Millican, p. 2-7). One needs to put effort into designing course content on the learning platform (Canvas at Lund University) so it can support all student groups and not just the most experienced, and the support we provide needs to be an integrated part of the course and lead towards the intended learning outcomes. 

Another important thing impacted by Covid-19 was the social opportunities offered in connection with teaching on campus. Students are socialized into the role of being university students, which affects both the self-image and the perception of career opportunities in the field of study (Millican, p.2). When used well, learning platforms provide opportunities to expand student-teacher relations in ways that can compensate for this social deficiency. Examples of this are peer-review that, when done more informally, can become a larger part of teaching, or a teacher can give more kinds of feedback as follow-up to their lessons with e.g. quizzes so that students can evaluate their understanding of the course material (Sharma & Alvi,  Millican, p.14).

The more teaching is done online, the more you need to actively work on creating the experience of a course and the social context of it, e.g. with Q&A and mentoring activities (Yang &Huang, 125-9). From a student perspective, it seems that what you lose in social factors online is outweighed by the opportunity to participate in teaching asynchronously, i.e., you choose for yourself when to watch lectures etc. (Yang &Huang, p.129).

Teachers 

Teachers value lectures in the classroom significantly higher than other forms of teaching (Millican, p.10), whereas our students are more likely to see online and campus teaching as two complementary parts of teaching. One recommendation is to focus online teaching on building professional relationships between teachers and students (Ahmed et al). To achieve this, the teaching in Canvas needs to be developed with the same focus on constructive alignment as other teaching elements. You need to see teaching as a whole that spans all teaching environments, online as well as on campus. No matter what we teach, there is a before, during and after each learning activity. We can plan how these are implemented and make conscious decisions about what should be done online and what should happen on campus (Nkomo &Nat, p.813). 

The learning platform produces a lot of information about how students interact with the published material. This can be used both to develop the course and to catch up and support students who are falling behind (Nkomo & Nat,p. 809-14). There is a clear positive correlation between a high degree of interaction with online teaching materials and high grades. It is also clear that formative assessment, using a learning platform such as Canvas, helps students from non-academic backgrounds (Nkomo & Nat,p.813). With Canvas, we have a learning platform that gives us many opportunities to develop formative assessments. 

The online tools are important, they enable flexibility, personalization, accessibility, and high quality of content. Equally important are the teachers’ part in online teaching where they need to offer support, enthusiasm, attendance and good communication with the students (Sharma & Alvi).  To get there, we all need to be adept at using our tools and seeing the possibilities.

Teaching 

The corona crisis has made us familiar with several of the tools we have available for online teaching; Canvas, quizzes, assignments, Zoom, Teams, Studio, Inspera, Padlet, Mentimeter. Most of us need more or updated training on these tools so that we can make online and on campus teaching work together. It is easy to feel overpowered, or pressed for time (you need about 12 h spread out over a year). Nevertheless, these tools are here, and our students expect teaching with good online support via Canvas (Millican, p.13). 

By thinking of all learning activities (lecture, seminar, laboratory, etc.) as something that has a before with instructions and preparation, a during with hands-on support for how to do things and an after with feedback or some kind of self-evaluation (e.g. a quiz), we can more easily plan which parts should be on campus and which should be given online (Nkomo &Nat,  p.813). 

Mastering the tools means that we can use them both technically and educationally; we can create a quiz and we know how to write good multiple-choice questions. Mastering video in teaching is not only a matter of being able to record and publish a film, but also to be able to judge when and how it works best (Belt & Lowenthal) and to see the different possibilities.

Summary 

The extended classroom is a good opportunity to improve the quality of our teaching. Properly used, we can provide more support when teaching, provide more support to students with special needs, raise the overall quality and, where necessary, also increase time available for teaching. We have the tools and the knowledge to take on a new normal university environment, what we need now is a methodical and pedagogical effort. 

Ahmed, Samar A., Nagwa N. Hegazy, Hany W. Abdel Malak, W. Cliff Kayser, Noha M. Elrafie, Mohammed Hassanien, Abdulmonem A. Al-Hayani, Sherif A. El Saadany, Abdulrahman O.AI-Youbi, and Mohamed H. Shehata. 2020. Model for Utilizing Distance Learning Post COVID-19 Using (PACT)™ a Cross Sectional Qualitative Study,  BMC Medical Education  20 (1): 1-13.   Link to full text here.

Belt, Eric S., and Patrick R. Lowenthal. Video Use in Online and Blended Courses: A Qualitative Synthesis . Distance Education 42, No. 3 (August 2021): 410-40.   Link to full text here.

Mates, Lewis, Adrian Millican, and Erin Hanson. Coping with Covid; Understanding and Mitigating Disadvantages Experienced by First Generation Scholars Studying Online . British Journal of Educational Studies, September8, 2021, 1-22. Link to full text here.

Nkomo, Larian M., and Muesser Nat. 2021. Student Engagement Patterns in a Blended Learning Environment: An Educational Data Mining Approach.  TechTrends: Linking Research & Practice to Improve Learning  65 (5): 808-17. Link to full text here.

Sharma, Anamika, and Irum Alvi. 2021. Evaluating Pre and Post COVID 19 Learning: An Empirical Study of Learners' Perception in Higher Education . Education and Information Technologies, April, 1-18. Link to full text here.

Yang, Bin, and Cheng Huang. 2021. Turn Crisis into Opportunity in Response to COVID-19: Experience from a Chinese University and Future Prospects . Studies in Higher Education  46 (1): 121-32. Link to full text here.

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The “new normal” in education

José augusto pacheco.

Research Centre on Education (CIEd), Institute of Education, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal

Effects rippling from the Covid 19 emergency include changes in the personal, social, and economic spheres. Are there continuities as well? Based on a literature review (primarily of UNESCO and OECD publications and their critics), the following question is posed: How can one resist the slide into passive technologization and seize the possibility of achieving a responsive, ethical, humane, and international-transformational approach to education? Technologization, while an ongoing and evidently ever-intensifying tendency, is not without its critics, especially those associated with the humanistic tradition in education. This is more apparent now that curriculum is being conceived as a complicated conversation. In a complex and unequal world, the well-being of students requires diverse and even conflicting visions of the world, its problems, and the forms of knowledge we study to address them.

From the past, we might find our way to a future unforeclosed by the present (Pinar 2019 , p. 12)

Texts regarding this pandemic’s consequences are appearing at an accelerating pace, with constant coverage by news outlets, as well as philosophical, historical, and sociological reflections by public intellectuals worldwide. Ripples from the current emergency have spread into the personal, social, and economic spheres. But are there continuities as well? Is the pandemic creating a “new normal” in education or simply accenting what has already become normal—an accelerating tendency toward technologization? This tendency presents an important challenge for education, requiring a critical vision of post-Covid-19 curriculum. One must pose an additional question: How can one resist the slide into passive technologization and seize the possibility of achieving a responsive, ethical, humane, and international-transformational approach to education?

The ongoing present

Unpredicted except through science fiction, movie scripts, and novels, the Covid-19 pandemic has changed everyday life, caused wide-scale illness and death, and provoked preventive measures like social distancing, confinement, and school closures. It has struck disproportionately at those who provide essential services and those unable to work remotely; in an already precarious marketplace, unemployment is having terrible consequences. The pandemic is now the chief sign of both globalization and deglobalization, as nations close borders and airports sit empty. There are no departures, no delays. Everything has changed, and no one was prepared. The pandemic has disrupted the flow of time and unraveled what was normal. It is the emergence of an event (think of Badiou 2009 ) that restarts time, creates radical ruptures and imbalances, and brings about a contingency that becomes a new necessity (Žižek 2020 ). Such events question the ongoing present.

The pandemic has reshuffled our needs, which are now based on a new order. Whether of short or medium duration, will it end in a return to the “normal” or move us into an unknown future? Žižek contends that “there is no return to normal, the new ‘normal’ will have to be constructed on the ruins of our old lives, or we will find ourselves in a new barbarism whose signs are already clearly discernible” (Žižek 2020 , p. 3).

Despite public health measures, Gil ( 2020 ) observes that the pandemic has so far generated no physical or spiritual upheaval and no universal awareness of the need to change how we live. Techno-capitalism continues to work, though perhaps not as before. Online sales increase and professionals work from home, thereby creating new digital subjectivities and economies. We will not escape the pull of self-preservation, self-regeneration, and the metamorphosis of capitalism, which will continue its permanent revolution (Wells 2020 ). In adapting subjectivities to the recent demands of digital capitalism, the pandemic can catapult us into an even more thoroughly digitalized space, a trend that artificial intelligence will accelerate. These new subjectivities will exhibit increased capacities for voluntary obedience and programmable functioning abilities, leading to a “new normal” benefiting those who are savvy in software-structured social relationships.

The Covid-19 pandemic has submerged us all in the tsunami-like economies of the Cloud. There is an intensification of the allegro rhythm of adaptation to the Internet of Things (Davies, Beauchamp, Davies, and Price 2019 ). For Latour ( 2020 ), the pandemic has become internalized as an ongoing state of emergency preparing us for the next crisis—climate change—for which we will see just how (un)prepared we are. Along with inequality, climate is one of the most pressing issues of our time (OECD 2019a , 2019b ) and therefore its representation in the curriculum is of public, not just private, interest.

Education both reflects what is now and anticipates what is next, recoding private and public responses to crises. Žižek ( 2020 , p. 117) suggests in this regard that “values and beliefs should not be simply ignored: they play an important role and should be treated as a specific mode of assemblage”. As such, education is (post)human and has its (over)determination by beliefs and values, themselves encoded in technology.

Will the pandemic detoxify our addiction to technology, or will it cement that addiction? Pinar ( 2019 , pp. 14–15) suggests that “this idea—that technological advance can overcome cultural, economic, educational crises—has faded into the background. It is our assumption. Our faith prompts the purchase of new technology and assures we can cure climate change”. While waiting for technology to rescue us, we might also remember to look at ourselves. In this way, the pandemic could be a starting point for a more sustainable environment. An intelligent response to climate change, reactivating the humanistic tradition in education, would reaffirm the right to such an education as a global common good (UNESCO 2015a , p. 10):

This approach emphasizes the inclusion of people who are often subject to discrimination – women and girls, indigenous people, persons with disabilities, migrants, the elderly and people living in countries affected by conflict. It requires an open and flexible approach to learning that is both lifelong and life-wide: an approach that provides the opportunity for all to realize their potential for a sustainable future and a life of dignity”.

Pinar ( 2004 , 2009 , 2019 ) concevies of curriculum as a complicated conversation. Central to that complicated conversation is climate change, which drives the need for education for sustainable development and the grooming of new global citizens with sustainable lifestyles and exemplary environmental custodianship (Marope 2017 ).

The new normal

The pandemic ushers in a “new” normal, in which digitization enforces ways of working and learning. It forces education further into technologization, a development already well underway, fueled by commercialism and the reigning market ideology. Daniel ( 2020 , p. 1) notes that “many institutions had plans to make greater use of technology in teaching, but the outbreak of Covid-19 has meant that changes intended to occur over months or years had to be implemented in a few days”.

Is this “new normal” really new or is it a reiteration of the old?

Digital technologies are the visible face of the immediate changes taking place in society—the commercial society—and schools. The immediate solution to the closure of schools is distance learning, with platforms proliferating and knowledge demoted to information to be exchanged (Koopman 2019 ), like a product, a phenomenon predicted decades ago by Lyotard ( 1984 , pp. 4-5):

Knowledge is and will be produced in order to be sold, it is and will be consumed in order to be valued in a new production: in both cases, the goal is exchange. Knowledge ceases to be an end in itself, it loses its use-value.

Digital technologies and economic rationality based on performance are significant determinants of the commercialization of learning. Moving from physical face-to-face presence to virtual contact (synchronous and asynchronous), the learning space becomes disembodied, virtual not actual, impacting both student learning and the organization of schools, which are no longer buildings but websites. Such change is not only coterminous with the pandemic, as the Education 2030 Agenda (UNESCO 2015b ) testified; preceding that was the Delors Report (Delors 1996 ), which recoded education as lifelong learning that included learning to know, learning to do, learning to be, and learning to live together.

Transnational organizations have specified competences for the 21st century and, in the process, have defined disciplinary and interdisciplinary knowledge that encourages global citizenship, through “the supra curriculum at the global, regional, or international comparative level” (Marope 2017 , p. 10). According to UNESCO ( 2017 ):

While the world may be increasingly interconnected, human rights violations, inequality and poverty still threaten peace and sustainability. Global Citizenship Education (GCED) is UNESCO’s response to these challenges. It works by empowering learners of all ages to understand that these are global, not local issues and to become active promoters of more peaceful, tolerant, inclusive, secure and sustainable societies.

These transnational initiatives have not only acknowledged traditional school subjects but have also shifted the curriculum toward timely topics dedicated to understanding the emergencies of the day (Spiller 2017 ). However, for the OECD ( 2019a ), the “new normal” accentuates two ideas: competence-based education, which includes the knowledges identified in the Delors Report , and a new learning framework structured by digital technologies. The Covid-19 pandemic does not change this logic. Indeed, the interdisciplinary skills framework, content and standardized testing associated with the Programme for International Student Assessment of the OECD has become the most powerful tool for prescribing the curriculum. Educationally, “the universal homogenous ‘state’ exists already. Globalization of standardized testing—the most prominent instance of threatening to restructure schools into technological sites of political socialization, conditioning children for compliance to a universal homogeneous state of mind” (Pinar 2019 , p. 2).

In addition to cognitive and practical skills, this “homogenous state of mind” rests on so-called social and emotional skills in the service of learning to live together, affirming global citizenship, and presumably returning agency to students and teachers (OECD 2019a ). According to Marope ( 2017 , p. 22), “this calls for higher flexibility in curriculum development, and for the need to leave space for curricula interpretation, contextualization, and creativity at the micro level of teachers and classrooms”. Heterogeneity is thus enlisted in the service of both economic homogeneity and disciplinary knowledge. Disciplinary knowledge is presented as universal and endowed with social, moral, and cognitive authority. Operational and effective knowledge becomes central, due to the influence of financial lobbies, thereby ensuring that the logic of the market is brought into the practices of schools. As Pestre ( 2013 , p. 21) observed, “the nature of this knowledge is new: what matters is that it makes hic et nunc the action, its effect and not its understanding”. Its functionality follows (presumably) data and evidence-based management.

A new language is thus imposed on education and the curriculum. Such enforced installation of performative language and Big Data lead to effective and profitable operations in a vast market concerned with competence in operational skills (Lyotard 1984 ). This “new normal” curriculum is said to be more horizontal and less hierarchical and radically polycentric with problem-solving produced through social networks, NGOs, transnational organizations, and think tanks (Pestre 2013 ; Williamson 2013 , 2017 ). Untouched by the pandemic, the “new (old) normal” remains based on disciplinary knowledge and enmeshed in the discourse of standards and accountability in education.

Such enforced commercialism reflects and reinforces economic globalization. Pinar ( 2011 , p. 30) worries that “the globalization of instrumental rationality in education threatens the very existence of education itself”. In his theory, commercialism and the technical instrumentality by which homogenization advances erase education as an embodied experience and the curriculum as a humanistic project. It is a time in which the humanities are devalued as well, as acknowledged by Pinar ( 2019 , p. 19): “In the United States [and in the world] not only does economics replace education—STEM replace the liberal arts as central to the curriculum—there are even politicians who attack the liberal arts as subversive and irrelevant…it can be more precisely characterized as reckless rhetoric of a know-nothing populism”. Replacing in-person dialogical encounters and the educational cultivation of the person (via Bildung and currere ), digital technologies are creating uniformity of learning spaces, in spite of their individualistic tendencies. Of course, education occurs outside schools—and on occasion in schools—but this causal displacement of the centrality of the school implies a devaluation of academic knowledge in the name of diversification of learning spaces.

In society, education, and specifically in the curriculum, the pandemic has brought nothing new but rather has accelerated already existing trends that can be summarized as technologization. Those who can work “remotely” exercise their privilege, since they can exploit an increasingly digital society. They themselves are changed in the process, as their own subjectivities are digitalized, thus predisposing them to a “curriculum of things” (a term coined by Laist ( 2016 ) to describe an object-oriented pedagogical approach), which is organized not around knowledge but information (Koopman 2019 ; Couldry and Mejias 2019 ). This (old) “new normal” was advanced by the OECD, among other international organizations, thus precipitating what some see as “a dynamic and transformative articulation of collective expectations of the purpose, quality, and relevance of education and learning to holistic, inclusive, just, peaceful, and sustainable development, and to the well-being and fulfilment of current and future generations” (Marope 2017 , p. 13). Covid-19, illiberal democracy, economic nationalism, and inaction on climate change, all upend this promise.

Understanding the psychological and cultural complexity of the curriculum is crucial. Without appreciating the infinity of responses students have to what they study, one cannot engage in the complicated conversation that is the curriculum. There must be an affirmation of “not only the individualism of a person’s experience but [of what is] underlining the significance of a person’s response to a course of study that has been designed to ignore individuality in order to buttress nation, religion, ethnicity, family, and gender” (Grumet 2017 , p. 77). Rather than promoting neuroscience as the answer to the problems of curriculum and pedagogy, it is long-past time for rethinking curriculum development and addressing the canonical curriculum question: What knowledge is of most worth from a humanistic perspective that is structured by complicated conversation (UNESCO 2015a ; Pinar 2004 , 2019 )? It promotes respect for diversity and rejection of all forms of (cultural) hegemony, stereotypes, and biases (Pacheco 2009 , 2017 ).

Revisiting the curriculum in the Covid-19 era then expresses the fallacy of the “new normal” but also represents a particular opportunity to promote a different path forward.

Looking to the post-Covid-19 curriculum

Based on the notion of curriculum as a complicated conversation, as proposed by Pinar ( 2004 ), the post-Covid-19 curriculum can seize the possibility of achieving a responsive, ethical, humane education, one which requires a humanistic and internationally aware reconceptualization of curriculum.

While beliefs and values are anchored in social and individual practices (Pinar 2019 , p. 15), education extracts them for critique and reconsideration. For example, freedom and tolerance are not neutral but normative practices, however ideology-free policymakers imagine them to be.

That same sleight-of-hand—value neutrality in the service of a certain normativity—is evident in a digital concept of society as a relationship between humans and non-humans (or posthumans), a relationship not only mediated by but encapsulated within technology: machines interfacing with other machines. This is not merely a technological change, as if it were a quarantined domain severed from society. Technologization is a totalizing digitalization of human experience that includes the structures of society. It is less social than economic, with social bonds now recoded as financial transactions sutured by software. Now that subjectivity is digitalized, the human face has become an exclusively economic one that fabricates the fantasy of rational and free agents—always self-interested—operating in supposedly free markets. Oddly enough, there is no place for a vision of humanistic and internationally aware change. The technological dimension of curriculum is assumed to be the primary area of change, which has been deeply and totally imposed by global standards. The worldwide pandemic supports arguments for imposing forms of control (Žižek 2020 ), including the geolocation of infected people and the suspension—in a state of exception—of civil liberties.

By destroying democracy, the technology of control leads to totalitarianism and barbarism, ending tolerance, difference, and diversity. Remembrance and memory are needed so that historical fascisms (Eley 2020 ) are not repeated, albeit in new disguises (Adorno 2011 ). Technologized education enhances efficiency and ensures uniformity, while presuming objectivity to the detriment of human reflection and singularity. It imposes the running data of the Curriculum of Things and eschews intellectual endeavor, critical attitude, and self-reflexivity.

For those who advocate the primacy of technology and the so-called “free market”, the pandemic represents opportunities not only for profit but also for confirmation of the pervasiveness of human error and proof of the efficiency of the non-human, i.e., the inhuman technology. What may possibly protect children from this inhumanity and their commodification, as human capital, is a humane or humanistic education that contradicts their commodification.

The decontextualized technical vocabulary in use in a market society produces an undifferentiated image in which people are blinded to nuance, distinction, and subtlety. For Pestre, concepts associated with efficiency convey the primacy of economic activity to the exclusion, for instance, of ethics, since those concepts devalue historic (if unrealized) commitments to equality and fraternity by instead emphasizing economic freedom and the autonomy of self-interested individuals. Constructing education as solely economic and technological constitutes a movement toward total efficiency through the installation of uniformity of behavior, devaluing diversity and human creativity.

Erased from the screen is any image of public education as a space of freedom, or as Macdonald ( 1995 , p. 38) holds, any image or concept of “the dignity and integrity of each human”. Instead, what we face is the post-human and the undisputed reign of instrumental reality, where the ends justify the means and human realization is reduced to the consumption of goods and experiences. As Pinar ( 2019 , p. 7) observes: “In the private sphere…. freedom is recast as a choice of consumer goods; in the public sphere, it converts to control and the demand that freedom flourish, so that whatever is profitable can be pursued”. Such “negative” freedom—freedom from constraint—ignores “positive” freedom, which requires us to contemplate—in ethical and spiritual terms—what that freedom is for. To contemplate what freedom is for requires “critical and comprehensive knowledge” (Pestre 2013 , p. 39) not only instrumental and technical knowledge. The humanities and the arts would reoccupy the center of such a curriculum and not be related to its margins (Westbury 2008 ), acknowledging that what is studied within schools is a complicated conversation among those present—including oneself, one’s ancestors, and those yet to be born (Pinar 2004 ).

In an era of unconstrained technologization, the challenge facing the curriculum is coding and STEM (science, technology, engineering, and mathematics), with technology dislodging those subjects related to the human. This is not a classical curriculum (although it could be) but one focused on the emergencies of the moment–namely, climate change, the pandemic, mass migration, right-wing populism, and economic inequality. These timely topics, which in secondary school could be taught as short courses and at the elementary level as thematic units, would be informed by the traditional school subjects (yes, including STEM). Such a reorganization of the curriculum would allow students to see how academic knowledge enables them to understand what is happening to them and their parents in their own regions and globally. Such a cosmopolitan curriculum would prepare children to become citizens not only of their own nations but of the world. This citizenship would simultaneously be subjective and social, singular and universal (Marope 2020 ). Pinar ( 2019 , p. 5) reminds us that “the division between private and public was first blurred then erased by technology”:

No longer public, let alone sacred, morality becomes a matter of privately held values, sometimes monetized as commodities, statements of personal preference, often ornamental, sometimes self-servingly instrumental. Whatever their function, values were to be confined to the private sphere. The public sphere was no longer the civic square but rather, the marketplace, the site where one purchased whatever one valued.

New technological spaces are the universal center for (in)human values. The civic square is now Amazon, Alibaba, Twitter, WeChat, and other global online corporations. The facts of our human condition—a century-old phrase uncanny in its echoes today—can be studied in schools as an interdisciplinary complicated conversation about public issues that eclipse private ones (Pinar 2019 ), including social injustice, inequality, democracy, climate change, refugees, immigrants, and minority groups. Understood as a responsive, ethical, humane and transformational international educational approach, such a post-Covid-19 curriculum could be a “force for social equity, justice, cohesion, stability, and peace” (Marope 2017 , p. 32). “Unchosen” is certainly the adjective describing our obligations now, as we are surrounded by death and dying and threatened by privation or even starvation, as economies collapse and food-supply chains are broken. The pandemic may not mean deglobalization, but it surely accentuates it, as national borders are closed, international travel is suspended, and international trade is impacted by the accompanying economic crisis. On the other hand, economic globalization could return even stronger, as could the globalization of education systems. The “new normal” in education is the technological order—a passive technologization—and its expansion continues uncontested and even accelerated by the pandemic.

Two Greek concepts, kronos and kairos , allow a discussion of contrasts between the quantitative and the qualitative in education. Echoing the ancient notion of kronos are the technologically structured curriculum values of quantity and performance, which are always assessed by a standardized accountability system enforcing an “ideology of achievement”. “While kronos refers to chronological or sequential time, kairos refers to time that might require waiting patiently for a long time or immediate and rapid action; which course of action one chooses will depend on the particular situation” (Lahtinen 2009 , p. 252).

For Macdonald ( 1995 , p. 51), “the central ideology of the schools is the ideology of achievement …[It] is a quantitative ideology, for even to attempt to assess quality must be quantified under this ideology, and the educational process is perceived as a technically monitored quality control process”.

Self-evaluation subjectively internalizes what is useful and in conformity with the techno-economy and its so-called standards, increasingly enforcing technical (software) forms. If recoded as the Internet of Things, this remains a curriculum in allegiance with “order and control” (Doll 2013 , p. 314) School knowledge is reduced to an instrument for economic success, employing compulsory collaboration to ensure group think and conformity. Intertwined with the Internet of Things, technological subjectivity becomes embedded in software, redesigned for effectiveness, i.e., or use-value (as Lyotard predicted).

The Curriculum of Things dominates the Internet, which is simultaneously an object and a thing (see Heidegger 1967 , 1971 , 1977 ), a powerful “technological tool for the process of knowledge building” (Means 2008 , p. 137). Online learning occupies the subjective zone between the “curriculum-as-planned” and the “curriculum-as-lived” (Pinar 2019 , p. 23). The world of the curriculum-as-lived fades, as the screen shifts and children are enmeshed in an ocularcentric system of accountability and instrumentality.

In contrast to kronos , the Greek concept of kairos implies lived time or even slow time (Koepnick 2014 ), time that is “self-reflective” (Macdonald 1995 , p. 103) and autobiographical (Pinar 2009 , 2004), thus inspiring “curriculum improvisation” (Aoki 2011 , p. 375), while emphasizing “the plurality of subjectivities” (Grumet 2017 , p. 80). Kairos emphasizes singularity and acknowledges particularities; it is skeptical of similarities. For Shew ( 2013 , p. 48), “ kairos is that which opens an originary experience—of the divine, perhaps, but also of life or being. Thought as such, kairos as a formative happening—an opportune moment, crisis, circumstance, event—imposes its own sense of measure on time”. So conceived, curriculum can become a complicated conversation that occurs not in chronological time but in its own time. Such dialogue is not neutral, apolitical, or timeless. It focuses on the present and is intrinsically subjective, even in public space, as Pinar ( 2019 , p. 12) writes: “its site is subjectivity as one attunes oneself to what one is experiencing, yes to its immediacy and specificity but also to its situatedness, relatedness, including to what lies beyond it and not only spatially but temporally”.

Kairos is, then, the uniqueness of time that converts curriculum into a complicated conversation, one that includes the subjective reconstruction of learning as a consciousness of everyday life, encouraging the inner activism of quietude and disquietude. Writing about eternity, as an orientation towards the future, Pinar ( 2019 , p. 2) argues that “the second side [the first is contemplation] of such consciousness is immersion in daily life, the activism of quietude – for example, ethical engagement with others”. We add disquietude now, following the work of the Portuguese poet Fernando Pessoa. Disquietude is a moment of eternity: “Sometimes I think I’ll never leave ‘Douradores’ Street. And having written this, it seems to me eternity. Neither pleasure, nor glory, nor power. Freedom, only freedom” (Pesssoa 1991 ).

The disquietude conversation is simultaneously individual and public. It establishes an international space both deglobalized and autonomous, a source of responsive, ethical, and humane encounter. No longer entranced by the distracting dynamic stasis of image-after-image on the screen, the student can face what is his or her emplacement in the physical and natural world, as well as the technological world. The student can become present as a person, here and now, simultaneously historical and timeless.

Conclusions

Slow down and linger should be our motto now. A slogan yes, but it also represents a political, as well as a psychological resistance to the acceleration of time (Berg and Seeber 2016 )—an acceleration that the pandemic has intensified. Covid-19 has moved curriculum online, forcing children physically apart from each other and from their teachers and especially from the in-person dialogical encounters that classrooms can provide. The public space disappears into the pre-designed screen space that software allows, and the machine now becomes the material basis for a curriculum of things, not persons. Like the virus, the pandemic curriculum becomes embedded in devices that technologize our children.

Although one hundred years old, the images created in Modern Times by Charlie Chaplin return, less humorous this time than emblematic of our intensifying subjection to technological necessity. It “would seem to leave us as cogs in the machine, ourselves like moving parts, we keep functioning efficiently, increasing productivity calculating the creative destruction of what is, the human now materialized (de)vices ensnaring us in convenience, connectivity, calculation” (Pinar 2019 , p. 9). Post-human, as many would say.

Technology supports standardized testing and enforces software-designed conformity and never-ending self-evaluation, while all the time erasing lived, embodied experience and intellectual independence. Ignoring the evidence, others are sure that technology can function differently: “Given the potential of information and communication technologies, the teacher should now be a guide who enables learners, from early childhood throughout their learning trajectories, to develop and advance through the constantly expanding maze of knowledge” (UNESCO 2015a , p. 51). Would that it were so.

The canonical question—What knowledge is of most worth?—is open-ended and contentious. In a technologized world, providing for the well-being of children is not obvious, as well-being is embedded in ancient, non-neoliberal visions of the world. “Education is everybody’s business”, Pinar ( 2019 , p. 2) points out, as it fosters “responsible citizenship and solidarity in a global world” (UNESCO 2015a , p. 66), resisting inequality and the exclusion, for example, of migrant groups, refugees, and even those who live below or on the edge of poverty.

In this fast-moving digital world, education needs to be inclusive but not conformist. As the United Nations ( 2015 ) declares, education should ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. “The coming years will be a vital period to save the planet and to achieve sustainable, inclusive human development” (United Nations 2019 , p. 64). Is such sustainable, inclusive human development achievable through technologization? Can technology succeed where religion has failed?

Despite its contradictions and economic emphases, public education has one clear obligation—to create embodied encounters of learning through curriculum conceived as a complicated conversation. Such a conception acknowledges the worldliness of a cosmopolitan curriculum as it affirms the personification of the individual (Pinar 2011 ). As noted by Grumet ( 2017 , p. 89), “as a form of ethics, there is a responsibility to participate in conversation”. Certainly, it is necessary to ask over and over again the canonical curriculum question: What knowledge is of most worth?

If time, technology and teaching are moving images of eternity, curriculum and pedagogy are also, both ‘moving’ and ‘images’ but not an explicit, empirical, or exact representation of eternity…if reality is an endless series of ‘moving images’, the canonical curriculum question—What knowledge is of most worth?—cannot be settled for all time by declaring one set of subjects eternally important” (Pinar 2019 , p. 12).

In a complicated conversation, the curriculum is not a fixed image sliding into a passive technologization. As a “moving image”, the curriculum constitutes a politics of presence, an ongoing expression of subjectivity (Grumet 2017 ) that affirms the infinity of reality: “Shifting one’s attitude from ‘reducing’ complexity to ‘embracing’ what is always already present in relations and interactions may lead to thinking complexly, abiding happily with mystery” (Doll 2012 , p. 172). Describing the dialogical encounter characterizing conceived curriculum, as a complicated conversation, Pinar explains that this moment of dialogue “is not only place-sensitive (perhaps classroom centered) but also within oneself”, because “the educational significance of subject matter is that it enables the student to learn from actual embodied experience, an outcome that cannot always be engineered” (Pinar 2019 , pp. 12–13). Lived experience is not technological. So, “the curriculum of the future is not just a matter of defining content and official knowledge. It is about creating, sculpting, and finessing minds, mentalities, and identities, promoting style of thought about humans, or ‘mashing up’ and ‘making up’ the future of people” (Williamson 2013 , p. 113).

Yes, we need to linger and take time to contemplate the curriculum question. Only in this way will we share what is common and distinctive in our experience of the current pandemic by changing our time and our learning to foreclose on our future. Curriculum conceived as a complicated conversation restarts historical not screen time; it enacts the private and public as distinguishable, not fused in a computer screen. That is the “new normal”.

is full professor in the Department of Curriculum Studies and Educational Technology (Institute of Education, University of Minho, Portugal). His research focuses on curriculum theory, curriculum politics, and teacher training and evaluation. Presently, he is director of the PhD Science Education Program of the University of Minho, member of the Advisory Board of the Organization of Ibero-American Studies, director of the European Journal of Curriculum Studies, and director of the European Association on Curriculum Studies.

My thanks to William F. Pinar. Friendship is another moving image of eternity. I am grateful to the anonymous reviewer. This work is financed by national funds through the FCT - Foundation for Science and Technology, under the project PTDC / CED-EDG / 30410/2017, Centre for Research in Education, Institute of Education, University of Minho.

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Jeneva J. Diez University of Mindanao Digos College, Digos City, Davao del Sur, Philippines

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Ronna Joy C. Dequito University of Mindanao Digos College, Digos City, Davao del Sur, Philippines

Tomas Jr A. Diquito University of Mindanao Digos College, Digos City, Davao del Sur, Philippines

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UNCOVERING LEARNERS’ EXPERIENCES TO NEW NORMAL EDUCATION: IMPLICATIONS OF ASYNCHRONOUS INSTRUCTION IN GE 5: SCIENCE, TECHNOLOGY, AND SOCIETY COURSE TEACHING

The new normal education policy in response to the pandemic crisis pushed institutions to shift from traditional face-to-face to asynchronous instruction that posed challenges particularly to science courses in higher education. The purpose of this study was to understand the learning experiences of the students and the implications of asynchronous teaching instruction in the Science, Technology, and Society course. This study utilized a convergent parallel mixed method of research employing descriptive-comparative and descriptive phenomenological research designs. There were 100 respondents for the quantitative part and 12 participants for the qualitative part. Based on the quantitative findings, the overall implementation of asynchronous instruction in the course was "excellent." Specifically, the level of implementation was "very satisfactory" in terms of Content and Course Evaluation, while "excellent" in terms of Instructional Design, Student Assessment, and Technology. There was no significant difference in the level of implementation of the course asynchronous instruction when analyzed by specialization. Moreover, based on the qualitative analysis, the learning experiences of students in asynchronous instruction were both positive and negative that implied two-way learning experiences. The general recommendation gleaned from the students was science, technology, and society asynchronous delivery improvement that covered teacher improvement, SIM improvement, and assessment tool improvement. The general recommendations of this study were improving asynchronous instruction delivery through teachers training proposals, modification of self-instructional materials, increasing the awareness and effective use of the varied assessment tools in sustaining the needs and interest of students in studying the course, creating a safe learning environment for the students, and conducting future researches to reveal significant factors which affect the learning experiences of students and the other points that the current researchers have not yet explored.

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Students’ Learning Experiences in The New Normal Education

• Norjannah M. Kudto Student, Bachelor of Social Work, Cotabato Foundation College of Science and Technology, Doroluman, Arakan, Cotabato, Philippines
• Husna T. Lumapenet Assistant Professor, Cotabato Foundation College of Science and Technology, Doroluman, Arakan, Cotabato, Philippines
• Tarhata S. Guiamalon Associate Professor, Cotabato State University, Cotabato City, Philippines

This qualitative research design employing phenomenological study aimed to explore students’ learning experiences in new the normal education. An individual interview was conducted in gaining insights into the participants’ perceptions, understanding, and experiences upon their learning in the new normal education. The interview question first focused on the students’ learning experiences in new normal education, and the second was how the students cope with the transition to the new normal education. The purposive sampling method was used in this study’s conduct. Ten (10) selected students were identified as the key informant from Cotabato Foundation College of Science and Technology-Pikit Extension Unit, Batulawan, Pikit, Cotabato, Philippines. The gathered data revealed that students are exhausted and struggling to look for a stable internet connection and become self-reliant. Furthermore, it was also revealed that most participants preferred Blended Learning rather than Distance Learning. Moreover, the study found five coping mechanisms the students used: acceptance, participating and complying, browsing the internet, seeking internet connection from friends, motivation, and hard work. The participants of this study were able to express and share their learning experiences and coping mechanisms. This study could serve as a good source of useful and accurate information to convey awareness to society.

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