Conference Agenda
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22 SES 03 B: Teaching and Learning Approaches and Innovations
Paper Session | ||
| Presentations | ||
22. Research in Higher Education
Paper Teacher–Student Mutual Reactivity in University Classroom Interaction: Evidence from Pre-service Teacher Education Tomas Bata University In Zlín, Czech Republic (Czechia) Presenting Author:Higher education is widely regarded as a natural setting for active and critically stimulating teaching and learning, drawing on a wide range of instructional practices that support deep learning and students’ critical engagement (Hermann, 2013). In recent years, increasing attention has been paid in higher education to students’ active engagement in the teaching and learning process as one of the key prerequisites for high-quality learning and professional development. From this perspective, good teaching is understood as teaching that encourages students to actively discuss subject matter, thereby providing them with sufficient opportunities to practise sophisticated communicative skills that may become their cognitive habits (Šeďová, Šalamounová, Švaříček, et al., 2019). This emphasis is particularly significant in the context of pre-service teacher education (Simpson, 2016), where university teaching not only mediates disciplinary knowledge but also models pedagogical approaches that future teachers may later transfer into their own instructional practice. This study focuses on how university teachers create opportunities for student participation through their interactional practices and on how students themselves actively contribute to the teaching and learning process. Teaching and learning are understood as a process of co-creation, in which knowledge, meanings, and learning opportunities are jointly constructed through dialogic interaction between teachers and students, rather than being unilaterally transmitted from expert to novice. The study is grounded in sociocultural and dialogic perspectives on learning, which conceptualize learning as a socially situated process emerging through interaction. Drawing on Nystrand’s (2001) concept of mutual reactivity, productive classroom dialogue is understood as a sequence of interconnected exchanges in which participants respond to and build upon each other’s contributions, rather than merely reproducing predetermined answers. Examining mutual reactivity in higher education classrooms is important because it illuminates micro-level interactional mechanisms that enable or constrain student engagement. By focusing on how teachers and students respond to one another in real time, research on mutual reactivity moves beyond static descriptions of participation and captures the dynamic, co-constructed nature of pedagogical communication. Understanding these interactional processes is particularly relevant in university teaching, where dialogic practices are often assumed but not systematically examined to the same extent. The findings of this study contribute to a deeper understanding of how dialogic and responsive teaching is enacted in practice and how specific interactional strategies support students’ active involvement in learning. From a practical perspective, the findings may contribute to the professional development of university teachers by offering empirically grounded insights into communicative practices that promote student participation, support deeper learning, and model dialogic approaches that future teachers can transfer into their own pedagogical practice. Methodology, Methods, Research Instruments or Sources Used Using an ethnographic approach to explore everyday teaching practices, the research was conducted within the context of undergraduate teacher education at universities in the Czech Republic. The sample consisted of nine tutors teaching core courses to pre-service teachers. Data collection involved two methods: i. seminar lessons were video-recorded (two lessons captured for each tutor) and transcribed verbatim; and ii. semi-structured interviews were conducted with each tutor following the recorded lessons in order to reflect on the observed teaching practices. All interviews were audio-recorded and transcribed verbatim. The first stage of data analysis involved a series of quantitative analyses aimed to objectively assess students’ active participation in the classroom. Basic participation metrics were measured, specifically the number of students who spoke at least once during a given lesson. In the second step, the duration of both student and teacher utterances in each lesson was examined. The quantitative analysis aimed to provide an objective evaluation of students’ active participation in higher education classrooms, based on the premise that learning is significantly more effective when students are actively involved in the dialogic co-construction of meaning (Wells & Arauz, 2006). Empirical studies indicate that the more students talk, discuss, and engage in argumentation, the better they learn and the more motivated they are to study (Baber, 2020; Bernard et al., 2009). The next step of the analysis involved coding the conversational moves of individual participants. Transcripts of the lessons were utilized for this purpose. In coding the conversational moves, we followed the framework outlined by Hardman (2016). Each utterance was classified as either a teacher initiation (open/closed question), a student response (no response, phrase, simple sentence, elaborated response), or teacher feedback. As Aranda et al. (2018) note, teacher initiation moves enable students to enter classroom discussions, and the cognitive complexity of teachers’ questions influences the length of student responses. This suggests that the type of teacher question may affect quantity and quality of student utterances. Accordingly, we conducted a pairing of the research results, examining the relationship between teacher questions and student responses. The final phase of analysis involved qualitative analysis, aimed at exploring how students engage in classroom communication spontaneously, without teacher prompts (codes: student question, student comment, and student experience during the lesson), and how teachers respond to them. Together, these quantitative and qualitative analytical procedures provided a comprehensive account of classroom interaction and student participation in higher education teaching. Conclusions, Expected Outcomes or Findings The findings demonstrate that the distribution of talk in university classrooms is uneven, with teacher talk remaining dominant. At the same time, substantial differences were observed among teachers in terms of the level of student participation, indicating that teaching practices play a decisive role in shaping interactional dynamics. Higher levels of student engagement were associated with dialogic communication approaches, whereas predominantly monologic teaching resulted in limited student participation. The analysis further showed that closed questions with low cognitive demand prevail in classroom discourse; however, these questions tend to result in limited or no student responses. In contrast, open-ended questions were more likely to prompt elaborated student contributions. Spontaneous student initiatives—such as comments, questions, and the sharing of experiences—emerged as important indicators of active participation, particularly when teachers acknowledged and integrated these contributions into the ongoing instructional discourse. These findings are consistent with previous research emphasizing dialogic teaching as a key factor in promoting student engagement. Several studies (Abdulah et al., 2012; Mundt & Hänze, 2022) recommend that university teachers actively invite student contributions, value diverse responses, and incorporate students’ ideas into classroom interaction. Integrating student contributions not only enhances students’ involvement in learning but also supports their understanding, interpretation of content, and development of critical thinking skills. Similarly, Sybing (2021) highlights dialogic opportunities in teacher–student interaction that facilitate the co-construction of knowledge, such as positioning students at the center of dialogue, validating their responses, and fostering a supportive classroom climate. Overall, the present study underscores that active student participation in higher education is largely shaped by teachers’ interactional choices and their ability to create dialogic spaces that sustain and develop student engagement. References Abdullah, M. Y., Abu Bakar, N. R. A. & Mahbob, M. H. (2012). The Dynamics of Student Participation in Classroom: Observation on Level and forms of Participation. Procedia - Social and Behavioral Sciences, Vol. 59, p. 61-70. https://doi.org/10.1016/j.sbspro.2012.09.246 Aranda, M. L., Lie, R., Guzey, S. S., Makarsu, M., Johnsn, A. & Moore, T. J. (2018). Examining Teacher Talk in an Engineering Design-Based Science Curricural Unit. Research in Science Education, Vol. 50, p. 469-487. Baber, H. (2020). Determinants of students’ perceived learning outcome and satisfaction in online learning during the pandemic of COVID-19. Journal of Education and e-Learning Research, Vol. 7 (3), p. 285–292. https://doi.org/10.20448/journal.509.2020.73.285.292 Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A., & Bethel, E. C. (2009). A meta-analysis of three types of interaction treatments in distance education. Review of Educational research, Vol. 79(3), p. 1243–1289. Hardman J. (2016). Tutor–student interaction in seminar teaching: Implications for professional development. Active Learning in Higher Education, Vol. 17, (1), p. https://doi.org/10.1177/1469787415616728 Hermann, K. (2013). The impact of cooperative learning on student engagement: Results from an intervention. Active Learning in Higher Education, Vol. 14 (3). https://doi.org/10.1177/1469787413498035 Mundt, E., & Hänze, M. (2022). Course characteristics influencing students’ oral participation in higher education. Learning Environments Research. Vol. 26, p. 427-444. https://doi.org/10.1007/s10984-022-09437-7 Nystrand, M., Gamoran, A., & Carbonaro, W. (2001). On the Ecology of Classroom Instruction: The Case of Writing in High School English and Social Studies. In P. Tynjala, L. Mason, & K. Londa (Eds.), Writing as a Learning Tool (pp. 57- 81). Dordrecht: Kluwer Academic Publishers. http://dx.doi.org/10.1007/978-94-010-0740-5_5 Simpson, A (2016). Designing pedagogic strategies for dialogic learning in higher education. Technology, Pedagogy and Education, 25 (2), p. 135-151. https://doi.org/10.1080/1475939X.2015.1038580 Sybing, R. (2021). Examining dialogic opportunities in teacher-student interaction: An ethnographic observation of the language classroom. Learning, Culture and Social Interaction, 28, 1-12. https://doi.org/10.1016/j.lcsi.2021.100492 Šeďová, K., Šalamounová Z., Švaříček, R., Sedláček M., Majcík, M. & Navrátilová, J. (2019). Výuková komunikace. Brno: Masarykova univerzita. Wells, G., & Arauz, R. M. (2006). Dialogue in the Classroom. Journal of the Learning Sciences, 15, 379-428. http://dx.doi.org/10.1207/s15327809jls1503_3 22. Research in Higher Education
Paper Simulating Success: A Mixed-Methods Analysis of Clinical Simulations as Situated Knowledge Production in Communication Disorders Training and Education Achva Academic College, Israel Presenting Author:In the contemporary landscape of higher education, the production and distribution of professional knowledge are increasingly conditioned by what may be termed a "poly-crisis": a confluence of diminishing clinical placement opportunities and heightened trainee-to-trainer ratios. As clinical placements become scarce due to socio-economic and geographic peripheries, higher education institutions must innovate to ensure the development of "situated" professional competence. This study investigates the effectiveness of clinical simulations—a high-fidelity pedagogical tool—in fostering professional efficacy and managing the psychological dynamics of stress among students in Communication Disorders (CD). Drawing on Bandura’s (1997) theoretical framework, professional efficacy is defined as a student’s belief in their ability to master professional tasks and meet clinical challenges. Scientific knowledge in clinical fields is not merely an abstract set of rules; it is a lived, "time- and place-bound" performance. Simulations provide a "safe" yet realistic ecosystem where this knowledge is taken up by its users through cycles of experience, feedback, and reflection. This research explores whether these simulations successfully mediate the transition from academic theory to clinical readiness, particularly regarding interpersonal therapeutic skills and professional identity, and thus is relevant to all CD practitioners, regardless of their institution of learning. This investigation employed a mixed-methods design involving 64 first- and second-year CD students. Quantitative data were gathered via a customized Professional Efficacy Questionnaire (adapted from the SLP-CSEI) and self-reported stress scales administered pre- and post-simulation. The qualitative dimension utilized semi-structured interviews with 21 participants, analyzed through phenomenological content analysis to capture the "lived experience" of the simulation. Quantitative results indicated a robust and statistically significant increase in both general and simulation-specific professional efficacy. For general efficacy, mean scores rose from M=3.59 to M=3.99 (p<.001,d=0.73), demonstrating a large effect size. Interestingly, while the quantitative data showed no significant reduction in immediate stress levels, the qualitative findings revealed a more nuanced reality. Students described simulations as "emotionally charged" threshold experiences. Six core themes emerged from the qualitative analysis: the emotional response to participation, observation as a form of "embodied learning," the instructor as an affective-cognitive scaffold, navigating complex communication with families, the emergence of professional identity, and the development of empathic communication. The findings suggest that simulations serve as a "lived crucible" where theoretical knowledge merges with practice. Crucially, the study highlights that observation is not a passive act but a deeply "embodied" form of learning where students experience vicarious stress and rehearse emotional regulation. Furthermore, the role of the moderator as a "scaffold" was identified as essential for transforming raw experience into reflective insight. Despite the high economic cost—estimated at three times the cost of traditional placements—the study justifies simulations as a necessary intervention in a shifting academic ecosystem. By fostering professional identity and empathic dialogue in a controlled environment, simulations address the need for creative and caring education research that prepares professionals for a complex, fluctuating world. Methodology, Methods, Research Instruments or Sources Used The study utilized a concurrent mixed-methods design, integrating quantitative hypothesis testing with qualitative phenomenology to explore the subjective experiences of CD students. Participants: The sample comprised of 64 unique students (62 female, 2 male) aged 20–30 (M=23.5), predominantly in their first or second year of a Communication Disorders program. For the qualitative phase, 21 female students volunteered for semi-structured interviews. Procedure and Context: Simulations were embedded within the clinical practicum rather than academic coursework, focusing on seven clinical areas: case history taking, Autism, Stuttering, Special Education, Developmental Communication Disorders, and Adult Neurological Disorders. Each workshop featured a 5–7 minute interaction between a student volunteer and a professional actor portraying a family member or colleague, while the remaining students observed. Every session concluded with a formal debriefing led by a content expert and a simulation specialist. Instrumentation: 1. Professional Efficacy: A customized version of the Speech-Language Pathology Clinical Self-Efficacy Inventory (SLP-CSEI) was used, focusing on communication, collaboration, and counseling. 2. Stress Measure: A 5-point Likert scale measured self-reported stress before and after the session. 3. Qualitative Interviews: Semi-structured protocols addressed expectations, emotional experiences, and reflections on professional growth. Data Analysis: Quantitative analysis utilized Linear Mixed-Effects Models (LMM) to account for the nested structure of data (91 paired observations from 64 participants). This approach adjusted for individual differences and variations across the seven simulation types. Frequentist p-values were supplemented with Bayes Factors (BF10) to quantify the strength of evidence for observed changes. Qualitative data were processed through content analysis (Elo & Kyngäs, 2008), which involved coding meaningful segments, identifying categories related to stress and efficacy, and consolidating these into recurring themes. To ensure data quality, quantitative analysis was performed both with and without smaller subgroups (e.g., Special Education) to confirm the stability of the findings. This multimodal approach ensured that the "how" of knowledge uptake was investigated alongside the "what" of skill acquisition. Conclusions, Expected Outcomes or Findings This research confirms that clinical simulations are a highly effective, albeit resource-intensive, component of professional training in higher education. The primary pedagogical contribution is the finding that simulations significantly enhance professional efficacy by providing "mastery experiences" and "vicarious learning" opportunities early in the training sequence. While simulations do not necessarily mitigate immediate stress, they function as "productive stress" environments—a "stretch zone" that promotes learning without reaching the "panic zone". The study offers several critical insights for the "Network 22" subtheme of teaching and supervision: 1. The Scaffolding Presence: The moderator’s role is not merely evaluative but serves as an affective-cognitive scaffold, enabling students to transform vulnerability into relational competence. 2. Observation as Embodied Pedagogy: Higher education must re-evaluate the role of the observer. Observation fosters analytical distancing and empathic identification, proving to be a deeply active form of knowledge production. 3. Justifying the Ecosystem: In an economic climate where simulations cost three times more than traditional placements, their value lies in providing exposure to complex, low-frequency clinical scenarios that a standard practicum cannot guarantee. 4. Empathy as a Situated Skill: Simulations successfully bridge the gap between abstract technical knowledge and the "empathic dialogue" required in real-world professional practice. Ultimately, this study demonstrates that education research can be "creative and caring" by prioritizing psychological safety and professional identity. By situating learning within authentic emotional challenges, higher education institutions can better equip students to navigate the complexities of their future professions in a world of flux. References • Alinier, G. (2007). A typology of educationally focused medical simulation tools. Medical Teacher, 29(8), e243-e250. • Bandura, A. (Ed.). (1997). Self-efficacy in changing societies. Cambridge University Press. • Biggs, J. (2012). What the student does: teaching for enhanced learning. Higher Education Research & Development, 31, 39–55. • Bosse, T., Gerritsen, C., de Man, J., & Treur, J. (2012). Measuring Stress-Reducing Effects of Virtual Training Based on Subjective Response. In Neural Information Processing (pp. 313-320). Springer. • Carter, M. D. (2019). The effects of computer-based simulations on speech-language pathology student performance. Journal of Communication Disorders, 77, 44-55. • Decker, S., Sportsman, S., Puetz, L., & Billings, L. (2008). The evolution of simulation and its contribution to competency. The Journal of Continuing Education in Nursing, 39(2), 74-80. • Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115. • Golombick, A. Z., Zukerman, G., & Icht, M. (2024). Exploring the impact of stuttering simulation‐based learning and personality traits on clinical self‐efficacy and professional interest among speech–language pathology students. International Journal of Language & Communication Disorders, 59(6), 2737-2751. • Ignacio, J., Dolmans, D., Scherpbier, A., Rethans, J. J., Chan, S., & Liaw, S. Y. (2016). Stress and anxiety management strategies in health professions' simulation training: a review of the literature. BMJ Simulation & Technology Enhanced Learning, 2(2), 42-46. • McGaghie, W. C., Issenberg, S. B., Petrusa, E. R., & Scalese, R. J. (2010). A critical review of simulation‐based medical education research: 2003–2009. Medical Education, 44(1), 50-63. • Nagdee, N., Sebothoma, B., Madahana, M., Khoza-Shangase, K., & Moroe, N. (2022). Simulations as a mode of clinical training in healthcare professions: A scoping review to guide planning in speech-language pathology and audiology during the COVID-19 pandemic and beyond. South African Journal of Communication Disorders, 69(2), a905. • Ormerod, E., & Mitchell, C. (2024). Evaluation of a pilot to introduce simulated learning activities to support speech and language therapy students’ clinical development. International Journal of Language & Communication Disorders, 59, 369–378. • Stein, C. (2020). The effect of clinical simulation assessment on stress and anxiety measures in emergency care students. African Journal of Emergency Medicine, 10(1), 35-39. • Watters, C., Reedy, G., Ross, A., Morgan, N. J., Handslip, R., & Jaye, P. (2015). Does interprofessional simulation increase self-efficacy: a comparative study. BMJ Open, 5(1), e005472. 22. Research in Higher Education
Paper Active Learning in Engineering Higher Education: Bridging Conceptual Perspectives and Student Learning Processes Tampere University, Finland Presenting Author:In recent decades, political, practical, and scholarly interest in teaching methods that aim to activate students has grown significantly. These methods are commonly referred to as active learning (AL). This doctoral dissertation explores AL within the context of engineering higher education (EHE). AL is a complex concept. It has been used to refer to two distinct aspects: learning (including psychological and social constructs) or teaching (including instructional pedagogies, strategies, and design principles) (Drew & Mackie, 2011; Lombardi et al., 2021). Scholarly discussions on AL as teaching in HE have primarily focused on three viewpoints. First, definitions of AL as teaching emphasize its distinction from lectures and its role in promoting students’ participation (Doolittle et al., 2023; Prince, 2004). Second, AL is theoretically linked mainly to constructivism (Driessen, 2020; Prince, 2004) although humanist ideals are also evident (Misseyanni et al., 2018). These perspectives have inspired student-centered teaching principles often associated with AL (Doolittle et al., 2023). Third, there is significant interest in the outcomes of AL—both its effectiveness in achieving intended learning outcomes (Pasquinelli, 2011) and its role in fostering graduate attributes valued by employers and society (Drew & Mackie, 2011). Consequently, encouraging students to be active in learning is now almost universally accepted (Dall’Alba & Bengtsen, 2019). However, there is no shared understanding of how AL should be defined (Drew & Mackie, 2011; Watters, 2014), and many scholars do not define AL at all (Doolittle et al., 2023; Driessen et al.; 2020). Therefore, further research is needed on the conceptualization of AL. In addition to conceptual issues, the widespread adoption of AL has practical implications for students. Although previous research has shown promising evidence of AL’s effectiveness in supporting desired learning outcomes (e.g., Freeman et al., 2014), not all studies have found positive results (e.g., Kapur et al., 2022). Additionally, many findings suggest that AL is not always a pleasant experience for students, but can enhance anxiety (Cooper et al., 2018), resistance (Shekhar et al., 2020) or feelings of incompetence (Chen et al., 2023; Shekhar et al., 2020). Additionally, group work in AL introduces several emotional and social difficulties, such as social loafing and interpersonal conflicts (Borrego et al., 2013). Consequently, understanding how students experience AL as a learning process is of great importance. This dissertation shifts the focus from students’ learning outcomes to their learning processes. Student experiences of these processes are investigated through two theoretical lenses: Control value theory (CVT) of achievement emotions (Pekrun, 2006; Pekrun et al., 2023) and basisc psychological need theory (BPNT) from self-determination theory (SDT) (Ryan & Deci, 2000, 2017). CVT emphasizes students’ appraisals of control and value as antecedents of emotions that influence performance and achievement (Pekrun et al., 2023). BPNT identifies three psychological needs, autonomy, competence, and relatedness, that drive more autonomous forms of motivation (Ryan & Deci, 2017). Exploring students’ experiences of AL through these theories provides insights into how AL shapes learning processes, particularly through perceived control and autonomy and competence satisfaction. The research has two main aims. First, it seeks to provide an overview of the state of AL research in EHE. The second aim is to develop a deeper understanding of how AL shapes students’ learning processes. The research questions are: 1. How is active learning defined and rationalized in engineering higher education research? 2. How is active learning experienced by students with respect to emotions and motivation? Methodology, Methods, Research Instruments or Sources Used This dissertation comprises three studies. Study I is a critical review that investigates the conceptualization and rationalization of AL in EHE research (RQ1). Critical review enabled an extensive search of the literature and a critical evaluation of its quality (Grant & Booth, 2009). The review was conducted using a systematic search that aimed at a representative search strategy (Paré et al., 2015), matching the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021). The search was conducted in May 2018 as a database search in the university library’s search portal. The portal included hundreds of databases, such as Scopus, ERIC, Web of Science, and Academic Search Premier. Two keywords, active learning and engineering, were used. The search focused on peer-reviewed journal articles published from 2000 to 2018 and resulted in 347 articles. The abstracts were read, and articles were chosen for thorough learning if they (1) were written in English, (2) were based on empirical research, (3) were implemented in the context of EHE, and (4) reported research on the effects of an AL activity on student learning outcomes. Finally, the search resulted in 66 original empirical studies that were analyzed with a qualitative content analysis (Elo & Kyngäs, 2008). The second RQ was addressed through two qualitative studies (Studies II and III) that provided insights into students’ emotions and motivation in AL. Altogether, 42 semi-structured engineering student interviews were conducted. Study II, drawing on 26 interviews conducted in two courses in Spring 2018, examined how engineering students perceived the emotional impact of AL in their studies so far. Study III was based on 16 interviews conducted in Spring 2020 after three engineering mathematics courses that were implemented either with flipped learning (FL) (n = 9) or lecture-based principles (n = 7). This study investigated how students experience their active and responsible role in AL and its influence on emotions and perceived competence development. In Studies II and III, the interview recordings were first transcribed verbatim (by an external company) and then analyzed with the principles and steps of thematic analysis introduced by Braun and Clarke (2006). Conclusions, Expected Outcomes or Findings According to Study I, definitions of AL were often absent or inconsistent in the reviewed studies. The used definitions portrayed AL as teaching methods designed to enhance students’ behavioral, cognitive, or social activity. The rationales for AL were rarely grounded in learning theories; most were based on assumptions or recommendations. The findings of the reviewed articles associated AL with mostly positive learning outcomes, although these claims were often methodologically limited. Studies II and III showed that students’ emotions and motivation were strongly shaped by how they experienced their active and responsible role in AL. Students’ experiences of success and sharing the experience with peers benefited student emotions, whereas an active role in front of peers, facing difficulties in individual work, or encountering collaboration challenges often triggered negative emotions. Opportunities for choice in FL supported competence development by allowing students to study in preferred ways and helping them manage their study-life balance. However, excessive responsibility for learning posed challenges for perceived competence development and evoked negative emotions. This research contributes to research and practice of AL. First, it provides a clearer definition and theoretical positioning of AL to support future research. Second, it highlights the need to study AL from the viewpoint of learning processes and not solely by focusing on outcomes. Third, the findings inform HE teachers’ pedagogical decisions by providing them with a practical, theoretical, and methodological understanding of AL. Finally, the dissertation offers strategies for supporting students’ emotions and motivation in AL. This dissertation challenges the taken-for-granted status of AL, which has shaped the research and practice of AL. The findings call for a recognition of the premises of AL, as well as greater attention to the learning processes within it. Consequently, AL could be studied and implemented in a more rigorous and well-grounded manner. References Dall’Alba, G., & Bengtsen, S. (2019). Re-imagining active learning: Delving into darkness. Educational Philosophy and Theory, 51(14), 1477–1489. https://doi.org/10.1080/00131857.2018.1561367 Doolittle, P., Wojdak, K., & Walters, A. (2023). Defining active learning: A restricted systematic review. Teaching and Learning Inquiry, 11. https://doi.org/10.20343/teachlearninqu.11.25 Drew, V., & Mackie, L. (2011). Extending the constructs of active learning: Implications for teachers’ pedagogy and practice. The Curriculum Journal, 22(4), 451–467. https://doi.org/10.1080/09585176.2011.627204 Driessen, E. P., Knight, J. K., Smith, M. K., & Ballen, C. J. (2020). Demystifying the meaning of active learning in postsecondary biology education. CBE – Life Sciences Education, 19(4), ar52. https://doi.org/10.1187/cbe.20-04-0068 Kapur, M., Hattie, J., Grossman, I., & Sinha, T. (2022). Fail, flip, fix, and feed – Rethinking flipped learning: A review of meta-analyses and a subsequent meta-analysis. Frontiers in Education, 7, 956416. https://doi.org/10.3389/feduc.2022.956416 Lombardi, D., Shipley, T. F., Astronomy Team, Biology Team, Chemistry Team, Engineering Team, Geography Team, Geoscience Team, & Physics Team. (2021). The curious construct of active learning. Psychological Science in the Public Interest, 22(1), 8–43. https://doi.org/10.1177/1529100620973974 Pasquinelli, E. (2011). Knowledge- and evidence-based education: Reasons, trends, and contents. Mind, Brain, and Education, 5(4), 186–195. https://doi.org/10.1111/j.1751-228X.2011.01128.x Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9 Pekrun, R., Marsh, H. W., Elliot, A. J., Stockinger, K., Perry, R. P., Vogl, E., Goetz, T., van Tilburg, W. A. P., Lüdtke, O., & Vispoel, W. P. (2023). A three-dimensional taxonomy of achievement emotions. Journal of Personality and Social Psychology, 124(1), 145–178. https://doi.org/10.1037/pspp0000448 Prince, M. J. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231. https://doi.org/10.1002/j.2168-9830.2004.tb00809.x Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. The American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68 Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press. Shekhar, P., Borrego, M., DeMonbrun, M., Finelli, C., Crockett, C., & Nguyen, K. (2020). Negative student response to active learning in STEM classrooms: A systematic review of underlying reasons. Journal of College Science Teaching, 49(6), 45–54. https://doi.org/10.1080/0047231x.2020.12290664 | ||