Exploring the Cognitive and Affective Reasons of Students' Behaviours While Watching Videos in a Blended Learning Environment

Yıl 2025, Cilt: 23 Sayı: 2, 2034 - 2074, 31.08.2025

Burçak Gönül Aydın , Gökhan Akçapınar , Vildan Özeke

https://doi.org/10.37217/tebd.1620258

Öz

This study aims to investigate students’ perspectives on the underlying cognitive and affective reasons behind their behaviors while interacting with screen-recorded instructional videos, within the context of a physical programming course conducted using a blended learning approach. For this purpose, data on students’ video-watching processes were collected over one semester using a one-time open-ended questionnaire at the end of the term. A total of 34 university students participated in the study. The data obtained were analysed through content analysis, revealing the cognitive and affective reasons behind students’ video interactions. The findings are expected to contribute to the understanding of the relationships between the cognitive and affective reasons underlying students' video interactions. The study highlighted that both behavioral and affective factors should be taken into consideration when designing videos for educational purposes. By providing insights into the connections between video interactions and emotional responses, this research contributes to improving the design of educational video materials and offers implications for enhancing video-based learning experiences.

Anahtar Kelimeler

Video-based learning , Educational videos , Video interactions , Emotions

Harmanlanmış Öğrenme Ortamında Öğrencilerin Video İzleme Davranışlarının Bilişsel ve Duyuşsal Nedenlerinin Araştırılması

Öz

Bu çalışmada, harmanlanmış öğrenme yaklaşımına göre yürütülen fiziksel programlama dersi kapsamında, öğrencilerin ekran kaydı türündeki ders anlatım videolarıyla etkileşimlerinin altında yatan bilişsel ve duyuşsal nedenlere ilişkin görüşlerinin belirlenmesi amaçlanmıştır. Bu amaçla, öğrencilerin ders kapsamında izledikleri videolarla etkileşimlerine ilişkin veriler, dönem sonunda açık uçlu sorular kullanılarak toplanmıştır. Çalışmaya 34 üniversite öğrencisi katılmıştır. Elde edilen veriler, içerik analizi yöntemiyle analiz edilmiş ve öğrencilerin video etkileşimlerinin altında yatan bilişsel ve duyuşsal nedenler ortaya konulmuştur. Bulguların, öğrencilerin video etkileşimlerinin altında yatan bilişsel ve duyuşsal nedenler arasındaki ilişkilerin anlaşılmasına katkı sağlaması beklenmektedir. Çalışma, eğitim amaçlı videolar tasarlanırken hem davranışsal hem de duyuşsal faktörlerin göz önünde bulundurulması gerektiğini vurgulamaktadır. Video etkileşimleriyle duyuşsal tepkiler arasındaki bağlantılara ilişkin öngörüler sunan bu araştırma, eğitsel video tasarımını iyileştirmeye katkıda bulunmakta ve öğrencilerin video tabanlı öğrenme deneyimlerini geliştirmek için önemli çıkarımlar sağlamaktadır.

Anahtar Kelimeler

Video tabanlı öğrenme , Eğitsel videolar , Video etkileşimleri , Duygular

Kaynakça

• Akçapınar, G. & Bayazıt, A. (2018). Investigating video viewing behaviors of students with different learning approaches using video analytics. The Turkish Online Journal of Distance Education, 19(4), 116–125. https://doi.org/10.17718/tojde.471907
• Andres, J. M. A. L., Ocumpaugh, J., Baker, R. S., Slater, S., Paquette, L., Jiang, Y., …, & Biswas, G. (2019). Affect sequences and learning in Betty’s brain. Proceedings of the 9th International Conference on Learning Analytics & Knowledge içinde (s. 383–390). ACM International Conference Proceeding Series. https://doi.org/10.1145/3303772.3303785
• APA. (2025). Frustration. Dictionary of psychology (5. b.) içinde (s. 459-460). American Psychological Association.
• Artino Jr, A. R. & McCoach, D. B. (2008). Development and initial validation of the online learning value and self-efficacy scale. Journal of Educational Computing Research, 38(3), 279-303. https://doi.org/10.2190/EC.38.3.c
• Artino, A. R. (2012). Emotions in online learning environments: Introduction to the special issue. The Internet and Higher Education, 15(3), 137-140. https://doi.org/10.1016/j.iheduc.2012.04.001
• Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), 369–386. https://doi.org/10.1002/pits.20303
• Aydın, A., Araz, A., & Asan, A. (2011). Görsel analog ölçeği ve duygu kafesi: Kültürümüze uyarlama çalışması. Türk Psikoloji Yazıları, 14(27), 1-13. https://www.tpd.com.tr/tr/yayinlar/dergiler/1031828/tpy1301996120110000m000115.pdf sayfasından erişilmiştir.
• Aytaçlı, B. (2012). Durum çalışmasına ayrıntılı bir bakış. Adnan Menderes Üniversitesi Eğitim Fakültesi Eğitim Bilimleri Dergisi, 3(1), 1-9. https://dergipark.org.tr/tr/download/article-file/399478 sayfasından erişilmiştir.
• Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003
• Baker, S., Field, C., Saintilan, N., & Lee, J. S. (2021). Supporting students’ academic literacies in post-covid-19 times: Developing digital videos to develop students’ critical academic reading practices. Journal of University Teaching and Learning Practice, 18(4), 35–49. https://doi.org/10.53761/1.18.4.5
• Bayazıt, A. & Akçapınar, G. (2018). Çevrim içi dersler için video analitik aracının tasarlanması ve geliştirilmesi. İlköğretim Online, 17(1), 14-25. https://doi.org/10.17051/ilkonline.2018.413719
• Brame, C. J. (2017). Effective educational videos: Principles and guidelines for maximizing student learning from video content. CBE—Life Sciences Education, 15(4), es6. https://doi.org/10.1187/cbe.16-03-0125
• Brecht, H. D. & Ogilby, S. M. (2008). Enabling a comprehensive teaching strategy: Video lectures. Journal of Information Technology Education, 7, 71e86. https://doi.org/10.28945/207
• Castillo, S., Calvitti, K., Shoup, J., Rice, M., Lubbock, H., & Oliver, K. H. (2021). Production processes for creating educational videos. CBE-Life Sciences Education, 20(2), es7. https://doi.org/10.1187/cbe.20-06-0120
• Chen, D. & Caropreso, E. J. (2004). Influence of personality on online discussion. Journal of Interactive Online Learning, 3(2), 1-19. https://www.ncolr.org/jiol/issues/pdf/3.2.2.pdf sayfasından erişilmiştir.
• Chen, C. M. & Wu, C. H. (2015). Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance. Computers & Education, 80, 108-121. https://doi.org/10.1016/j.compedu.2014.08.015
• Chen, M., Liu, Y., Yang, H. H., Li, Y., & Zhou, C. (2023). Investigating teachers’ participation patterns in online teacher professional development: what is the relationship between participation frequency and participation quality? Education and Information Technologies, 28(11), 15011-15030. https://doi.org/10.1007/s10639-023-11829-y
• Chew, R., Bollenbacher, J., Wenger, M., Speer, J., & Kim, A. (2023). LLM-assisted content analysis: Using large language models to support deductive coding. arXiv Preprint. https://doi.org/10.48550/arXiv.2306.14924
• Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145-182. https://doi.org/10.1207/s15516709cog1302_1
• Clark, R. C. & Mayer, R. E. (2023). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons.
• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. https://doi.org/10.1177/001316446002000104
• Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004). Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media, 29(3), 241-250. https://doi.org/10.1080/1358165042000283101
• Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five approaches (2. b.). Sage.
• Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
• Daschmann, E. C., Goetz, T., & Stupnisky, R. H. (2011). Testing the predictors of boredom at school: Development and validation of the precursors to boredom scales. British Journal of Educational Psychology, 81, 421–440. https://doi.org/10.1348/000709910X526038
• Dillon, J., Bosch, N., Chetlur, M., Wanigasekara, N., Ambrose, G. A., Sengupta, B., & D'Mello, S. K. (2016). Student emotion, co-occurrence, and dropout in a MOOC context. Proceedings of the 9th International Educational Data Mining Society içinde (s. 570–575). International Educational Data Mining Society. https://files.eric.ed.gov/fulltext/ED592723.pdf sayfasından erişilmiştir.
• Ding, Y. & Zhao, T. (2020). Emotions, engagement, and self-perceived achievement in a small private online course. Journal of Computer Assisted Learning, 36(4), 449–457. https://doi.org/10.1111/jcal.12410
• D'Mello, S., Picard, R. W., & Graesser, A. (2007). Toward an affect-sensitive AutoTutor. IEEE Intelligent Systems, 22(4), 53-61. https://doi.org/10.1109/mis.2007.79
• D’Mello, S. K. & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145-157. https://doi.org/10.1016/j.learninstruc.2011.10.001
• D’Mello, S. K., Lehman, B., & Graesser, A. (2011). A motivationally supportive affect-sensitive AutoTutor. R. Calvo, & S. D'Mello (Ed.), New perspectives on affect and learning technologies içinde (s. 113-126). Springer. https://doi.org/10.1007/978-1-4419-9625-1_9
• D’Mello, S. K. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology, 105(4), 1082-1099. https://doi.org/10.1037/a0032674
• D’Mello, S. K. & Graesser, A. C. (2014). Confusion. R. Pekrun & L. Linnenbrink-Garcia (Ed.), The international handbook of emotions in education içinde (s. 299-320). Routledge.
• D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153-170.
• D’Mello, S. K. (2018). What do we think about when we learn? K. Millis, D. L. Long, J. P. Magliano, & K. Wiemer (Ed.), Deep comprehension içinde (s. 52-67). Routledge.
• Dolan, J. (2015). The effectiveness of video-based instruction. Journal of Educational Psychology, 107(4), 852-863. https://doi.org/10.1037/edu0000023
• Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4-58. https://doi.org/10.1177/1529100612453266
• Eastwood, J. D., Frischen, A., Fenske, M. J., & Smilek, D. (2012). The unengaged mind: Defining boredom in terms of attention. Perspectives on Psychological Science, 7(5), 482-495. https://doi.org/10.1177/1745691612447303
• Feidakis, M., Daradoumis, T., CaballÃ, S., & Conesa, J. (2014). Embedding emotion awareness into e-learning environments. International Journal of Emerging Technologies in Learning, 9(7), 39-46. https://doi.org/10.18608/hla17.023
• Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059
• Furlong, M. J., Whipple, A. D., St. Jean, G., Simental, J., Soliz, A., & Punthuna, S. (2003). Multiple contexts of school engagement: Moving toward a unifying framework for educational research and practice. The California School Psychologist, 8, 99-113. https://casponline.org/pdfs/pdfs/journal03.pdf#page=101 sayfasından erişilmiştir.
• Garrison, D. R. & Vaughan, N. D. (2008). Blended learning in higher education: Framework, principles, and guidelines. John Wiley & Sons.
• Giannakos, M. N. (2013). Exploring the video‐based learning research: A review of the literature. British Journal of Educational Technology, 44(6), E191-E195. https://doi.org/10.1111/bjet.12070
• Giannakos, M. N., Chorianopoulos, K., Ronchetti, M., Szegedi, P., & Teasley, S. D. (2014). Video-based learning and open online courses. International Journal of Emerging Technologies in Learning (IJET), 9(1),4-7. https://doi.org/10.3991/ijet.v9i1.3354
• Goggins, S. & Xing, W. (2016). Building models explaining student participation behavior in asynchronous online discussion. Computers & Education, 94, 241–251. https://doi.org/10.1016/j.compedu.2015.11.002
• Graesser, A. C., Rus, V., D’Mello, S., & Jackson, G. T. (2008). AutoTutor: Learning through natural language dialogue that adapts to the cognitive and affective states of the learner. D. H. Robinson & G. Schraw (Ed.), Current perspectives on cognition, learning, and instruction: Recent innovations in educational technology that facilitate student learning içinde (s. 95–125). Information Age.
• Graham, C. R. (2006). Blended learning systems. The handbook of blended learning: Global perspectives, local designs içinde (s. 3-21).
• Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: An empirical study of MOOC videos. M. Sahami (Chair), L@S 2014: First (2014) ACM Conference on Learning @ Scale Conference içinde (s. 41–50). Association for Computing Machinery. https://doi.org/10.1145/2556325.2566239 Hajhashemi, K., Caltabiano, N., & Anderson, N. (2017). Net-geners’ perceptions of engagement through online videos. Journal of Computers in Education, 4(3), 321–337. https://doi.org/10.1007/s40692-017-0084-8
• Harley, J. M., Lajoie, S. P., Frasson, C., & Hall, N. C. (2017). Developing emotion-aware, advanced learning technologies: A taxonomy of approaches and features. International Journal of Artificial Intelligence in Education, 27, 268-297. https://doi.org/10.1007/s40593-016-0126-8
• Hatch, T., Shuttleworth, J., Jaffee, A. T., & Marri, A. (2016). Videos, pairs, and peers: What connects theory and practice in teacher education? Teaching and Teacher Education, 59, 274-284. https://doi.org/10.1016/j.tate.2016.04.011
• He, J., Bailey, J., Rubinstein, B., & Zhang, R. (2015). Identifying at-risk students in massive open online courses. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1), 1749-1755. Association for Computing Machinery. https://doi.org/10.1609/aaai.v29i1.9471
• Heflin, H., Shewmaker, J., & Nguyen, J. (2017). Impact of mobile technology on student attitudes, engagement, and learning. Computers & Education, 107, 91–99. https://doi.org/10.1016/j.compedu.2017.01.006
• Ho, C. M., Yeh, C. C., Wang, J. Y., Hu, R. H., & Lee, P. H. (2021). Pre-class online video learning and class style expectation: Patterns, association, and precision medical education. Annals of Medicine, 53(1), 1390-1401. https://doi.org/10.1080/07853890.2021.1967441
• Hsieh, H. F. & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288. https://doi.org/10.1177/1049732305276687
• Hughes, C., Costley, J., & Lange, C. (2019). The effects of multimedia video lectures on extraneous load. Distance Education, 40(1), 54–75. https://doi.org/10.1080/01587919.2018.1553559
• Hutt, S., DePiro, A., Wang, J., Rhodes, S., Baker, R. S., Hieb, G., Sethuraman, S., Ocumpaugh, J., & Mills, C. (2024). Feedback on feedback: Comparing classic natural language processing and generative AI to evaluate peer feedback. B. Flanagan, B. Wasson, & D. Gašević (Ed.), LAK ’24: Proceedings of the 14th Learning Analytics & Knowledge Conference içinde (s. 55–65). ACM. https://doi.org/10.1145/3636555.3636850
• Jimerson, S. R., Campos, E., & Greif, J. L. (2003). Toward an understanding of definitions and measures of school engagement and related terms. The California School Psychologist, 8, 7-27. https://doi.org/10.1007/BF03340893
• Johnson, A. M., Jacovina, M. E., Russell, D. G., & Soto, C. M. (2016). Challenges and solutions when using technologies in the classroom. S. A. Crossley & D. S. McNamara (Ed.), Adaptive educational technologies for literacy instruction içinde (s. 13-30). Routledge. https://doi.org/10.4324/9781315647500-2
• Jung, Y. & Lee, J. (2018). Learning engagement and persistence in massive open online courses (MOOCS). Computers & Education, 122, 9–22. https://doi.org/10.1016/j.compedu.2018.02.013
• Kalyuga, S., Chandler, P., & Sweller, J. (1999). Managing split‐attention and redundancy in multimedia instruction. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition, 13(4), 351-371. https://doi.org/10.1002/(SICI)1099-0720(199908)13:4<351::AID-ACP589>3.0.CO;2-6
• Kalyuga, S. (2007). Enhancing instructional efficiency of interactive e-learning environments: A cognitive load perspective. Educational Psychology Review, 19(3), 387-399. https://doi.org/10.1007/s10648-007-9051-6
• Kapoor, A., Burleson, B., & Picard, R. (2007). Automatic prediction of frustration. International Journal of Human-Computer Studies, 65, 724–736. https://doi.org/10.1016/j.ijhcs.2007.02.003
• Karumbaiah, S., Baker, R., Tao, Y., & Liu, Z. (2022). How does students’ affect in virtual learning relate to their outcomes? A systematic review challenging the positive- negative dichotomy. LAK22: 12th International Learning Analytics and Knowledge Conference içinde (s. 24–33). Association for Computing Machinery. https://doi.org/10.1145/3506860.3506861
• Kay, R. H. & Kletskin, I. (2012). Evaluating the use of problem-based video podcasts to teach mathematics in higher education. Computers & Education, 59(2), 619-627. https://doi.org/10.1016/j.compedu.2012.03.007
• Khalil, M. & Ebner, M. (2017). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): The use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29, 114-132. https://doi.org/10.1007/s12528-016-9126-9
• Khalil, M., Topali, P., Ortega-Arranz, A., Er, E., Akçapınar, G., & Belokrys, G. (2023). Video analytics in digital learning environments: Exploring student behaviour across different learning contexts. Technology, Knowledge and Learning, 29(4), 1877-1905. https://doi.org/10.1007/s10758-022-09592-1
• Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., & Miller, R. C. (2014). Understanding in-video dropouts and interaction peaks in online lecture videos. Proceedings of the first ACM Conference on Learning @ Scale Conference içinde (s. 31-40). Association for Computing Machinery. https://doi.org/10.1145/2556325.2566237 Kim, D., Jo, I. H., Song, D., Zheng, H., Li, J., Zhu, J., ..., & Xu, Z. (2021). Self-regulated learning strategies and student video engagement trajectory in a video-based asynchronous online course: A Bayesian latent growth modeling approach. Asia Pacific Education Review, 22(2), 305-317. https://doi.org/10.1007/s12564-021-09690-0
• Kintsch, W. (1998). Comprehension: A paradigm for cognition. Cambridge University.
• Kirsten, E., Buckmann, A., Mhaidli, A., & Becker, S. (2024). Decoding complexity: Exploring human–AI concordance in qualitative coding. arXiv Preprint. https://doi.org/10.48550/arXiv.2403.06607
• Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage.
• Landis, J. R. & Koch, G. G. (1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 33(2), 363-374. https://doi.org/10.2307/2529786
• Lee, J., So, H. J., Ha, S., Kim, E., & Park, K. (2021). Unpacking academic emotions in asynchronous video-based learning: focusing on Korean learners’ affective experiences. The Asia-Pacific Education Researcher, 30, 247-261. https://doi.org/10.1007/s40299-021-00565-x
• Leite, L., Roy, S. W., Chakraborty, N., Michailidis, G., Huggins-Manley, A. C., D'Mello, S., ..., & Jing, Z. (2022). A novel video recommendation system for algebra: An effectiveness evaluation study. LAK22: Proceeding of the 12th International Learning Analytics and Knowledge Conference içinde (s. 294-303). Association for Computing Machinery. https://doi.org/10.1145/3506860.3506861
• Liu, Z., Baker, R. S. J. D., Pataranutaporn, V., & Ocumpaugh, J. (2013). Sequences of frustration and confusion, and learning. Proceedings of the 6th International Conference on Educational Data Mining içinde (s. 114-120). https://doi.org/10.1145/3303772.3303785
• Liu, X., Zambrano, A. F., Baker, R. S., Barany, A., Ocumpaugh, J., Zhang, J., ..., & Wei, Z. (2025). Qualitative coding with GPT-4: Where it works better. Journal of Learning Analytics, 12(1), 169-185. https://doi.org/10.18608/jla.2025.8575
• Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 70, 101162. https://doi.org/10.1016/j.learninstruc.2018.08.002
• Love, B., Hodge, A., Grandgenett, N., & Swift, A. W. (2014). Student learning and perceptions in a flipped linear algebra course. International Journal of Mathematical Education in Science and Technology, 45(3), 317–324. https://doi.org/10.1080/0020739X.2013.822582
• Maloney, S., Axelsen, M., Galligan, L., Turner, J., Redmond, P., Brown, A., ..., & Lawrence, J. (2022). Using LMS log data to explore student engagement with coursework videos. Online Learning, 26(4), 399-423. https://doi.org/10.24059/olj.v26i4.2998
• Marton, F. & Säljö, R. (1976). On qualitative differences in learning: I—Outcome and process. British Journal of Educational Psychology, 46(1), 4-11. https://doi.org/10.1111/j.2044-8279.1976.tb02980.x Mayer, R. E. & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43-52. https://doi.org/10.1207/S15326985EP3801_6
• Mayer, R. E. (2008). Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American Psychologist, 63(8), 760-769. https://doi.org/10.1037/0003-066X.63.8.760
• Mayer, R. E. (2009). Multimedia learning (2. b.). Cambridge University.
• Mayer, R. E. (2014). Incorporating motivation into multimedia learning. Learning and Instruction, 29, 171-173. https://doi.org/10.1016/j.learninstruc.2013.04.003
• MAXQDA. (2025). Implementing AI in qualitative research software. MAXQDA Blog. https://www.maxqda.com/blogpost/implementing-ai-in-qualitative-research-software sayfasından erişilmiştir.
• Maxwell, J. A. (2013). Qualitative research design: An interactive approach: An interactive approach. Sage.
• Merkt, M., Hoppe, A., Bruns, G., Ewerth, R., & Huff, M. (2022). Pushing the button: Why do learners pause online videos? Computers & Education, 176, Article 104355. https://doi.org/10.1016/j.compedu.2021.104355
• Merkt, M. & Bodemer, D. (2024). Learning with videos: Do task instructions and the availability of a pause button matter? Journal of Computer Assisted Learning, 40(6), 2856-2871. https://doi.org/10.1111/jcal.13044 Merriam, S. B. & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.
• Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43-59. https://doi.org/10.1007/BF02505024
• Miles, M. B. & Huberman, A. M. (1984). Qualitative data analysis. Sage.
• Morgan, D. L. (2023). Exploring the use of artificial intelligence for qualitative data analysis: The case of ChatGPT. International Journal of Qualitative Methods, 22, 16094069231211248. https://doi.org/10.1177/16094069231211248
• Mueller, P. A. & Oppenheimer, D. M. (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological Science, 25(6), 1159-1168. https://doi.org/10.1177/0956797614524581
• Noetel, M., Griffith, S., Delaney, O., Sanders, T., Parker, P., del Pozo Cruz, B., & Lonsdale, C. (2021). Video improves learning in higher education: A systematic review. Review of Educational Research, 91(2), 204-236. https://doi.org/10.3102/0034654321990713
• Öztüre, G., Fidan, A., Bakir, E., Uslu, N. A., & Usluel, Y. (2021). A systematic mapping study on technology and emotion studies in educational context: Definitions, theories, future directions. Educational Technology Theory and Practice, 11(1), 20-47. https://doi.org/10.17943/etku.745236
• Öztüre-Yavuz, G., Akçapınar, G., Çıralı-Sarıca, H., & Koçak-Usluel, Y. (2024). Investigating features that play a role in predicting gifted student engagement using machine learning: Video log and self-report data. Education and Information Technologies, 29(13), 1-27. https://doi.org/10.1007/s10639-024-12490-9
• Parrott, W. G. (2001). Emotions in social psychology: Essential readings. Psychology.
• Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students' self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91-105. https://doi.org/10.1207/S15326985EP3702_4
• Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315-341. https://doi.org/10.1007/s10648-006-9029-9
• Pekrun, R., Goetz, T., Daniels, L. M., Stupnisky, R. H., & Perry, R. P. (2010). Boredom in achievement settings: Exploring control-value antecedents and performance outcomes of a neglected emotion. Journal of Educational Psychology, 102, 531–549. https://doi.org/10.1037/a0019243
• Pekrun, R. & Stephens, E. J. (2012). Academic emotions. K. R. Harris, S. Graham, T. Urdan, S. Graham, J. M. Royer, & M. Zeidner (Ed.), APA educational psychology handbook, Vol 2: Individual differences and cultural and contextual factors içinde (s. 3-31). American Psychological Association. https://doi.org/10.1037/13274-001
• Phan, T., McNeil, S. G., & Robin, B. R. (2016). Students’ patterns of engagement and course performance in a massive open online course. Computers & Education, 95, 36–44. https://doi.org/10.1016/j.compedu.2015.11.015
• Polit, D. F. & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489-497. https://doi.org/10.1002/nur.20147
• Ploetzner, R. (2024). The effectiveness of enhanced interaction features in educational videos: a meta-analysis. Interactive Learning Environments, 32(5), 1597-1612. https://doi.org/10.1080/10494820.2022.2123002
• Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714
• Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172. https://doi.org/10.1037/0033-295X.110.1.145
• Sablić, M., Mirosavljević, A., & Škugor, A. (2021). Video-based learning (VBL)—past, present and future: An overview of the research published from 2008 to 2019. Technology, Knowledge and Learning, 26(4), 1061-1077. https://doi.org/10.1007/s10758-020-09455-5
• Saldaña, J. (2015). The coding manual for qualitative researchers. Sage.
• Santagata, R. & Taylor, K. (2018). Novice teachers’ use of student thinking and learning as evidence of teaching effectiveness: A longitudinal study of video-enhanced teacher preparation. Contemporary Issues in Technology and Teacher Education, 18(1), 11-28.
• Schwan, S. & Riempp, R. (2004). The cognitive benefits of interactive videos: Learning to tie nautical knots. Learning and Instruction, 14(3), 293-305. https://doi.org/10.1016/j.learninstruc.2004.06.005
• Schnotz, W. & Rasch, T. (2005). Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning. Educational Technology Research and Development, 53(3), 47–58. https://doi.org/10.1007/BF02504797
• Schroeder, N. L. & Adesope, O. O. (2014). A systematic review of pedagogical agents’ persona, motivation, and cognitive load implications for learners. Journal of Research on Technology in Education, 46(3), 229-251. https://doi.org/10.1080/15391523.2014.888272
• Seo, K., Dodson, S., Harandi, N. M., Roberson, N., Fels, S., & Roll, I. (2021). Active learning with online video: The impact of learning context on engagement. Computers & Education, 165, 104132. https://doi.org/10.1016/j.compedu.2021.104132
• Shorter, J. & Dean, R. (1994). Computing in collegiate schools of business: are mainframes & stand-alone microcomputers still good enough? Journal of Systems Management, 45(7), 36e41. https://doi.org/10.1080/07399019408960952
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4
• Tai, R. H., Bentley, L. R., Xia, X., Sitt, J. M., Fankhauser, S. C., Chicas-Mosier, A. M., & Monteith, B. G. (2024). An examination of the use of large language models to aid analysis of textual data. International Journal of Qualitative Methods, 23, 1-14. https://doi.org/10.1177/16094069241231168
• Tseng, S. S. (2021). The influence of teacher annotations on student learning engagement and video watching behaviors. International Journal of Educational Technology in Higher Education, 18(1), 1-17. https://doi.org/10.1186/s41239-021-00242-5
• Xiao, Z., Yuan, X., Liao, Q. V., Abdelghani, R., & Oudeyer, P.-Y. (2023). Supporting qualitative analysis with large language models: Combining codebook with GPT-3 for deductive coding. F. Chen, M. Billinghurst, M. Zhou, & S. Berkovsky (Ed.), IUI ’23 Companion: Companion Proceedings of the 28th International Conference on Intelligent User Interfaces içinde (s. 75–78). ACM. https://doi.org/10.1145/3581754.3584136
• Uysal, Ö. (2016). Harmanlanmış öğrenme ortamında proje tabanlı öğrenmenin gerçekleştirilmesi. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 2(2), 89-113.
• Vytasek, J. M., Patzak, A., & Winne, P. H. (2020). Analytics for student engagement. Machine learning paradigms: Advances in learning analytics içinde (s. 23-48). https://doi.org/10.1007/978-3-030-13743-4_3
• Van Meter, P., Yokoi, L., & Pressley, M. (1994). College students' theory of note-taking derived from their perceptions of note-taking. Journal of Educational Psychology, 86(3), 323-338. https://doi.org/10.1037/0022-0663.86.3.323
• Van Teijlingen, E. & Hundley, V. (2001). The importance of pilot studies. Social Research Update(35), 1-4. http://sru.soc.surrey.ac.uk/SRU35.pdf sayfasından erişilmiştir.
• Wang, F., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educational technology research and development, 53(4), 5-23. doi: 10.1007/BF02504682
• Wang, J. Y., Yang, C. H., Liao, W. C., Yang, K. C., Chang, I. W., Sheu, B. C., & Ni, Y. H. (2022). Highly engaged video-watching pattern in asynchronous online pharmacology course in pre-clinical 4th-year medical students was associated with a good self-expectation, understanding, and performance. Frontiers in Medicine, 8, 799412. https://doi.org/10.3389/fmed.2021.799412
• Wachinger, J., Bärnighausen, K., Schäfer, L. N., Scott, K., & McMahon, S. A. (2024). Prompts, pearls, imperfections: Comparing ChatGPT and a human researcher in qualitative data analysis. Qualitative Health Research, 35(9), 1-16. https://doi.org/10.1177/10497323241244669
• Yıldırım, A. & Şimşek, H. (2018). Sosyal bilimlerde nitel araştırma yöntemleri (11. b.). Seçkin.
• Yin, R. K. (2009). Case study research: Design and methods (5. b.). Sage.
• Yoon, M., Lee, J., & Jo, I.-H. (2021). Video learning analytics: Investigating behavioral patterns and learner clusters in video-based online learning. The Internet and Higher Education, 50, Article 100806. https://doi.org/10.1016/j.iheduc.2021.100806
• Yu, Z. & Gao, M. (2022). Effects of video length on a flipped English classroom. Sage Open, 12(1), Article 21582440211068474. https://doi.org/10.1177/21582440211068474
• Yürüm, O. R., Yıldırım, S., & Taşkaya-Temizel, T. (2023). An intervention framework for developing interactive video lectures based on video clickstream behavior: A quasi-experimental evaluation. Interactive Learning Environments, 31(10), 6611-6626. https://doi.org/10.1007/s10758-023-09697-z
• Zambrano, A. F., Baker, R. S., Baral, S., Heffernan, N. T., & Lan, A. (2024). From reaction to anticipation: Predicting future affect. Proceedings of the 17th International Conference on Educational Data Mining içinde (s. 566-574). https://doi.org/10.5281/zenodo.12729885
• Zambrano, A. F., Pankiewicz, M., Barany, A., & Baker, R. S. (2024). Ordered network analysis in CS education: Unveiling patterns of success and struggle in automated programming assessment. M. Monga, V. Lonati, E. Barendsen, J. Sheard, J. Patterson, & L. Barker (Ed.), ITiCSE 2024: Proceedings of the 2024 Conference on Innovation and Technology in Computer Science Education (c. 1) içinde (s. 443–449). ACM. https://doi.org/10.1145/3649217.3653613
• Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker Jr, J. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & Management, 43(1), 15-27. https://doi.org/10.1016/j.im.2005.01.004
• Zhang, J., Huang, Y., & Gao, M. (2022). Video features, engagement, and patterns of collective attention allocation: An open flow network perspective. Journal of Learning Analytics, 9(1), 32-52. https://doi.org/10.18608/jla.2022.7421
• Zheng, G., Zhang, Q., & Ran, G. (2023). The association between academic stress and test anxiety in college students: The mediating role of regulatory emotional self-efficacy and the moderating role of parental expectations. Frontiers in Psychology, 14, 1008679. https://doi.org/10.3389/fpsyg.2023.1008679
• Zhu, J., Yuan, H., Zhang, Q., Huang, P. H., Wang, Y., Duan, S., ..., & Song, P. (2022). The impact of short videos on student performance in an online-flipped college engineering course. Humanities and Social Sciences Communications, 9(1), 1-10. https://doi.org/10.1057/s41599-022-01355-6
• Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3-17. https://doi.org/10.1207/s15326985ep2501_2
• Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. M. Boekaerts, P. R. Pintrich, & M. Zeidner (Ed.), Handbook of self-regulation içinde (s. 13-39). Academic. https://doi.org/10.1016/B978-012109890-2/50031-7