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            <front>

                <journal-meta>
                                                                <journal-id>ettp</journal-id>
            <journal-title-group>
                                                                                    <journal-title>Eğitim Teknolojisi Kuram ve Uygulama</journal-title>
            </journal-title-group>
                            <issn pub-type="ppub">2147-1908</issn>
                                                                                                        <publisher>
                    <publisher-name>Tolga GÜYER</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.17943/etku.1367188</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Educational Technology and Computing</subject>
                                                            <subject>Learning Analytics</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>Eğitim Teknolojisi ve Bilgi İşlem</subject>
                                                            <subject>Öğrenme Analitiği</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                        <article-title>IDENTIFYING BEHAVIORAL PATTERNS IN MOOC VIDEO ENGAGEMENT USING CLUSTERING APPROACH</article-title>
                                                                                                                                                                                                <trans-title-group xml:lang="tr">
                                    <trans-title>MOOC VİDEO ETKİLEŞİMİNDEKİ DAVRANIŞ ÖRÜNTÜLERİNİN KÜMELEME YAKLAŞIMI İLE BELİRLENMESİ</trans-title>
                                </trans-title-group>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0002-9624-4055</contrib-id>
                                                                <name>
                                    <surname>Er</surname>
                                    <given-names>Erkan</given-names>
                                </name>
                                                                    <aff>ORTA DOĞU TEKNİK ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ, BİLGİSAYAR VE ÖĞRETİM TEKNOLOJİLERİ EĞİTİMİ BÖLÜMÜ</aff>
                                                            </contrib>
                                                    <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0002-0742-1612</contrib-id>
                                                                <name>
                                    <surname>Akçapınar</surname>
                                    <given-names>Gökhan</given-names>
                                </name>
                                                                    <aff>HACETTEPE ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ, BİLGİSAYAR VE ÖĞRETİM TEKNOLOJİLERİ EĞİTİMİ BÖLÜMÜ, BİLGİSAYAR VE ÖĞRETİM TEKNOLOJİLERİ EĞİTİMİ ANABİLİM DALI</aff>
                                                            </contrib>
                                                    <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0002-0140-0837</contrib-id>
                                                                <name>
                                    <surname>Sökücü</surname>
                                    <given-names>Gamze</given-names>
                                </name>
                                                                    <aff>ORTA DOĞU TEKNİK ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ, BİLGİSAYAR VE ÖĞRETİM TEKNOLOJİLERİ EĞİTİMİ BÖLÜMÜ</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20240726">
                    <day>07</day>
                    <month>26</month>
                    <year>2024</year>
                </pub-date>
                                        <volume>14</volume>
                                        <issue>2</issue>
                                        <fpage>198</fpage>
                                        <lpage>217</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20230928">
                        <day>09</day>
                        <month>28</month>
                        <year>2023</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20240213">
                        <day>02</day>
                        <month>13</month>
                        <year>2024</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2011, Educational Technology Theory and Practice</copyright-statement>
                    <copyright-year>2011</copyright-year>
                    <copyright-holder>Educational Technology Theory and Practice</copyright-holder>
                </permissions>
            
                                                                                                <abstract><p>Videos are the core components of MOOCs for delivering course content and teaching the core concepts effectively. While the literature provided strong and consistent evidence regarding the link between video engagement and the success in MOOCs, the research on video engagement behavior is still emerging and in demand of further research. This research aims to contribute to the literature by identifying behavioral patterns of video engagement in a MOOC and reveal the association of these patterns with success and failure. In particular, we employed simple video engagement metrics with an attempt to identify clusters of behavioral patterns that can be applied to different contexts. Acknowledging that students may exhibit varied engagement behaviors across study sessions, a session-level clustering analysis was performed, differently from previous research. After applying K-Means clustering algorithm, three clusters of behavioral patterns were identified: static viewing (the most predominant behavior), in which students viewed videos with minimal interactions; engaged viewing, involving high frequency of play and pause events; and focused viewing (the least frequent pattern), which involved mainly seeking the video for specific information. While video sessions with static viewing were very common among both high and low achieving students, most engaged-viewing sessions or focused-viewing sessions consistently belonged to the successful students. In addition, successful students were found to demonstrate multiple viewing behaviors, suggesting their effort in using multiple strategies while watching videos. Based on the findings, the paper discusses implications for the design of MOOCs and other online learning platforms that support video-based learning.</p></abstract>
                                                                                                                                    <trans-abstract xml:lang="tr">
                            <p>Videolar, ders içeriğini iletmek ve temel kavramları etkili bir şekilde öğretmek için kitlesel açık çevrimiçi derslerin temel bileşenleridir. Literatür, video izleme ile bu kitlesel derslerdeki öğrencilerin başarısı arasındaki bağlantı konusunda güçlü ve tutarlı bulgular sunsa da video izleme davranışı üzerine yapılan araştırmalar hala sınırlı ve yeni gelişmekte olan bir alandır. Bu araştırma makalesi, bir kitlesel açık çevrimiçi dersindeki video izleme aktivitelerindeki davranışsal desenleri tanımlayarak bu desenlerin başarı ve başarısızlıkla ilişkisini ortaya çıkarmayı amaçlamaktadır. Özellikle farklı bağlamlarda kullanılabilmesi ve uygulanabilmesi amacıyla temel ve yaygınlaştırılabilir video izleme metrikleri kullanılmıştır. Öğrencilerin bir öğrenme oturumu süresince farklı video görüntüleme davranışları gösterebileceği kabul edilerek, önceki araştırmalardan farklı olarak kümeleme analizi öğrenci özelinde değil oturum düzeyinde gerçekleştirilmiştir. Kümeleme analizi sonucunda üç davranışsal desen kümesi ortaya çıkmıştır: statik görüntüleme (en yaygın davranış), öğrencilerin minimum etkileşimle videoları izlediği durum; katılımlı görüntüleme, oynatma ve duraklama olaylarının sık olduğu durum; ve odaklı görüntüleme (en az rastlanan desen), özellikle belirli bir bilgiyi arama durumu. Statik görüntülemenin hâkim olduğu video oturumları hem başarılı hem de başarısız öğrenciler arasında yaygın olarak gözlemlenmiştir. Ancak katılımlı görüntüleme oturumları veya odaklı görüntüleme oturumları ise en çok başarılı öğrenciler tarafından sergilenmiştir. Ayrıca başarılı öğrencilerin birden fazla görüntüleme davranışı sergilediği saptanmıştır. Bu bulgu, öğrencilerin videoları izlerken çeşitli sayıda strateji uygulama çabalarını göstermektedir. Bulgulara dayalı olarak, video tabanlı öğrenmeyi destekleyen diğer çevrimiçi öğrenme platformlarının tasarımı için pratik öneriler paylaşılmıştır.</p></trans-abstract>
                                                            
            
                                                            <kwd-group>
                                                    <kwd>massive open online course</kwd>
                                                    <kwd>  video analytics</kwd>
                                                    <kwd>  clustering</kwd>
                                                    <kwd>  video-based learning</kwd>
                                            </kwd-group>
                                                        
                                                                            <kwd-group xml:lang="tr">
                                                    <kwd>kümeleme</kwd>
                                                    <kwd>  video tabanlı öğrenme</kwd>
                                                    <kwd>  video etkileşimleri</kwd>
                                                    <kwd>  öğrenme analitiği</kwd>
                                                    <kwd>  Kitlesel açık çevrimiçi ders</kwd>
                                            </kwd-group>
                                                                                                            </article-meta>
    </front>
    <back>
                            <ref-list>
                                    <ref id="ref1">
                        <label>1</label>
                        <mixed-citation publication-type="journal">Aggarwal, D., &amp; Sharma, D. (2019). Application of clustering for student result analysis. International Journal of Recent Technology and Engineering, 7(6C). https://www.researchgate.net/publication/333115249.</mixed-citation>
                    </ref>
                                    <ref id="ref2">
                        <label>2</label>
                        <mixed-citation publication-type="journal">Akcapinar, G., &amp; Bayazit, A. (2018). Investigating Video Viewing Behaviors of Students with Different Learning Approaches Using Video Analytics. Turkish Online Journal of Distance Education, 19(4), 116–125. https://doi.org/10.17718/tojde.471907.</mixed-citation>
                    </ref>
                                    <ref id="ref3">
                        <label>3</label>
                        <mixed-citation publication-type="journal">Antonenko, P. D., Toy, S., &amp; Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383–398. https://doi.org/10.1007/s11423-012-9235-8.</mixed-citation>
                    </ref>
                                    <ref id="ref4">
                        <label>4</label>
                        <mixed-citation publication-type="journal">Armstrong, A. W., Idriss, N. Z., &amp; Kim, R. H. (2011). Effects of video-based, online education on behavioral and knowledge outcomes in sunscreen use: A randomized controlled trial. Patient Education and Counseling, 83(2), 273–277. https://doi.org/10.1016/j.pec.2010.04.033.</mixed-citation>
                    </ref>
                                    <ref id="ref5">
                        <label>5</label>
                        <mixed-citation publication-type="journal">Benson, R., &amp; Samarawickrema, G. (2009). Addressing the context of e‐learning: using transactional distance theory to inform design. Distance Education, 30(1), 5–21. https://doi.org/10.1080/01587910902845972.</mixed-citation>
                    </ref>
                                    <ref id="ref6">
                        <label>6</label>
                        <mixed-citation publication-type="journal">Boroujeni, M. S., &amp; Dillenbourg, P. (2019). Discovery and temporal analysis of MOOC study patterns. Journal of Learning Analytics, 6(1), 16–33. https://doi.org/10.18608/jla.2019.61.2.</mixed-citation>
                    </ref>
                                    <ref id="ref7">
                        <label>7</label>
                        <mixed-citation publication-type="journal">Brinton, C. G., Buccapatnam, S., Chiang, M., &amp; Poor, H. V. (2016). Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz Performance. IEEE Transactions on Signal Processing, 64(14), 3677–3692. https://doi.org/10.1109/TSP.2016.2546228.</mixed-citation>
                    </ref>
                                    <ref id="ref8">
                        <label>8</label>
                        <mixed-citation publication-type="journal">Chatti, M. A., Marinov, M., Sabov, O., Laksono, R., Sofyan, Z., Fahmy Yousef, A. M., &amp; Schroeder, U. (2016). Video annotation and analytics in CourseMapper. Smart Learning Environments, 3(1). https://doi.org/10.1186/s40561-016-0035-1.</mixed-citation>
                    </ref>
                                    <ref id="ref9">
                        <label>9</label>
                        <mixed-citation publication-type="journal">Colasante, M. (2022). Not drowning, waving: The role of video in a renewed digital learning world. Australasian Journal of Educational Technology, 38(4), 176–189. https://doi.org/10.14742/ajet.7915.</mixed-citation>
                    </ref>
                                    <ref id="ref10">
                        <label>10</label>
                        <mixed-citation publication-type="journal">Darmayanti, P. and Nova, M. (2022). Evaluating interactive video utilization in english for tourism business class. Premise Journal of English Education, 11(3), 646-662. https://doi.org/10.24127/pj.v11i3.5661.</mixed-citation>
                    </ref>
                                    <ref id="ref11">
                        <label>11</label>
                        <mixed-citation publication-type="journal">Desai, T. and Kulkarni, D. (2022). Assessment of interactive video to enhance learning experience: a case study. Journal of Engineering Education Transformations, 35(S1), 74-80. https://doi.org/10.16920/jeet/2022/v35is1/22011.</mixed-citation>
                    </ref>
                                    <ref id="ref12">
                        <label>12</label>
                        <mixed-citation publication-type="journal">Eisenberg, M., &amp; Fischer, G. (2014). MOOCs: a Perspective from the Learning Sciences. Proceedings of ICLS 2014.</mixed-citation>
                    </ref>
                                    <ref id="ref13">
                        <label>13</label>
                        <mixed-citation publication-type="journal">Er, E., Gómez-Sánchez, E., Dimitriadis, Y., Bote-Lorenzo, M. L., Asensio-Pérez, J. I., &amp; Álvarez-Álvarez, S. (2019). Aligning learning design and learning analytics through instructor involvement: A MOOC case study. Interactive Learning Environments, 27(5–6), 685–698. https://doi.org/10.1080/10494820.2019.1610455.</mixed-citation>
                    </ref>
                                    <ref id="ref14">
                        <label>14</label>
                        <mixed-citation publication-type="journal">Giannakos, M. N., Chorianopoulos, K. &amp; Chrisochoides, N. (2014), Collecting and making sense of video learning analytics, in ‘Frontiers in Education Conference (FIE), 2014 IEEE’, IEEE, pp. 1–7.</mixed-citation>
                    </ref>
                                    <ref id="ref15">
                        <label>15</label>
                        <mixed-citation publication-type="journal">Giannakos, M. N., Chorianopoulos, K., &amp; Chrisochoides, N. (2015). Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course. International Review of Research in Open and Distributed Learning, 16(1).</mixed-citation>
                    </ref>
                                    <ref id="ref16">
                        <label>16</label>
                        <mixed-citation publication-type="journal">Glance, D. G., Forsey, M., &amp; Riley, M. (2013). The pedagogical foundations of massive open online courses. First Monday, 18(5). https://doi.org/10.5210/fm.v18i5.4350.</mixed-citation>
                    </ref>
                                    <ref id="ref17">
                        <label>17</label>
                        <mixed-citation publication-type="journal">Guo, P. J., Kim, J., &amp; Rubin, R. (2014). How video production affects student engagement. Proceedings of the First ACM Conference on Learning @ Scale Conference, 41–50. https://doi.org/10.1145/2556325.2566239.</mixed-citation>
                    </ref>
                                    <ref id="ref18">
                        <label>18</label>
                        <mixed-citation publication-type="journal">Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., &amp; Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences (Switzerland), 10(11). https://doi.org/10.3390/app10113894.</mixed-citation>
                    </ref>
                                    <ref id="ref19">
                        <label>19</label>
                        <mixed-citation publication-type="journal">Hew, K. F. (2015). Towards a model of engaging online students: Lessons from MOOCs and four policy documents. International Journal of Information and Education Technology, 5(6), 425–431. https://doi.org/10.7763/IJIET.2015.V5.543.</mixed-citation>
                    </ref>
                                    <ref id="ref20">
                        <label>20</label>
                        <mixed-citation publication-type="journal">Kay, J., Reimann, P., Diebold, E., &amp; Kummerfeld, B. (2013). MOOCs: So many learners , so much potential. IEEE Intelligent Systems, 28(3), 70–77.</mixed-citation>
                    </ref>
                                    <ref id="ref21">
                        <label>21</label>
                        <mixed-citation publication-type="journal">Khalil, M., Topali, P., Ortega-Arranz, A. et al. (2023) Video Analytics in Digital Learning Environments: Exploring Student Behaviour Across Different Learning Contexts. Technology, Knowledge, and Learning. https://doi.org/10.1007/s10758-023-09680-8.</mixed-citation>
                    </ref>
                                    <ref id="ref22">
                        <label>22</label>
                        <mixed-citation publication-type="journal">Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., &amp; Miller, R. C. (2014). Understanding in-video dropouts and interaction peaks inonline lecture videos. Proceedings of the First ACM Conference on Learning @ Scale Conference, 31–40. https://doi.org/10.1145/2556325.2566237.</mixed-citation>
                    </ref>
                                    <ref id="ref23">
                        <label>23</label>
                        <mixed-citation publication-type="journal">Kizilcec, R. F., Piech, C., &amp; Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. Proceedings of Third International Conference on Learning Analytics and Knowledge, 170–179. https://doi.org/10.1145/2460296.2460330.</mixed-citation>
                    </ref>
                                    <ref id="ref24">
                        <label>24</label>
                        <mixed-citation publication-type="journal">Lan, A. S., Brinton, C. G., Yang, T.-Y., &amp; Chiang, M. (2017). Behavior-based latent variable model for learner engagement. Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017).</mixed-citation>
                    </ref>
                                    <ref id="ref25">
                        <label>25</label>
                        <mixed-citation publication-type="journal">Lee, J. K., &amp; Lee, W. K. (2008). The relationship of e-Learner’s self-regulatory efficacy and perception of e-Learning environmental quality. Computers in Human Behavior, 24(1), 32–47.</mixed-citation>
                    </ref>
                                    <ref id="ref26">
                        <label>26</label>
                        <mixed-citation publication-type="journal">Lemay, D. J., &amp; Doleck, T. (2020). Grade prediction of weekly assignments in MOOCS: mining video-viewing behavior. Education and Information Technologies, 25(2), 1333–1342. https://doi.org/10.1007/s10639-019-10022-4.</mixed-citation>
                    </ref>
                                    <ref id="ref27">
                        <label>27</label>
                        <mixed-citation publication-type="journal">Li, N., Kidzinski, L., Jermann, P., &amp; Dillenbourg, P. (2015). MOOC video interaction patterns: What do they tell us? In G. Conole, T. Klobučar, C. Rensing, J. Konert, &amp; E. Lavoué (Eds.), Design for Teaching and Learning in a Networked World (Vol. 9307). Springer, Cham. https://doi.org/10.1007/978-3-319-24258-3.</mixed-citation>
                    </ref>
                                    <ref id="ref28">
                        <label>28</label>
                        <mixed-citation publication-type="journal">Littlejohn, A., Hood, N., Milligan, C., &amp; Mustain, P. (2016). Learning in MOOCs : Motivations and self-regulated learning in MOOCs. The Internet and Higher Education, 29, 40–48. https://doi.org/10.1016/j.iheduc.2015.12.003.</mixed-citation>
                    </ref>
                                    <ref id="ref29">
                        <label>29</label>
                        <mixed-citation publication-type="journal">Lockyer, L., Heathcote, E., &amp; Dawson, S. (2013). Informing pedagogical action: aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439–1459. https://doi.org/10.1177/0002764213479367.</mixed-citation>
                    </ref>
                                    <ref id="ref30">
                        <label>30</label>
                        <mixed-citation publication-type="journal">Matcha, W., Gasevic, D., Uzir, N. A., Jovanovic, J., Pardo, A., Lim, L., &amp; Maldonado-Mahauad, J. (2020). Analytics of learning strategies: Role of course design and delivery modality. Journal of Learning Analytics, 7(2), 45–71.</mixed-citation>
                    </ref>
                                    <ref id="ref31">
                        <label>31</label>
                        <mixed-citation publication-type="journal">Mbouzao, B., Desmarais, M. C., &amp; Shrier, I. (2020). Early prediction of success in MOOC from video interaction features. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12164 LNAI, 191–196. https://doi.org/10.1007/978-3-030-52240-7_35.</mixed-citation>
                    </ref>
                                    <ref id="ref32">
                        <label>32</label>
                        <mixed-citation publication-type="journal">Milligan, C., Littlejohn, A., &amp; Margaryan, A. (2013). Patterns of engagement in connectivist MOOCs. MERLOT Journal of Online Learning and Teaching, 9(2), 149–159.</mixed-citation>
                    </ref>
                                    <ref id="ref33">
                        <label>33</label>
                        <mixed-citation publication-type="journal">Mirriahi, N., Liaqat, D., Dawson, S., &amp; Gašević, D. (2016). Uncovering student learning profiles with a video annotation tool: Reflective learning with and without instructional norms. Educational Technology Research and Development, 64(6), 1083–1106. https://doi.org/10.1007/s11423-016-9449-2.</mixed-citation>
                    </ref>
                                    <ref id="ref34">
                        <label>34</label>
                        <mixed-citation publication-type="journal">Mubarak, A. A., Cao, H., Zhang, W., &amp; Zhang, W. (2021). Visual analytics of video-clickstream data and prediction of learners’ performance using deep learning models in MOOCs’ courses. Computer Applications in Engineering Education, 29(4), 710–732. https://doi.org/10.1002/cae.22328.</mixed-citation>
                    </ref>
                                    <ref id="ref35">
                        <label>35</label>
                        <mixed-citation publication-type="journal">Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., &amp; Duchesnay, É. (2012). Scikit-learn: Machine learning in Python. 12, 2825–2830. https://doi.org/10.1007/s13398-014-0173-7.2.</mixed-citation>
                    </ref>
                                    <ref id="ref36">
                        <label>36</label>
                        <mixed-citation publication-type="journal">Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. In Journal of Computational and Applied Mathematics (Vol. 20).</mixed-citation>
                    </ref>
                                    <ref id="ref37">
                        <label>37</label>
                        <mixed-citation publication-type="journal">Safitri, D., Lestari, I., Maksum, A., Ibrahim, N., Marini, A., Zahari, M., … &amp; Iskandar, R. (2021). Web-based animation video for student environmental education at elementary schools. International Journal of Interactive Mobile Technologies, 15(11), 66-80. https://doi.org/10.3991/ijim.v15i11.22023.</mixed-citation>
                    </ref>
                                    <ref id="ref38">
                        <label>38</label>
                        <mixed-citation publication-type="journal">Shen, W. (2014). Using Video Recording System to Improve Student Performance in High-Fidelity Simulation. In S. Li, Q. Jin, X. Jiang, &amp; J. H. Par (Eds.), Frontier and Future Development of Information Technology in Medicine and Education (pp. 1753–1757).</mixed-citation>
                    </ref>
                                    <ref id="ref39">
                        <label>39</label>
                        <mixed-citation publication-type="journal">Stöhr, C., Stathakarou, N., Mueller, F., Nifakos, S., &amp; McGrath, C. (2019). Videos as learning objects in MOOCs: A study of specialist and non-specialist participants’ video activity in MOOCs. British Journal of Educational Technology, 50(1), 166–176. https://doi.org/10.1111/bjet.12623.</mixed-citation>
                    </ref>
                                    <ref id="ref40">
                        <label>40</label>
                        <mixed-citation publication-type="journal">Su, Y. S., &amp; Wu, S. Y. (2021). Applying data mining techniques to explore user behaviors and watching video patterns in converged IT environments. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02712-6.</mixed-citation>
                    </ref>
                                    <ref id="ref41">
                        <label>41</label>
                        <mixed-citation publication-type="journal">Walji, S., Deacon, A., Small, J., &amp; Czerniewicz, L. (2016). Learning through engagement: MOOCs as an emergent form of provision. Distance Education, 37(2), 208–223. https://doi.org/10.1080/01587919.2016.1184400.</mixed-citation>
                    </ref>
                                    <ref id="ref42">
                        <label>42</label>
                        <mixed-citation publication-type="journal">Yoon, M., Lee, J., &amp; Jo, I. H. (2021). Video learning analytics: Investigating behavioral patterns and learner clusters in video-based online learning. Internet and Higher Education, 50. https://doi.org/10.1016/j.iheduc.2021.100806.</mixed-citation>
                    </ref>
                                    <ref id="ref43">
                        <label>43</label>
                        <mixed-citation publication-type="journal">Yürüm, O., Temizel, T., &amp; Yildirim, S. (2022). The use of video clickstream data to predict university students’ test performance: a comprehensive educational data mining approach. Education and Information Technologies, 28(5), 5209-5240. https://doi.org/10.1007/s10639-022-11403-y.</mixed-citation>
                    </ref>
                                    <ref id="ref44">
                        <label>44</label>
                        <mixed-citation publication-type="journal">Vioskha, Y., Roza, Y., &amp; Maimunah, M. (2021). Improving mathematics cognitive learning outcomes through the application of bandicam video to class x senior high school students in kampar regency. Journal of Educational Sciences, 5(4), 665-667. https://doi.org/10.31258/jes.5.4.p.665-677.</mixed-citation>
                    </ref>
                                    <ref id="ref45">
                        <label>45</label>
                        <mixed-citation publication-type="journal">Zhang, J., Huang, Y., &amp; 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</mixed-citation>
                    </ref>
                            </ref-list>
                    </back>
    </article>
