Handbook of Learning Analytics

Chapter 22

Handbook of Learning Analytics
First Edition

Analytics of Learner Video Use

Negin Mirriahi & Lorenzo Vigentini


Videos are becoming a core component of many pedagogical approaches, particularly with the rise in interest in blended learning, flipped classrooms, and massive open and online courses (MOOCs). Although there are a variety of types of videos used for educational purposes, lecture videos are the most widely adopted. Furthermore, with recent advances in video streaming technologies, learners’ digital footprints when accessing videos can be mined and analyzed to better understand how they learn and engage with them. The collection, measurement, and analysis of such data for the purposes of understanding how learners use videos can be referred to as video analytics. Coupled with more traditional data collection methods, such as interviews or surveys, and performance data to obtain a holistic view of how and why learners engage and learn with videos, video analytics can help inform course design and teaching practice. In this chapter, we provide an overview of videos integrated in the curriculum including an introduction to multimedia learning and discuss data mining approaches for investigating learner use, engagement with, and learning with videos, and provide suggestions for future directions.

Export Citation: Plain Text (APA)     BIBTeX     RIS

Supplementary Material

No Supplementary Material Available

References (104)

Antani, S., Kasturi, R., & Jain, R. (2002). A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recognition, 35(4), 945–965. http://doi.org/10.1016/S0031-3203(01)00086-3

Anusha, V., & Shereen, J. (2014). Multiple lecture video annotation and conducting quiz using random tree classification. International Journal of Engineering Trends and Technology, 8(10), 522–525.

Atkins, M. J. (1993). Theories of learning and multimedia applications: An overview. Research Papers in Education, 8(2), 251–271. http://doi.org/10.1080/0267152930080207

Avlonitis, M., & Chorianopoulos, K. (2014). Video pulses: User-based modeling of interesting video segments. Advances in Multimedia, 2014. http://doi.org/10.1155/2014/712589

Avlonitis, M., Karydis, I., & Sioutas, S. (2015). Early prediction in collective intelligence on video users’ activity. Information Sciences, 298, 315–329. http://doi.org/10.1016/j.ins.2014.11.039

Beretvas, S. N., Meyers, J. L., & Leite, W. L. (2002). A reliability generalization study of the Marlowe-Crowne social desirability scale. Educational and Psychological Measurement, 62(4), 570–589. http://doi.org/10.1177/0013164402062004003

Bloom, B. S. (1968). Learning for Mastery: Instruction and Curriculum. Regional Education Laboratory for the Carolinas and Virginia, Topical Papers and Reprints, Number 1. Evaluation Comment, 1(2), n2.

Brooks, C., Epp, C. D., Logan, G., & Greer, J. (2011). The who, what, when, and why of lecture capture. Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK ʼ11), 27 February–1 March 2011, Banff, AB, Canada (pp. 86–92). New York: ACM.

Brunelli, R., Mich, O., & Modena, C. M. (1999). A survey on the automatic indexing of video data. Journal of Visual Communication and Image Representation, 10(2), 78–112. http://doi.org/10.1006/jvci.1997.0404

Chen, B., Seilhamer, R., Bennett, L., & Bauer, S. (2015, June 22). Students’ mobile learning practices in higher education: A multi-year study. Educause Review. http://er.educause.edu/articles/2015/6/students-mobile-learning-practices-in-higher-education-a-multiyear-study

Chen, L., Chen, G.-C., Xu, C.-Z., March, J., & Benford, S. (2008). EmoPlayer: A media player for video clips with affective annotations. Interacting with Computers, 20(1), 17–28. http://doi.org/10.1016/j.intcom.2007.06.003

Chorianopoulos, K. (2012). Crowdsourcing user interactions with the video player. Proceedings of the 18th Brazilian Symposium on Multimedia and the Web (WebMedia ’12), 15–18 October 2012, São Paulo, Brazil (pp. 13–16). New York: ACM. http://doi.org/10.1145/2382636.2382642

Chorianopoulos, K. (2013). Collective intelligence within web video. Human-Centric Computing and Information Sciences, 3(1), 1–16. http://doi.org/10.1186/2192-1962-3-10

Chorianopoulos, K., Giannakos, M. N., Chrisochoides, N., & Reed, S. (2014). Open service for video learning analytics. Proceedings of the 14th IEEE International Conference on Advanced Learning Technologies (ICALT 2014), 7–10 July 2014, Athens, Greece (pp. 28–30). http://doi.org/10.1109/ICALT.2014.19

Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459. http://doi.org/10.3102/00346543053004445

Clark, R. E. (1994). Media and method. Educational Technology Research and Development, 42(3), 7–10. http://doi.org/10.1007/BF02298090

Cobârzan, C., & Schoeffmann, K. (2014). How do users search with basic HTML5 video players? In C. Gurrin, F. Hopfgartner, W. Hurst, H. Johansen, H. Lee, & N. O’Connor (Eds.), MultiMedia Modeling (pp. 109–120). Springer. http://link.springer.com.wwwproxy0.library.unsw.edu.au/chapter/10.1007/978-3-319-04114-8_10

Colasante, M. (2010). Future-focused learning via online anchored discussion, connecting learners with digital artefacts, other learners, and teachers. Proceedings of the 27th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education: Curriculum, Technology & Transformation for an Unknown Future (ASCILITE 2010), 5–8 December 2010, Sydney, Australia (pp. 211–221). ASCILITE.

Colasante, M. (2011). Using video annotation to reflect on and evaluate physical education pre-service teaching practice. Australasian Journal of Educational Technology, 27(1), 66–88.

Coleman, C. A., Seaton, D. T., & Chuang, I. (2015). Probabilistic use cases: Discovering behavioral patterns for predicting certification. Proceedings of the 2nd ACM conference on Learning@Scale (L@S 2015), 14–18 March 2015, Vancouver, BC, Canada (pp. 141–148). New York: ACM. http://doi.org/10.1145/2724660.2724662

Conole, G. (2013). MOOCs as disruptive technologies: Strategies for enhancing the learner experience and quality of MOOCs. Revista de Educación a Distancia (RED), 39. http://www.um.es/ead/red/39/conole.pdf

Crockford, C., & Agius, H. (2006). An empirical investigation into user navigation of digital video using the VCR-like control set. International Journal of Human–Computer Studies, 64(4), 340–355. http://doi.org/10.1016/j.ijhcs.2005.08.012

Daniel, R. (2001). Self-assessment in performance. British Journal of Music Education, 18(3). http://doi.org/10.1017/S0265051701000316

Dawson, S., Macfadyen, L., Evan, F. R., Foulsham, T., & Kingstone, A. (2012). Using technology to encourage self-directed learning: The Collaborative Lecture Annotation System (CLAS). Proceedings of the 29th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE 2012), 25–28 October, Wellington, New Zealand (pp. XXX–XXX). ASCILITE. http://www.ascilite.org/conferences/Wellington12/2012/images/custom/dawson,_shane_-_using_technology.pdf

de Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2011). Attention cueing in an instructional animation: The role of presentation speed. Computers in Human Behavior, 27(1), 41–45. http://doi.org/10.1016/j.chb.2010.05.010

Delen, E., Liew, J., & Willson, V. (2014). Effects of interactivity and instructional scaffolding on learning: Self-regulation in online video-based environments. Computers & Education, 78, 312–320. http://doi.org/10.1016/j.compedu.2014.06.018

Diwanji, P., Simon, B. P., Marki, M., Korkut, S., & Dornberger, R. (2014). Success factors of online learning videos. Proceedings of the International Conference on Interactive Mobile Communication Technologies and Learning (IMCL 2014), 13–14 November 2014, Thessaloniki, Greece (pp. 125–132). IEEE. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7011119

Dufour, C., Toms, E. G., Lewis, J., & Baecker, R. (2005). User strategies for handling information tasks in webcasts. CHI ’05 Extended Abstracts on Human Factors in Computing Systems (CHI EA ’05), 2–7 April 2005, Portland, OR, USA (pp. 1343–1346). New York: ACM. http://doi.org/10.1145/1056808.1056912

El Samad, A., & Hamid, O. H. (2015). The role of socio-economic disparities in varying the viewing behavior of e-learners. Proceedings of the 5th International Conference on Digital Information and Communication Technology and its Applications (DICTAP) (pp. 74–79). https://doi.org/10.1109/DICTAP.2015.7113174

Fegade, M. A., & Dalal, V. (2014). A survey on content based video retrieval. International Journal of Engineering and Computer Science, 3(7), 7271–7279.

Gagne, R. M. (1965). The conditions of learning. Holt, Rinehart & Winston.

Gašević, D., Mirriahi, N., & Dawson, S. (2014). Analytics of the effects of video use and instruction to support reflective learning. Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 123–132). New York: ACM. http://doi.org/10.1145/2567574.2567590

Giannakos, M. N. (2013). Exploring the video-based learning research: A review of the literature: Colloquium. British Journal of Educational Technology, 44(6), E191–E195. http://doi.org/10.1111/bjet.12070

Giannakos, M. N., Chorianopoulos, K., & Chrisochoides, N. (2014). Collecting and making sense of video learning analytics. Proceedings of the 2014 IEEE Frontiers in Education Conference (FIE 2014), 22–25 October 2014, Madrid, Spain. IEEE. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7044485

Giannakos, M. N., Chorianopoulos, K., & Chrisochoides, N. (2015). Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course. The International Review of Research in Open and Distributed Learning, 16(1). http://www.irrodl.org/index.php/irrodl/article/view/1976

Giannakos, M. N., Jaccheri, L., & Krogstie, J. (2015). Exploring the relationship between video lecture usage patterns and students’ attitudes: Usage patterns on video lectures. British Journal of Educational Technology, 47(6), 1259–1275. http://doi.org/10.1111/bjet.12313

Gkonela, C., & Chorianopoulos, K. (2012). VideoSkip: Event detection in social web videos with an implicit user heuristic. Multimedia Tools and Applications, 69(2), 383–396. http://doi.org/10.1007/s11042-012-1016-1

Gonyea, R. M. (2005). Self-reported data in institutional research: Review and recommendations. New Directions for Institutional Research, 2005(127), 73–89.

Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42–57.

Grigoras, R., Charvillat, V., & Douze, M. (2002). Optimizing hypervideo navigation using a Markov decision process approach. Proceedings of the 10th ACM International Conference on Multimedia (MULTIMEDIA ’02), 1–6 December 2002, Juan-les-Pins, France (pp. 39–48). New York: ACM. http://doi.org/10.1145/641007.641014

Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: An empirical study of MOOC videos. Proceedings of the 1st ACM conference on Learning@Scale (L@S 2014), 4–5 March 2014, Atlanta, Georgia, USA (pp. 41–50). New York: ACM. http://doi.org/10.1145/2556325.2566239

Guskey, T. R., & Good, T. L. (2009). Mastery learning. In T. L. Good (Ed.), 21st Century Education: A Reference Handbook, vol. 1 (pp. 194–202). Thousand Oaks, CA: Sage.

He, W. (2013). Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29(1), 90–102. http://doi.org/10.1016/j.chb.2012.07.020

He, L., Grudin, J., & Gupta, A. (2000). Designing presentations for on-demand viewing. Proceedings of the 2000 Conference on Computer Supported Cooperative Work (CSCW ’00), 2–6 December 2000, Philadelphia, PA, USA (pp. 127–134). New York: ACM. http://doi.org/10.1145/358916.358983

He, L., Sanocki, E., Gupta, A., & Grudin, J. (2000). Comparing presentation summaries: Slides vs. reading vs. listening. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2000), 1–6 April 2000, The Hague, Netherlands (pp. 177–184). New York: ACM. http://doi.org/10.1145/332040.332427

Hulsman, R. L., Harmsen, A. B., & Fabriek, M. (2009). Reflective teaching of medical communication skills with DiViDU: Assessing the level of student reflection on recorded consultations with simulated patients. Patient Education and Counseling, 74(2), 142–149. http://doi.org/10.1016/j.pec.2008.10.009

Ilioudi, C., Giannakos, M. N., & Chorianopoulos, K. (2013). Investigating differences among the commonly used video lecture styles. Proceedings of the Workshop on Analytics on Video-Based Learning (WAVe 2013), 8 April 2013, Leuven, Belgium (pp. 21–26). http://ceur-ws.org/Vol-983/WAVe2013-Proceedings.pdf

Johnson, T. (2011, July 22). A few notes from usability testing: Video tutorials get watched, text gets skipped. I’d Rather Be Writing [Blog]. http://idratherbewriting.com/2011/07/22/a-few-notes-from-usability-testing-video-tutorials-get-watched-text-gets-skipped/

Joy, E. H., & Garcia, F. E. (2000). Measuring learning effectiveness: A new look at no-significant-difference findings. Journal of Asynchronous Learning Networks, 4(1), 33–39.

Juhlin, O., Zoric, G., Engström, A., & Reponen, E. (2014). Video interaction: A research agenda. Personal and Ubiquitous Computing, 18(3), 685–692. http://doi.org/10.1007/s00779-013-0705-8

Kamahara, J., Nagamatsu, T., Fukuhara, Y., Kaieda, Y., & Ishii, Y. (2009). Method for identifying task hardships by analyzing operational logs of instruction videos. In T.-S. Chua, Y. Kompatsiaris, B. Mérialdo, W. Haas, G. Thallinger, & W. Bailer (Eds.), Semantic Multimedia (pp. 161–164). Springer. http://link.springer.com.wwwproxy0.library.unsw.edu.au/chapter/10.1007/978-3-642-10543-2_16

Keller, F. S. (1967). Engineering personalized instruction in the classroom. Revista Interamericana de Psicologia, 1(3), 144–156.

Kim, J., Guo, P. J., Cai, C. J., Li, S.-W. (Daniel), Gajos, K. Z., & Miller, R. C. (2014). Data-driven Interaction Techniques for Improving Navigation of Educational Videos. In Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology (pp. 563–572). New York, NY, USA: ACM. https://doi.org/10.1145/2642918.2647389

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 1st ACM Conference on Learning @ Scale (L@S 2014), 4–5 March 2014, Atlanta, GA, USA (pp. 31–40). New York: ACM. http://doi.org/10.1145/2556325.2566239

Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. http://doi.org/10.1207/s15326985ep4102_1

Kolowich, S. (2013, March 18). The professors who make the MOOCs. The Chronicle of Higher Education, 25. http://www.chronicle.com/article/The-Professors-Behind-the-MOOC/137905/

Kozma, R. B. (1991). Learning with media. Review of Educational Research, 61(2), 179–211. http://doi.org/10.3102/00346543061002179

Kozma, R. B. (1994). Will media influence learning? Reframing the debate. Educational Technology Research and Development, 42(2), 7–19. http://doi.org/10.1007/BF02299087

Kulik, C.-L. C., Kulik, J. A., & Bangert-Drowns, R. L. (1990). Effectiveness of mastery learning programs: A meta-analysis. Review of Educational Research, 60(2), 265–299. http://doi.org/10.3102/00346543060002265

Lee, H. S., & Anderson, J. R. (2013). Student learning: What has instruction got to do with it? Annual Review of Psychology, 64(1), 445–469. http://doi.org/10.1146/annurev-psych-113011-143833

Li, F. C., Gupta, A., Sanocki, E., He, L., & Rui, Y. (2000). Browsing digital video. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2000), 1–6 April 2000, The Hague, Netherlands (pp. 169–176). New York: ACM. http://doi.org/10.1145/332040.332425

Li, N., Kidzinski, L., Jermann, P., & Dillenbourg, P. (2015). How do in-video interactions reflect perceived video difficulty? Proceedings of the 3rd European MOOCs Stakeholder Summit, 18–20 May 2015, Mons, Belgium (pp. 112–121). PAU Education. http://infoscience.epfl.ch/record/207968

Li, K., T. Zhang, X. Hu, D. Zhu, H. Chen, X. Jiang, F. Deng, J. Lv, C. C. Faraco, and D. Zhang. 2010. “Human-Friendly Attention Models for Video Summarization.” In International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction (pp. 27:1–27:8). New York: ACM. http://doi.org/10.1145/1891903.1891938

Lyons, A., Reysen, S., & Pierce, L. (2012). Video lecture format, student technological efficacy, and social presence in online courses. Computers in Human Behavior, 28(1), 181–186. http://doi.org/10.1016/j.chb.2011.08.025

Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of Massive Open Online Courses (MOOCs). Computers & Education, 80, 77–83. http://doi.org/10.1016/j.compedu.2014.08.005

Mayer, R. E. (2009). Multimedia learning. Cambridge, UK: Cambridge University Press.

Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93(1), 187–198. http://doi.org/10.1037/0022-0663.93.1.187

McKeachie, W. J. (1974). Instructional psychology. Annual Review of Psychology, 25(1), 161–193. http://doi.org/10.1146/annurev.ps.25.020174.001113

Merkt, M., Weigand, S., Heier, A., & Schwan, S. (2011). Learning with videos vs. learning with print: The role of interactive features. Learning and Instruction, 21(6), 687–704. http://doi.org/10.1016/j.learninstruc.2011.03.004

Mirriahi, N., & Dawson, S. (2013). The pairing of lecture recording data with assessment scores: A method of discovering pedagogical impact. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (LAK ’13), 8–12 April 2013, Leuven, Belgium (pp. 180–184). New York: ACM. http://dl.acm.org/citation.cfm?id=2460331

Mirriahi, N., Liaqat, D., Dawson, S., & 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. http://doi.org/10.1007/s11423-016-9449-2

Monserrat, T.-J. K. P., Zhao, S., McGee, K., & Pandey, A. V. (2013). NoteVideo: Facilitating navigation of Blackboard-style lecture videos. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ʼ13), 27 April–2 May 2013, Paris, France (pp. 1139–1148). New York: ACM. http://doi.org/10.1145/2470654.2466147

Mu, X. (2010). Towards effective video annotation: An approach to automatically link notes with video content. Computers & Education, 55(4), 1752–1763. http://doi.org/10.1016/j.compedu.2010.07.021

Oblinger, D. G., & Hawkins, B. L. (2006). The myth about no significant difference. EDUCAUSE Review, 41(6), 14–15.

Owston, R., Lupshenyuk, D., & Wideman, H. (2011). Lecture capture in large undergraduate classes: Student perceptions and academic performance. The Internet and Higher Education, 14(4), 262–268. http://doi.org/10.1016/j.iheduc.2011.05.006

Palincsar, A. S. (1998). Social constructivist perspectives on teaching and learning. Annual Review of Psychology, 49(1), 345–375. http://doi.org/10.1146/annurev.psych.49.1.345

Pardo, A., Mirriahi, N., Dawson, S., Zhao, Y., Zhao, A., & Gašević, D. (2015). Identifying learning strategies associated with active use of video annotation software. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 255–259). New York: ACM Press. http://doi.org/10.1145/2723576.2723611

Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4, Part 1), 1432–1462. http://doi.org/10.1016/j.eswa.2013.08.042

Phillips, R., Maor, D., Cumming-Potvin, W., Roberts, P., Herrington, J., Preston, G., … Perry, L. (2011). Learning analytics and study behaviour: A pilot study. In G. Williams, P. Statham, N. Brown, & B. Cleland (Eds.), Proceedings of the 28th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education: Changing Demands, Changing Directions (ASCILITE 2011), 4–7 December 2011, Hobart, Tasmania, Australia (pp. 997–1007). ASCILITE. http://researchrepository.murdoch.edu.au/6751/

Reeves, T. C. (1986). Research and evaluation models for the study of interactive video. Journal of Computer-Based Instruction, 13(4), 102–106.

Reeves, T. C. (1991). Ten commandments for the evaluation of interactive multimedia in higher education. Journal of Computing in Higher Education, 2(2), 84–113. http://doi.org/10.1007/BF02941590

Risko, E. F., Foulsham, T., Dawson, S., & Kingstone, A. (2013). The collaborative lecture annotation system (CLAS): A new TOOL for distributed learning. IEEE Transactions on Learning Technologies, 6(1), 4–13. http://doi.org/10.1109/TLT.2012.15

Ritzhaupt, A. D., Pastore, R., & Davis, R. (2015). Effects of captions and time-compressed video on learner performance and satisfaction. Computers in Human Behavior, 45, 222–227. http://doi.org/10.1016/j.chb.2014.12.020
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146.

Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), 601–618. https://doi.org/10.1109/TSMCC.2010.2053532

Russell, T. L. (1999). The no significant difference phenomenon: A comparative research annotated bibliography on technology for distance education: As reported in 355 research reports, summaries and papers. North Carolina State University.

Schwan, S., & Riempp, R. (2004). The cognitive benefits of interactive videos: Learning to tie nautical knots. Learning and Instruction, 14(3), 293–305. http://doi.org/10.1016/j.learninstruc.2004.06.005

Shi, C., Fu, S., Chen, Q., & Qu, H. (2014). VisMOOC: Visualizing video clickstream data from massive open online courses. Proceedings of the 2014 IEEE Conference on Visual Analytics Science and Technology (VAST 2014), 9–14 November 2014, Paris, France (pp. 277–278). IEEE. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7042528

Sinha, T., & Cassell, J. (2015). Connecting the dots: Predicting student grade sequences from Bursty MOOC interactions over time. Proceedings of the 2nd ACM Conference on Learning@Scale (L@S 2015), 14–18 March 2015, Vancouver, BC, Canada (pp. 249–252). New York: ACM. http://doi.org/10.1145/2724660.2728669

Skinner, F. B. (1950). Are theories of learning necessary? Psychological Review, 57(4), 193–216. http://doi.org/10.1037/h0054367

Song, S., Hong, J., Oakley, I., Cho, J. D., & Bianchi, A. (2015). Automatically adjusting the speed of e-learning videos. CHI 33rd Conference on Human Factors in Computing Systems: Extended Abstracts (CHI EA ’15), 18–23 April 2015, Seoul, Republic of Korea (pp. 1451–1456). New York: ACM. http://doi.org/10.1145/2702613.2732711

Syeda-Mahmood, T., & Ponceleon, D. (2001). Learning video browsing behavior and its application in the generation of video previews. Proceedings of the 9th ACM International Conference on Multimedia (MULTIMEDIA ’01), 30 September–5 October 2001, Ottawa, ON, Canada (pp. 119–128). New York: ACM. http://doi.org/10.1145/500141.500161

Tennyson, R. D. (1994). The big wrench vs. integrated approaches: The great media debate. Educational Technology Research and Development, 42(3), 15–28. http://doi.org/10.1007/BF02298092

Vondrick, C., & Ramanan, D. (2011). Video annotation and tracking with active learning. In J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 24 (NIPS 2011), 12–17 December 2011, Granada, Spain (pp. 28–36). http://papers.nips.cc/paper/4233-video-annotation-and-tracking-with-active-learning

Weir, S., Kim, J., Gajos, K. Z., & Miller, R. C. (2015). Learnersourcing subgoal labels for how-to videos. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’15), 14–18 March 2015, Vancouver, BC, Canada (pp. 405–416). New York: ACM. http://doi.org/10.1145/2675133.2675219

Wen, M., & Rosé, C. P. (2014). Identifying latent study habits by mining learner behavior patterns in massive open online courses. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM ’14), 3–7 November 2014, Shanghai, China (pp. 1983–1986). New York: ACM. http://doi.org/10.1145/2661829.2662033

Wieling, M. B., & Hofman, W. H. A. (2010). The impact of online video lecture recordings and automated feedback on student performance. Computers & Education, 54(4), 992–998. http://doi.org/10.1016/j.compedu.2009.10.002

Winne, P. H., & Jamieson-Noel, D. (2002). Exploring students’ calibration of self reports about study tactics and achievement. Contemporary Educational Psychology, 27(4), 551–572.

Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Burlington, MA: Morgan Kaufmann Publishers.

Yousef, A. M. F., Chatti, M. A., & Schroeder, U. (2014). Video-based learning: A critical analysis of the research published in 2003–2013 and future visions. Proceedings of the 6th International Conference on Mobile, Hybrid, and On-line Learning (ThinkMind/eLmL 2014), 23–27 March 2014, Barcelona, Spain (pp. 112–119). http://www.thinkmind.org/index.php?view=article&articleid=elml_2014_5_30_50050

Yu, B., Ma, W.-Y., Nahrstedt, K., & Zhang, H.-J. (2003). Video summarization based on user log enhanced link analysis. Proceedings of the 11th ACM International Conference on Multimedia (MULTIMEDIA ’03), 2–8 November 2003, Berkeley, CA, USA (pp. 382–391). New York: ACM. http://doi.org/10.1145/957013.957095

Zahn, C., Barquero, B., & Schwan, S. (2004). Learning with hyperlinked videos: Design criteria and efficient strategies for using audiovisual hypermedia. Learning and Instruction, 14(3), 275–291. http://doi.org/10.1016/j.learninstruc.2004.06.004

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. http://doi.org/10.1016/j.im.2005.01.004

Zupancic, B., & Horz, H. (2002). Lecture recording and its use in a traditional university course. In Proceedings of the 7th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE ’02), 24–28 June 2002, Aarhus, Denmark (pp. 24–28). New York: ACM. http://doi.org/10.1145/544414.544424

About this Chapter

Analytics of Learner Video Use

Book Title
Handbook of Learning Analytics

pp. 251-267




Society for Learning Analytics Research

Negin Mirriahi1
Lorenzo Vigentini2

Author Affiliations
1. School of Education & Teaching Innovation Unit, University of South Australia, Australia
2. School of Education & PVC (Education Portfolio), University of New South Wales, Australia

Charles Lang3
George Siemens4
Alyssa Wise5
Dragan Gašević6

Editor Affiliations
3. Teachers College, Columbia University, USA
4. LINK Research Lab, University of Texas at Arlington, USA
5. Learning Analytics Research Network, New York University, USA
6. Schools of Education and Informatics, University of Edinburgh, UK


Register | Lost Password