Akçapinar, G., Chen, M.-R. A., Majumdar, R., Flanagan, B., & Ogata, H. (2020). Exploring student approaches to learning through sequence analysis of reading logs. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 106–111).
Akçapınar, G., Hasnine, M. N., Majumdar, R., Flanagan, B., & Ogata, H. (2019). Developing an early-warning system for spotting at-risk students by using ebook interaction logs. Smart Learning Environments, 6(1), 4.
Avlonitis, M., & Chorianopoulos, K. (2014). Video pulses: User-based modeling of interesting video segments. Advances in Multimedia, 2014, 2.
Bholowalia, P., & Kumar, A. (2014). EBK-means: A clustering technique based on elbow method and k-means in WSN. International Journal of Computer Applications, 105(9), 17–24.
Boticki, I., Akçapınar, G., & Ogata, H. (2019). E-book user modelling through learning analytics: The case of learner engagement and reading styles. Interactive Learning Environments, 27(5–6), 754–765.
Brinton, C. G., & Chiang, M. (2015). MOOC performance prediction via clickstream data and social learning networks. In 2015 IEEE conference on computer communications (INFOCOM) (pp. 2299–2307). IEEE.
Carlier, A., Ravindra, G., Charvillat, V., & Ooi, W. T. (2011). Combining content-based analysis and crowdsourcing to improve user interaction with zoomable video. In Proceedings of the 19th ACM international conference on multimedia (pp. 43–52).
Chen, C.-H., & Su, C.-Y. (2019). Using the BookRoll e-book system to promote self-regulated learning, self-efficacy and academic achievement for university students. Journal of Educational Technology& Society, 22(4), 33–46.
Chen, C.-H., Yang, S. J., Weng, J.-X., Ogata, H., & Su, C.-Y. (2021). Predicting at-risk university students based on their e-book reading behaviours by using machine learning classifiers. Australasian Journal of Educational Technology, 37, 130–144.
Cheng, K.-H., & Tsai, C.-C. (2014). Children and parents’ reading of an augmented reality picture book: Analyses of behavioral patterns and cognitive attainment. Computers& Education, 72, 302–312.
Chorianopoulos, K. (2013). Collective intelligence within web video. Human-centric Computing and Information Sciences, 3(1), 1–16.
Chorianopoulos, K., Leftheriotis, I., & Gkonela, C. (2011). SocialSkip: Pragmatic understanding within web video. In Proceedings of the 9th European conference on interactive TV and video (pp. 25–28).
Costa, A. L., & Kallick, B. (2008). Learning and leading with habits of mind: 16 essential characteristics for success. ASCD.
Crossley, S., Paquette, L., Dascalu, M., McNamara, D. S., & Baker, R. S. (2016). Combining click-stream data with NLP tools to better understand MOOC completion. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 6–14).
Freeman, R. S., & Saunders, E. S. (2016). E-book reading practices in different subject areas: An exploratory log analysis. In S. M. Ward, R. S. Freeman, & J. M. Nixon (Eds.), Academic E-Books (p. 223). Purdue University Press.
Goda, Y., Yamada, M., Kato, H., Matsuda, T., Saito, Y., & Miyagawa, H. (2015). Procrastination and other learning behavioral types in e-learning and their relationship with learning outcomes. Learning and Individual Differences, 37, 72–80.
Gyllen, J., Stahovich, T., & Mayer, R. (2018). How students read an e-textbook in an engineering course. Journal of Computer Assisted Learning, 34(6), 701–712.
Huang, Y., Yudelson, M., Han, S., He, D., & Brusilovsky, P. (2016). A framework for dynamic knowledge modeling in textbook-based learning. In Proceedings of the 2016 conference on user modeling adaptation and personalization (pp. 141–150).
Junco, R., & Clem, C. (2015). Predicting course outcomes with digital textbook usage data. The Internet and Higher Education, 27, 54–63.
Kim, J., Guo, P. J., Cai, C. J., Li, S.-W., Gajos, K. Z., & Miller, R. C. (2014a). 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).
Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., & Miller, R. C. (2014b). Understanding in-video dropouts and interaction peaks in online lecture videos. In Proceedings of the first ACM conference on learning@ scale conference (pp. 31–40).
Law, E.L.-C., & Lárusdóttir, M. K. (2015). Whose experience do we care about? Analysis of the fitness of Scrum and Kanban to user experience. International Journal of Human-Computer Interaction, 31(9), 584–602.
Li, N., Kidziński, Ł, Jermann, P., & Dillenbourg, P. (2015). MOOC video interaction patterns: What do they tell us? In G. Conole, T. Klobucar, C. Rensing, J. Konert, & E. Lavoué (Eds.), Design for teaching and learning in a networked world (pp. 197–210). Spain: Springer.
Lindsey, R. V., Shroyer, J. D., Pashler, H., & Mozer, M. C. (2014). Improving students’ long-term knowledge retention through personalized review. Psychological Science, 25(3), 639–647.
Liu, D.Y.-T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-driven personalization of student learning support in higher education. In A. Peña-Ayala (Ed.), Learning analytics: Fundaments, applications, and trends (pp. 143–169). Springer.
Lorenzen, S., Hjuler, N., & Alstrup, S. (2018). Tracking behavioral patterns among students in an online educational system. In International educational data mining society.
Lu M, Chen L, Goda Y, Shimada A, Yamada M (2020) In Development of a learning dashboard prototype supporting meta-cognition for students. Companion Proceedings of the 10th International Conference on Learning Analytics \& Knowledge (LAK20), (pp. 104–106)
Ma, B., Chen, J., Li, C., Liu, L., Lu, M., Taniguchi, Y., & Konomi, S. (2020). Understanding jump back behaviors in e-book system. In Companion proceedings of the 10th international conference on learning analytics & knowledge (pp. 623–631).
McKay, D. (2011). A jump to the left (and then a step to the right) reading practices within academic ebooks. In Proceedings of the 23rd Australian computer–human interaction conference (pp. 202–210).
Mostow, J. (2004). Some useful design tactics for mining its data. In Proceedings of the ITS2004 workshop on analyzing student–tutor interaction logs to improve educational outcomes (pp. 20–28).
Myrberg, C. (2017). Why doesn’t everyone love reading e-books? Insights the UKSG Journal, 30(3), 115–126.
Ogata, H., Oi, M., Mohri, K., Okubo, F., Shimada, A., Yamada, M., et al. (2017). Learning analytics for e-book-based educational big data in higher education. In H. Yasuura, C. M. Kyung, Y. Liu, & Y. L. Lin (Eds.), Smart sensors at the IoT Frontier (pp. 327–350). Springer.
Oi, M., Okubo, F., Shimada, A., Yin, C., & Ogata, H. (2015). Analysis of preview and review patterns in undergraduates’ e-book logs. In Proceedings of the 23rd international conference on computers in education (pp. 166–171).
Okubo, F., Yamashita, T., Shimada, A., & Ogata, H. (2017). A neural network approach for students’ performance prediction. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 598–599).
Rainie, L., Zickuhr, K., Purcell, K., Madden, M., & Brenner, J. (2012). The rise of e-reading. Pew Internet & American Life Project.
Ren, Z., Uosaki, N., Kumamoto, E., Liu, G.-Z., & Yin, C. (2017). Improving teaching materials through digital book reading log. In Proceedings of the international conference on advanced technologies enhancing education (pp. 90–96).
Rugg, G., & McGeorge, P. (1997). The sorting techniques: A tutorial paper on card sorts, picture sorts and item sorts. Expert Systems, 14(2), 80–93.
Shepperd, J. A., Grace, J. L., & Koch, E. J. (2008). Evaluating the electronic textbook: Is it time to dispense with the paper text? Teaching of Psychology, 35(1), 2–5.
Shimada, A., Okubo, F., & Ogata, H. (2016). Browsing-pattern mining from e-book logs with non-negative matrix factorization. In EDM (pp. 636–637).
Shin, J. (2012). Analysis on the digital textbook’s different effectiveness by characteristics of learner. International Journal of Education and Learning, 1(2), 23–38.
Sutcliffe, A., & Hart, J. (2017). Analyzing the role of interactivity in user experience. International Journal of Human-Computer Interaction, 33(3), 229–240.
Taniguchi, Y., Shimada, A., Yamada, M., & Konomi, S. (2019). Recommending highlights on students’ e-textbooks. In Society for information technology & teacher education international conference (pp. 1128–1134). Association for the Advancement of Computing in Education (AACE).
Wang, G., Zhang, X., Tang, S., Zheng, H., & Zhao, B. Y. (2016). Unsupervised clickstream clustering for user behavior analysis. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 225–236).
Yadav, K., Shrivastava, K., Mohana Prasad, S., Arsikere, H., Patil, S., Kumar, R., & Deshmukh, O. (2015). Content-driven multi-modal techniques for non-linear video navigation. In Proceedings of the 20th international conference on intelligent user interfaces (pp. 333–344).
Yang, A., Chen, Y., Flanagan, B., & Ogata, H. (2020). Applying key concepts extraction for evaluating the quality of students’ highlights on e-book. In 28th international conference on computers in education conference proceedings (Vol. 1, pp. 284–288). Asia-Pacific Society for Computers in Education (APSCE).
Yin, C., Okubo, F., Shimada, A., Kojima, K., Yamada, M., Fujimura, N., & Ogata, H. (2014). Smart phone based data collecting system for analyzing learning behaviors. In International conference on computer in education (ICCE 2014) (pp. 575–577).
Yin, C., Okubo, F., Shimada, A., Oi, M., Hirokawa, S., & Ogata, H. (2015a). Identifying and analyzing the learning behaviors of students using e-books. In Proceedings of the 23rd international conference on computers in education (pp. 118–120). Asia-Pacific Society for Computers in Education Hangzhou, China.
Yin, C., Okubo, F., Shimada, A., Oi, M., Hirokawa, S., Yamada, M., Kojima, K., & Ogata, H. (2015b). Analyzing the features of learning behaviors of students using e-books. In Proceedings of the international conference on computers in education (pp. 617–626).
Yin, C., Yamada, M., Oi, M., Shimada, A., Okubo, F., Kojima, K., & Ogata, H. (2019). Exploring the relationships between reading behavior patterns and learning outcomes based on log data from e-books: A human factor approach. International Journal of Human-Computer Interaction, 35(4–5), 313–322.
Zhang, H., Sun, M., Wang, X., Song, Z., Tang, J., & Sun, J. (2017). Smart jump: Automated navigation suggestion for videos in MOOCS. In Proceedings of the 26th international conference on world wide web companion (pp. 331–339).
Zhou, Z.-J., Hu, C.-H., Zhang, B.-C., Xu, D.-L., & Chen, Y.-W. (2013). Hidden behavior prediction of complex systems based on hybrid information. IEEE Transactions on Cybernetics, 43(2), 402–411.