• 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.

    Article 

    Google Scholar
     

  • Avlonitis, M., & Chorianopoulos, K. (2014). Video pulses: User-based modeling of interesting video segments. Advances in Multimedia, 2014, 2.

    Article 

    Google Scholar
     

  • 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.


    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.


    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • Chorianopoulos, K. (2013). Collective intelligence within web video. Human-centric Computing and Information Sciences, 3(1), 1–16.

    Article 

    Google Scholar
     

  • 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.


    Google Scholar
     

  • 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.


    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.

    Chapter 

    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.

    Chapter 

    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.

    Chapter 

    Google Scholar
     

  • 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.


    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.


    Google Scholar
     

  • Sutcliffe, A., & Hart, J. (2017). Analyzing the role of interactivity in user experience. International Journal of Human-Computer Interaction, 33(3), 229–240.

    Article 

    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • 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.

    Article 

    Google Scholar
     

  • Rights and permissions

    Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

    Disclaimer:

    This article is autogenerated using RSS feeds and has not been created or edited by OA JF.

    Click here for Source link (https://www.springeropen.com/)