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Blended Learning als Spielfeld für Learning Analytics und Educational Data Mining

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Digitale Kompetenz

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Zusammenfassung

Der Einsatz digitaler Lernformate im Blended Learning bietet demnach Chancen in mindestens zwei Bereichen. Zum einen können digitale Lernformate direkt die Lernprozesse von Studierenden günstig beeinflussen, ihre Leistungen verbessern und zudem positive Effekte auf vielen weiteren Ebenen wie der Motivation oder des Selbstkonzeptes bewirken. Zum anderen generieren digitale Lernformate eine Fülle von Daten in vielfältiger Gestalt. Studierende erzeugen bei der Arbeit mit digitalen Werkzeugen Nutzungsdaten, wie Verweildauern und Aktivitätsprofile, sie produzieren Leistungsdaten aus digitalen Aufgaben, sie hinterlassen Textbeiträge in Foren und Chats. All diese Daten können genutzt werden, um mit Methoden von Learning Analytics (LA) und Educational Data Mining (EDM) zu analysieren, Schlüsse und Vorhersagen über studentisches Lernverhalten zu ziehen und die Lernangebote entsprechend zu optimieren.

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Literatur

  • Abeysekera, L., & Dawson, P. (2015). Motivation and cognitive load in the flipped classroom: Definition, rationale and a call for research. Higher Education Research & Development, 34(1), 1–14.

    Google Scholar 

  • Appana, S. (2008). A review of benefits and limitations of online learning in the context of the student, the instructor, and the tenured faculty. International Journal on E-Learning, 7(1), 5.

    Google Scholar 

  • Arnold, K. (2010). Signals: Applying academic analytics. EDUCAUSE Quarterly, 33(1), 11–18.

    Google Scholar 

  • Banks, D. (Hrsg.). (2006). Audience response systems in higher education: Applications and cases. Pennsylvania: IGI Global.

    Google Scholar 

  • Basu Roy, R., & McMahon, G. T. (2012). Video-based cases disrupt deep critical thinking in problem-based learning. Medical Education, 46(4), 426–435.

    Google Scholar 

  • Beckwith, L., Kissinger, C., Burnett, M., Wiedenbeck, S., Lawrance, J., Blackwell, A., & Cook, C. (2006). Tinkering and gender in end-user programmers’ debugging. In Proceedings of the SIGCHI conference on human factors in computing systems (S. 231–240). New York: ACM.

    Google Scholar 

  • Berk, R. A. (2009). Multimedia teaching with video clips: TV, movies, YouTube, and mtvU in the college classroom. International Journal of Technology in Teaching and Learning, 5(1), 1.

    Google Scholar 

  • Biasutti, M. (2017). A comparative analysis of forums and wikis as tools for online collaborative learning. Computers & Education, 111, 158–171.

    Google Scholar 

  • Bliuc, A. M., Goodyear, P., & Ellis, R. A. (2007). Research focus and methodological choices in studies into students’ experiences of blended learning in higher education. The Internet and Higher Education, 10(4), 231–244.

    Google Scholar 

  • Bodily, R., & Verbert, K. (2017). Trends and issues in student-facing learning analytics reporting systems research. In: Proceedings of the seventh international learning analytics & knowledge conference (S. 309–318). New York: ACM.

    Google Scholar 

  • Bolliger, D. U., Supanakorn, S., & Boggs, C. (2010). Impact of podcasting on student motivation in the online learning environment. Computers & Education, 55(2), 714–722.

    Google Scholar 

  • Bos, N. R. (2016). Effectiveness of blended learning: Factors facilitating effective behavior in a blended learning environment. Maastricht: Doctoral Dissertation.

    Google Scholar 

  • Bramucci, R., & Gaston, J. (2012). Sherpa: Increasing student success with a recommendation engine. In Proceedings of the 2nd international conference on learning analytics and knowledge (S. 82–83) (April 2012). New York: ACM.

    Google Scholar 

  • Brecht, H. D. (2012). Learning from online video lectures. Journal of Information Technology Education, 11, 227–250.

    Google Scholar 

  • Burnett, M. M., Beckwith, L., Wiedenbeck, S., Fleming, S. D., Cao, J., Park, T. H., … & Rector, K. (2011). Gender pluralism in problem-solving software☆. Interacting with Computers, 23(5), 450–460.

    Google Scholar 

  • Caldwell, J. E. (2007). Clickers in the large classroom: Current research and best-practice tips. CBE-Life sciences education, 6(1), 9–20.

    Google Scholar 

  • Cambruzzi, W. L., Rigo, S. J., & Barbosa, J. L. (2015). Dropout prediction and reduction in distance education courses with the learning analytics multitrail approach. Journal of Universal Computer Science, 21(1), 23–47.

    Google Scholar 

  • Cardall, S., Krupat, E., & Ulrich, M. (2008). Live lecture versus video-recorded lecture: Are students voting with their feet? Academic Medicine, 83(12), 1174–1178.

    Google Scholar 

  • 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.

    Google Scholar 

  • Chen, Y. T. (2012). The effect of thematic video-based instruction on learning and motivation in e-learning. International Journal of Physical Sciences, 7(6), 957–965.

    Google Scholar 

  • Choi, I., Lee, S. J., & Jung, J. W. (2008). Designing multimedia case-based instruction accommodating students’ diverse learning styles. Journal of Educational Multimedia and Hypermedia, 17(1), 5.

    Google Scholar 

  • Clark, R. C., Nguyen, F., & Sweller, J. (2011). Efficiency in learning: Evidence-based guidelines to manage cognitive load. San Francisco: Wiley.

    Google Scholar 

  • Cole, M. (2009). Using Wiki technology to support student engagement: Lessons from the trenches. Computers & Education, 52(1), 141–146.

    Google Scholar 

  • Copley, J. (2007). Audio and video podcasts of lectures for campus-based students: Production and evaluation of student use. Innovations in Education and Teaching International, 44(4), 387–399.

    Google Scholar 

  • Dierenfeld, H., & Merceron, A. (2012). Learning analytics with excel pivot tables. https://research.moodle.net/56/1/15%20-%20Dierenfeld%20-%20Learning%20Analytics%20with%20Excel%20Pivot%20Tables.pdf. Zugegriffen am 22.05.2018.

  • Dringus, L. P., & Seagull, A. B. (2013). A five-year study of sustaining blended learning initiatives to enhance academic engagement in computer and information sciences campus courses. In Blended learning research perspectives (Bd. 2). New York: Taylor & Francis.

    Google Scholar 

  • Ebner, M., & Schön, S. (2011). Lern-und Lehrvideos: Gestaltung, Produktion, Einsatz. In Handbuch E-Learning (Bd. 71). Erg. Lfg. Köln: Deutscher Wirtschaftsdienst (Wolters Kluwer Deutschland).

    Google Scholar 

  • Fortenbacher, A., Pinkwart, N., & Yun, H. (2017). [LISA] Learning analytics for sensor-based adaptive learning. In LAK ’17 – Proceedings of the seventh international learning analytics & knowledge conference (S. 592–593). New York: ACM.

    Google Scholar 

  • Fournier, H., Kop, R., & Sitlia, H. (2011). The value of learning analytics to networked learning on a personal learning environment. In Proceedings of the 1st international conference on learning analytics and knowledge (S. 104–109). New York: ACM.

    Google Scholar 

  • Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105.

    Google Scholar 

  • Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012). The student activity meter for awareness and self-reflection. In CHI’12 on human factors in computing systems (S. 869–884). New York: Association for Computing Machinery.

    Google Scholar 

  • Graham, C. R., Tripp, T. R., Seawright, L., & Joeckel, G. (2007). Empowering or compelling reluctant participators using audience response systems. Active Learning in Higher Education, 8(3), 233–258.

    Google Scholar 

  • Hill, J. L., & Nelson, A. (2011). New technology, new pedagogy? Employing video podcasts in learning and teaching about exotic ecosystems. Environmental Education Research, 17(3), 393–408.

    Google Scholar 

  • Homer, B. D., Plass, J. L., & Blake, L. (2008). The effects of video on cognitive load and social presence in multimedia-learning. Computers in Human Behavior, 24(3), 786–797.

    Google Scholar 

  • Homme, J., Asay, G., & Morgenstern, B. (2004). Utilisation of an audience response system. Medical Education, 38(5), 575–575.

    Google Scholar 

  • Horn, M. B., & Staker, H. (2014). Blended: Using disruptive innovation to improve schools. New York: Wiley.

    Google Scholar 

  • Ifenthaler, D. (2017a). Are higher education institutions prepared for learning analytics? TechTrends, 61(4), 366–371.

    Google Scholar 

  • Ifenthaler, D. (2017b). Studienerfolg mittels Learning Analytics – Handlungsempfehlungen für deutsche Hochschulen. https://www.wihoforschung.de/de/stela-1328. Zugegriffen am 18.02.2018.

  • Ivančević, V., Knežević, M., Pušić, B., & Luković, I. (2014). Adaptive testing in programming courses based on educational data mining techniques. In Educational data mining (S. 257–287). Cham: Springer.

    Google Scholar 

  • Jordan, J. T., Box, M. C., Eguren, K. E., Parker, T. A., Saraldi-Gallardo, V. M., Wolfe, M. I., & Gallardo-Williams, M. T. (2015). Effectiveness of student-generated video as a teaching tool for an instrumental technique in the organic chemistry laboratory. Journal of Chemical Education, 93(1), 141–145.

    Google Scholar 

  • Karasavvidis, I. (2010). Wiki uses in higher education: Exploring barriers to successful implementation. Interactive Learning Environments, 18(3), 219–231.

    Google Scholar 

  • Kay, R. H., & LeSage, A. (2009). Examining the benefits and challenges of using audience response systems: A review of the literature. Computers & Education, 53(3), 819–827.

    Google Scholar 

  • Kearney, M. (2011). A learning design for student-generated digital storytelling. Learning, Media and Technology, 36(2), 169–188.

    Google Scholar 

  • Kearney, M., & Schuck, S. (2006). Spotlight on authentic learning: Student developed digital video projects. Australasian Journal of Educational Technology, 22(2), 189–208.

    Google Scholar 

  • Khribi, M. K., Jemni, M., & Nasraoui, O. (2008). Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. In Advanced learning technologies, 2008. ICALT’08. Eighth IEEE international conference (S. 241–245). Chicago.

    Google Scholar 

  • Kim, W. (2007). Towards a definition and methodology for blended learning. In The proceedings of workshop on blended learning (S. 1–8). Edinburgh: Pearson.

    Google Scholar 

  • Knoblauch, H., & Schnettler, B. (2012). Videography: Analysing video data as a ‚focused‘ ethnographic and hermeneutical exercise. Qualitative Research, 12(3), 334–356.

    Google Scholar 

  • Kotsiantis, S. B., Pierrakeas, C. J., & Pintelas, P. E. (2003). Preventing student dropout in distance learning using machine learning techniques. In International conference on knowledge-based and intelligent information and engineering systems (S. 267–274). Berlin: Springer.

    Google Scholar 

  • Kuh, G. D. (2001). Assessing what really matters to student learning inside the national survey of student engagement. Change: The Magazine of Higher Learning, 33(3), 10–17.

    Google Scholar 

  • Kukulska-Hulme, A., Foster-Jones, J., Jelfs, A., Mallett, E., & Holland, D. (2004). Investigating digital video applications in distance learning. Journal of Educational Media, 29(2), 125–137.

    Google Scholar 

  • Kumar, A., Kumar, P., & Basu, S. C. (2001). Student perceptions of virtual education: An exploratory study. In: Proceedings of 2001 information resources management association international conference (S. 400–403). Toronto.

    Google Scholar 

  • de Laat, M., Lally, V., Lipponen, L., & Simons, R. J. (2007). Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for social network analysis. International Journal of Computer-Supported Collaborative Learning, 2(1), 87–103.

    Google Scholar 

  • Lai, C., Lei, C., & Liu, Y. (2016). The nature of collaboration and perceived learning in wiki-based collaborative writing. Australasian Journal of Educational Technology, 32(3), 80–96.

    Google Scholar 

  • Lang, C., Siemens, G., Wise, A., & Grasevic, D. (2017). The handbook of learning analytics. Society for Learning Analytics Research. https://doi.org/10.18608/hla17.

  • Li, N., Cohen, W., Koedinger, K. R., & Matsuda, N. (2011). A machine learning approach for automatic student model discovery. In Proceedings of the 4th international conf on educational data mining (S. 31–40). Eindhoven: EDM.

    Google Scholar 

  • Li, N., Verma, H., Skevi, A., Zufferey, G., & Dillenbourg, P. (2014). MOOC learning in spontaneous study groups: Does synchronously watching videos make a difference? In Proceedings of the European MOOC Stakeholder Summit 2014 (No. EPFL-CONF-196608) (S. 88–94). Barcelona: PAU Education.

    Google Scholar 

  • Li, N., Kidziński, Ł., Jermann, P., & Dillenbourg, P. (2015). MOOC video interaction patterns: What do they tell us? In Design for teaching and learning in a networked world (S. 197–210). Switzerland: Springer International Publishing.

    Google Scholar 

  • Manso-Vázquez, M., & Llamas-Nistal, M. (2015). A monitoring system to ease self-regulated learning processes. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 10(2), 52–59.

    Google Scholar 

  • Meisel, S. (1998). Videotypes: Considerations for effective use of video in teaching and training. Journal of Management Development, 17(4), 251–258.

    Google Scholar 

  • Mödritscher, F. (2006). E-learning theories in practice: A comparison of three methods. Journal of Universal Science and Technology of Learning, 28, 3–18.

    Google Scholar 

  • Nelson, C., Hartling, L., Campbell, S., & Oswald, A. E. (2012). The effects of audience response systems on learning outcomes in health professions education. A BEME systematic review: BEME Guide No. 21. Medical Teacher, 34(6), e386–e405.

    Google Scholar 

  • Nussbaumer, A., Hillemann, E. C., Gütl, C., & Albert, D. (2015). A competence-based service for supporting self-regulated learning in virtual environments. Journal of Learning Analytics, 2(1), 101–133.

    Google Scholar 

  • Orús, C., Barlés, M. J., Belanche, D., Casaló, L., Fraj, E., & Gurrea, R. (2016). The effects of learner-generated videos for YouTube on learning outcomes and satisfaction. Computers & Education, 95, 254–269.

    Google Scholar 

  • 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.

    Google Scholar 

  • Pardo, A., Mirriahi, N., Dawson, S., Zhao, Y., Zhao, A., & Gašević, D. (2015). Identifying learning strategies associated with active use of video annotation software. In Proceedings of the fifth international conference on learning analytics and knowledge (S. 255–259). New York: ACM.

    Google Scholar 

  • Rabbany, R., Takaffoli, M., & Zaïane, O. R. (2011). Analyzing participation of students in online courses using social network analysis techniques. In Proceedings of the 4th international conference on educational data mining (S. 21–30). Eindhoven.

    Google Scholar 

  • Ravi, G. A., & Sosnovsky, S. (2013). Exercise difficulty calibration based on student log mining. In Proceedings of DAILE, 13. Leon.

    Google Scholar 

  • Reamer, A. C., Ivy, J. S., Vila-Parrish, A. R., & Young, R. E. (2015). Understanding the evolution of mathematics performance in primary education and the implications for STEM learning: A Markovian approach. Computers in Human Behavior, 47, 4–17.

    Google Scholar 

  • Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. (Hrsg.). (2010). Handbook of educational data mining. Boca Raton: CRC press.

    Google Scholar 

  • Ronchetti, M. (2010). Perspectives of the application of video streaming to education. In Streaming media architectures, techniques, and applications: Recent advances (S. 411). Toronto: ED-MEDIA.

    Google Scholar 

  • Ryan, B. (2013). A walk down the red carpet: Students as producers of digital video-based knowledge. International Journal of Technology Enhanced Learning, 5, 24–41. Geneva: Inderscience Publishers.

    Google Scholar 

  • Sage, K. (2014). What pace is best? Assessing adults’ learning from slideshows and video. Journal of Educational Multimedia and Hypermedia, 23(1), 91–108.

    Google Scholar 

  • Sahasrabudhe, V., & Kanungo, S. (2014). Appropriate media choice for e-learning effectiveness: Role of learning domain and learning style. Computers & Education, 76, 237–249.

    Google Scholar 

  • Scheffel, M., Niemann, K., Leony, D., …, & Kloos, C. D. (2012). Key action extraction for learning analytics. In Conference on technology enhanced learning (S. 320–333). Berlin: Springer.

    Google Scholar 

  • Schmidt-Weigand, F., Kohnert, A., & Glowalla, U. (2010). A closer look at split visual attention in system-and self-paced instruction in multimedia learning. Learning and Instruction, 20(2), 100–110.

    Google Scholar 

  • Schön, S., & Ebner, M. (Hrsg.). (2013). Lehrbuch für Lernen und Lehren mit Technologien (2. Aufl).

    Google Scholar 

  • Schuck, S., & Kearney, M. (2006). Capturing learning through student-generated digital video. Australian Educational Computing, 21(1), 15–20.

    Google Scholar 

  • Scott, J. (2017). Social network analysis. Thousand Oaks: Sage.

    Google Scholar 

  • Sharma, K., Mangaroska, K., Trætteberg, H., Lee-Cultura, S., & Giannakos, M. (2018). Evidence for programming strategies in university coding exercises. In European Conference on Technology Enhanced Learning (S. 326–339). Cham: Springer.

    Google Scholar 

  • Sherer, P., & Shea, T. (2011). Using online video to support student learning and engagement. College Teaching, 59(2), 56–59.

    Google Scholar 

  • Siemens, G., & d Baker, R. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (S. 252–254). New York: ACM.

    Google Scholar 

  • Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.

    Google Scholar 

  • So, H.-J., & Brush, T. A. (2008). Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors. Computers & Education, 51(1), 318–336.

    Google Scholar 

  • Sonnleitner, M., Prock, S., Kirchhoff, P., & Rank, A. (Hrsg.). (2018). Video-und Audiografie von Unterricht in der LehrerInnenbildung: Planung und Durchführung aus methodologischer, technisch-organisatorischer, ethisch-datenschutzrechtlicher und inhaltlicher Perspektive. Stuttgart: UTB GmbH.

    Google Scholar 

  • Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning Analytics in a data-rich context. Computers in Human Behavior, 47, 157–167.

    Google Scholar 

  • Thomas, E. H., & Galambos, N. (2004). What satisfies students? Mining student-opinion data with regression and decision tree analysis. Research in Higher Education, 45(3), 251–269.

    Google Scholar 

  • Traphagan, T., Kucsera, J. V., & Kishi, K. (2010). Impact of class lecture webcasting on attendance and learning. Educational Technology Research and Development, 58(1), 19–37.

    Google Scholar 

  • Wolff, A., Zdrahal, Z., Nikolov, A., & Pantucek, M. (2013). Improving retention: Predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In Proceedings of the third international conference on learning analytics and knowledge (S. 145–149). New York: ACM.

    Google Scholar 

  • Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker, J. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & Management, 43(1), 15–27.

    Google Scholar 

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  1. Johannes Gutenberg-Universität, Mainz, Deutschland

    Malte Persike

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  1. University of Digital Science (UDS Berlin), Berlin, Germany

    Mike Friedrichsen

  2. University of Digital Science (UDS Berlin), Berlin, Germany

    Wulf Wersig

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Persike, M. (2020). Blended Learning als Spielfeld für Learning Analytics und Educational Data Mining. In: Friedrichsen, M., Wersig, W. (eds) Digitale Kompetenz. Synapsen im digitalen Informations- und Kommunikationsnetzwerk. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-22109-6_12

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