Weitere Artikel dieser Ausgabe durch Wischen aufrufen
This study examines the relationship between log data of student activity in learning management systems and self-reported student engagement survey scores. Log data has the potential to serve as a meaningful proxy for survey scores. Should this be the case, log data could be used as a minimally disruptive and scalable approach to quickly identify who needs help, evaluate design, and personalize instruction. We correlated LMS log data variables to student engagement survey scores to study the relationship between these two sources of data. Overall, log data was not a statistically significant proxy measure of students’ self-reported cognitive and emotional engagement. Our results underscore the complexity of learning and the relationship between observed and reported cognitive and emotional states. Future educational research using log data will need to account for other factors that help explain trends in student engagement. Exploring the Potential of LMS Log Data as a Proxy Measure of Student Engagement.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Baker, R. S. J., Gowda, S. M., Wixon, M., Kalka, J., Wagner, A. Z., Aleven, V., Rossi, L. et al. (2012). Towards sensor-free affect detection in cognitive tutor algebra. In K. Yacef, O. Zaïane, H. Hershkovitz, M. Yudelson, & J. Stamper (Eds.), Proceedings of the 5th international conference on educational data mining (pp. 126–133), Chania. Retrieved April 27, 2015 from http://educationaldatamining.org/EDM2012/index.php?page=proceedings.
Beer, C., Clark, K., & Jones, D. (2010). Indicators of engagement. In Proceedings of ASCILITE 2010 (pp. 75–86), Sydney. Retrieved November 15, 2015 from http://ascilite.org.au/conferences/sydney10/procs/Beer-full.pdf.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300. Retrieved from http://www.jstor.org.byui.idm.oclc.org/stable/2346101.
Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington, DC: SRI International. Retrieved from http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf.
Bodily, R. G., Graham, C. R., & Bush, M. D. (2017). Online learner engagement: Opportunities and challenges with using data analytics. Educational Technology, 57(1), 10–18.
Dixson, M. D. (2010). Creating effective student engagement in online courses: What do students find engaging? Journal of the Scholarship of Teaching and Learning, 10(2), 1–13. Retrieved November 15, 2015 from http://josotl.indiana.edu/.
Drutsa, A., & Serdyukov, P. (2015). Future user engagement prediction and its application to improve the sensitivity of online experiments. In WWW 2015 (pp. 256–266), Florence. doi: 10.1145/2736277.2741116.
Finn, J. D., & Owings, J. (2006). The adult lives of at-risk students: The roles of attainment and engagement in high school. Statistical Analysis Report (NCES 2006-328). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Retrieved November 15, 2015 from http://eric.ed.gov/?id=ED491285.
Fredricks, J. A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School engagement. In K. A. Moore & L. Lippman (Eds.), What do children need to flourish? Conceptualizing and measuring indicators of positive development (pp. 305–321). New York, NY: Kluwer Academic/Plenum Press. doi: 10.1007/0-387-23823-9_19. CrossRef
Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 763–782). Boston, MA: Springer. doi: 10.1007/978-1-4614-2018-7_37. CrossRef
Fredricks, J., McColskey, W., Meli, J., Mordica, J., Montrosse, B., & Mooney, K. (2011). Measuring student engagement in upper elementary through high school: A description of 21 instruments. (Issues & Answers Report, REL 2011–No. 098). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southeast. Retrieved April 27, 2015 from http://ies.ed.gov/ncee/edlabs/regions/southeast/pdf/REL_2011098.pdf.
Halverson, L.R., & Graham, C. R. (2015). Learner engagement in blended learning environments ( Submitted for publication).
Halverson, L., Spring, K., Huyett, S., Henrie, C., & Graham, C. (2016). Blended learning research in higher education and K-12 settings. In J. M. Spector, B. B. Lockee, & M. D. Childress (Eds.), Learning, design, and technology: An international compendium of theory, research, practice and policy. Berlin: Springer. doi: 10.1007/978-3-319-17727-4_31-1.
Henrie, C., Bodily, R., Manwaring, K., & Graham, C. (2015). Exploring intensive longitudinal measures of student engagement in blended learning. The International Review of Research in Open and Distributed Learning, 16(3), 131–155. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/2015.
Henrie, C. R., Larsen, R., Manwaring, K., Halverson, L. R., & Graham, C. R. (2016). Validation of a longitudinal activity-level measure of student engagement ( Submitted for publication).
Hollands, F., & Bakir, I. (2015). Efficiency of automated detectors of learner engagement and affect compared with traditional observation methods. New York, NY: Center for Benefit-Cost Studies of Education, Teachers College, Columbia University. Retrieved November 15, 2015 from http://cbcse.org/
Janosz, M. (2012). Part IV commentary: Outcomes of engagement and engagement as an outcome: Some consensus, divergences, and unanswered questions. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 695–703). Boston, MA: Springer. doi: 10.1007/978-1-4614-2018-7_33. CrossRef
Jordan, K. (2014). Initial trends in enrollment and completion of massive open online courses. The International Review of Research in Open and Distance Learning, 15(1), 133–159. Retrieved April 27, 2015 from http://www.irrodl.org/index.php/irrodl/article/view/1651.
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. Retrieved April 27, 2015 from http://www.jstor.org/stable/40165768.
Prince, S. A., Adamo, K. B., Hamel, M. E., Hardt, J., Gorber, S. C., & Tremblay, M. (2008). A comparison of direct versus self-report measures for physical activity in adults: A systematic review. International Journal of Behavioral Nutrition and Physical Activity. doi: 10.1186/1479-5868-5-56.
Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 3–20). New York, NY: Springer. doi: 10.1007/978-1-4614-2018-7_1. CrossRef
Russell, J., Ainley, M., & Frydenberg, E. (2005). Schooling issues digest: Student motivation and engagement (pp. 1–16), Canberra, AU. Retrieved April 27, 2015 from http://docs.education.gov.au
Skinner, E. A., Kindermann, T. A., & Furrer, C. J. (2009). A motivational perspective on engagement and disaffection: Conceptualization and assessment of children’s behavioral and emotional participation in academic activities in the classroom. Educational and Psychological Measurement, 69, 493–525. doi: 10.1177/0013164408323233. CrossRef
Watson, J., Pape, L., Murin, A., Gemin, B., & Vashaw, L. (2014). Keeping pace with K- 12 digital learning. Retrieved April 27, 2015 from http://www.kpk12.com/reports/.
- Exploring the potential of LMS log data as a proxy measure of student engagement
Curtis R. Henrie
Charles R. Graham
- Springer US
Neuer Inhalt/© ITandMEDIA