Educational Technology International

  • 제18권2호
  • /
  • Pages.73-99
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  • 2017
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  • 1598-5776(pISSN)
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  • 2289-0300(eISSN)
한국교육공학회 (The Korean Society for Education Technology)
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Interaction of Learning Motivation with Dashboard Intervention and Its Effect on Learning Achievement

  • 투고 : 2017.08.24
  • 심사 : 2017.10.05
  • 발행 : 2017.10.30
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초록

The learning analytics dashboard (LAD) is a supporting tool for teaching and learning in its personalized, automatic, and visual aspects. While several studies have focused on the effect of using dashboard on learning achievement, there is a research gap concerning the impacts of learners' characteristics on it. Accordingly, this study attempted to verify the differences in learning achievement depending on learning motivation level (high vs. low) and dashboard intervention (use vs. non-use). The final participants were 231 university students enrolled in a basic statistics course. As a research design, a 2 × 2 factorial design was employed. The results showed that learning achievement varied with dashboard intervention and the interaction effect was significant between learning motivation and dashboard intervention. The results imply that the impact of LAD may vary depending on learner characteristics. Consequently, this study suggests that the dashboard interventions should be offered after careful consideration of individual students' differences, particularly their learning motivation.

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과제정보

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A5B6036244).

참고문헌

  1. Ali, L., Hatala, M., Gasevic, D., & Jovanovic, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470-489.
  2. Amrai, K., Motlagh, S. E., Zalani, H. A., & Parhon, H. (2011). The relationship between academic motivation and academic achievement students. Procedia-Social and Behavioral Sciences, 15, 399-402.
  3. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 267-270.
  4. Broussard, S. C., & Garrison, M. (2004). The relationship between classroom motivation and academic achievement in elementary-school-aged children. Family and Consumer Sciences Research Journal, 33(2), 106-120.
  5. Brouwer, A. M., Zander, T. O., van Erp, J. B., Korteling, J. E., & Bronkhorst, A. W. (2015). Using neurophysiological signals that reflect cognitive or affective state: Six recommendations to avoid common pitfalls. Frontiers in Neuroscience, 9, 136. doi:10.3389/fnins.2015.00136 [doi]
  6. Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: Using vision to think Morgan Kaufmann.
  7. Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thus, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331.
  8. Corrin, L., & de Barba, P. (2014). Exploring students' interpretation of feedback delivered through learning analytics dashboards. Proceedings of the Ascilite 2014 Conference, 629-633.
  9. Corrin, L., & de Barba, P. (2015). How do students interpret feedback delivered via dashboards? Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, 430-431.
  10. Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. Irvington.
  11. Dirican, A. C., & Gokturk, M. (2011). Psychophysiological measures of human cognitive states applied in human computer interaction. Procedia Computer Science, 3, 1361-1367.
  12. Dollar, A., & Steif, P. S. (2012). Web-based statics course with learning dashboard for instructors. Proceedings of Computers and Advanced Technology in Education (CATE 2012),
  13. Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317.
  14. Field, A., & Hole, G. (2002). How to design and report experiments Sage.
  15. Grann, J., & Bushway, D. (2014). Competency map: Visualizing student learning to promote student success. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, 168-172.
  16. Hernandez-Garcia, A., Gonzalez-Gonzalez, I., Jimenez-Zarco, A. I., & Chaparro-Pelaez, J. (2015). Applying social learning analytics to message boards in online distance learning: A case study. Computers in Human Behavior, 47, 68-80.
  17. Jo, I. H., Ha, K., & Park, Y. (2015). Measuring Information Perception in Learning Analytics Dashboard: Use of Eye-Tracking System. The Journal of Educational Information and Media, 21(3), 441-469.
  18. Jonassen, D., & Grabowski, B. (1993). Individual differences and instruction. New York: Allen & Bacon.
  19. Kahneman, D. (2011). Thinking, fast and slow Macmillan.
  20. Kerly, A., Ellis, R., & Bull, S. (2008). CALMsystem: A conversational agent for learner modelling. Knowledge-Based Systems, 21(3), 238-246.
  21. Kosba, E., Dimitrova, V., & Boyle, R. (2005). Using student and group models to support teachers in web-based distance education. International Conference on User Modeling, 124-133.
  22. Lievens, F., Coetsier, P., De Fruyt, F., & De Maeseneer, J. (2002). Medical students' personality characteristics and academic performance: A five-factor model perspective. Medical Education, 36(11), 1050-1056.
  23. Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90-97.
  24. Maier, U., Wolf, N., & Randler, C. (2016). Effects of a computer-assisted formative assessment intervention based on multiple-tier diagnostic items and different feedback types. Computers & Education, 95, 85-98.
  25. Malik, S. (2005). Enterprise dashboards: Design and best practices for IT John Wiley & Sons.
  26. Melero, J., Hernandez-Leo, D., Sun, J., Santos, P., & Blat, J. (2015). How was the activity? A visualization support for a case of location-based learning design. British Journal of Educational Technology, 46(2), 317-329.
  27. O'Connor, M. C., & Paunonen, S. V. (2007). Big five personality predictors of post-secondary academic performance. Personality and Individual Differences, 43(5), 971-990.
  28. O'Donoghue, T., & Rabin, M. (2003). Self-awareness and self-control, time and decision: Economic and psychological perspectives on intertemporal choice.
  29. Park, Y., & Jo, I. H. (2015). Development of the Learning Analytics Dashboard to Support Students' Learning Performance. J. UCS, 21(1), 110-133.
  30. Park, Y., & Jo, I. H. (2014). Design and Application of Visual Dashboard Based on Learning Analytics. The Journal of Educational Information and Media, 20(2), 191-216.
  31. Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667.
  32. Pintrich, P. R, & Schunk, D. (2002). Motivation in education: Theory, research, and application. Columbus, OH: Merrill Prentice Hall.
  33. Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire(MSLQ). Educational and Psychological Measurement, 53(3), 801-813.
  34. Poon, L. K., Kong, S., Yau, T. S., Wong, M., & Ling, M. H. (2017). Learning analytics for monitoring students participation online: Visualizing navigational patterns on learning management system. International Conference on Blended Learning, 166-176.
  35. Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135, 322.
  36. Pressler, E. J. (2014). Logging in to learning analytics. Current Issues in Emerging eLearning, 1(1), 6.
  37. Rodriguez-Triana, M. J., Martinez-Mones, A., Asensio-Perez, J. I., & Dimitriadis, Y. (2015). Scripting and monitoring meet each other: Aligning learning analytics and learning design to support teachers in orchestrating CSCL situations. British Journal of Educational Technology, 46(2), 330-343.
  38. Rodriguez-Triana, M. J., Prieto, L. P., Vozniuk, A., Boroujeni, M. S., Schwendimann, B. A., Holzer, A., & Gillet, D. (2017). Monitoring, awareness and reflection in blended technology enhanced learning: A systematic review. International Journal of Technology Enhanced Learning, 9(2-3), 126-150.
  39. Schiefele, U., & Rheinberg, F. (1997). Motivation and knowledge acquisition: Searching for mediating processes.
  40. Seligman, C., & Darley, J. M. (1977). Feedback as a means of decreasing residential energy consumption. Journal of Applied Psychology, 62(4), 363.
  41. Shin, M. (1998). Promoting students' self-regulation ability: Guidelines for instructional design. Educational Technology, 38(1), 38-44.
  42. Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S. P., Shum, S., Ferguson, R., Baker, R. (2011). Open learning analytics: An integrated & modularized platform.
  43. Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30.
  44. Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses. Journal of Asynchronous Learning Networks, 16(3), 51-61.
  45. Spann, C. A., Schaeffer, J., & Siemens, G. (2017). Expanding the scope of learning analytics data: Preliminary findings on attention and self-regulation using wearable technology. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 203-207.
  46. Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500-1509.
  47. Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18(6), 1499-1514.
  48. Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Journal of Educational Technology & Society, 15(3), 133.
  49. Zimmerman, B. J., & Kitsantas, A. (1999). Acquiring writing revision skill: Shifting from process to outcome self-regulatory goals. Journal of Educational Psychology, 91(2), 241.