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Student-Centered Design and Evaluation of a Learning Analytics Dashboard

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Digital Economy. Emerging Technologies and Business Innovation (ICDEc 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 485))

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Abstract

The digitization of teaching at universities has increased significantly in recent years, with online and hybrid courses becoming more popular. These formats allow students a high degree of autonomy, but also require them to work independently and organize themselves. However, students often lack these skills. Learning analytics (LA) evaluations, provided as dashboards, can help students to continuously monitor their learning progress and compare themselves to their peers. Nevertheless, the student perspective has often been underrepresented in LA research. There is also a lack of standardized knowledge and processes for implementing LA and making LA information available to end users. This paper aims to develop and evaluate a LA dashboard for a university course based on the requirements of the students, using data from a university’s learning management and examination system. Three dashboard versions are designed and evaluated quantitatively and qualitatively in a study with 114 participants. The results will be discussed, along with limitations and potential future research directions.

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References

  1. Zhao, Y., Watterston, J.: The changes we need: education post COVID-19. J. Educ. Change 22, 3–12 (2021)

    Article  Google Scholar 

  2. Kim, M.K., Kim, S.M., Khera, O., Getman, J.: The experience of three flipped classrooms in an urban university: an exploration of design principles. Internet High. Educ. 22, 37–50 (2014)

    Article  Google Scholar 

  3. Lai, C.-L., Hwang, G.-J.: A self-regulated flipped classroom approach to improving students’ learning performance in a mathematics course. Comput. Educ. 100, 126–140 (2016)

    Article  Google Scholar 

  4. Mason, G.S., Shuman, T.R., Cook, K.E.: Comparing the effectiveness of an inverted classroom to a traditional classroom in an upper-division engineering course. IEEE Trans. Educ. 56, 430–435 (2013)

    Article  Google Scholar 

  5. Ferguson, R.: Learning analytics: drivers, developments and challenges. Int. J. Technol. Enhanc. Learn. 4, 304–317 (2012)

    Article  Google Scholar 

  6. Mazza, R., Dimitrova, V.: Visualising student tracking data to support instructors in web- based distance education. In: 13th International World Wide Web, pp. 154–161 (2004)

    Google Scholar 

  7. Jovanović, J., Gašević, D., Dawson, S., Pardo, A., Mirriahi, N.: Learning analytics to unveil learning strategies in a flipped classroom. Internet High. Educ. 33, 74–85 (2017)

    Article  Google Scholar 

  8. Long, P., Siemens, G.: Penetrating the fog: analytics in learning and education. Educ. Rev. 46, 31–40 (2011)

    Google Scholar 

  9. Greller, W., Drachsler, H.: Translating learning into numbers: a generic framework for learning analytics. Educ. Technol. Soc. 15, 42–57 (2012)

    Google Scholar 

  10. Ifenthaler, D., Schumacher, C.: Student perceptions of privacy principles for learning analytics. Educ. Technol. Res. Dev. 64, 923–938 (2016)

    Article  Google Scholar 

  11. Nguyen, A., Tuunanen, T., Gardner, L., Sheridan, D.: Design principles for learning analytics information systems in higher education. Eur. J. Inf. Syst. 30, 541–568 (2021)

    Article  Google Scholar 

  12. Ifenthaler, D.: Are higher education institutions prepared for learning analytics? TechTrends 61(4), 366–371 (2016). https://doi.org/10.1007/s11528-016-0154-0

    Article  Google Scholar 

  13. Yigitbasioglu, O.M., Velcu, O.: A review of dashboards in performance management: implications for design and research. Int. J. Account. Inf. Syst. 13, 41–59 (2012)

    Article  Google Scholar 

  14. Vieira, C., Parsons, P., Byrd, V.: Visual learning analytics of educational data: a systematic literature review and research agenda. Comput. Educ. 122, 119–135 (2018)

    Article  Google Scholar 

  15. Droit, A., Rieger, B.: Learning analytics in the flipped classroom – learning dashboards from the students’ perspective. In: Proceedings of the 53rd Hawaii International Conference on System Sciences, pp. 100–107 (2020)

    Google Scholar 

  16. Gašević, D., Dawson, S., Siemens, G.: Let’s not forget: learning analytics are about learning. TechTrends 59, 64–71 (2015)

    Article  Google Scholar 

  17. Bayne, S., Ross, J.: The pedagogy of the Massive Open Online Course (MOOC): the UK View (2014)

    Google Scholar 

  18. Ellis, C.: Broadening the scope and increasing the usefulness of learning analytics: the case for assessment analytics. Br. J. Edu. Technol. 44, 662–664 (2013)

    Article  Google Scholar 

  19. Daniel, B.: Big Data and analytics in higher education: opportunities and challenges. Br. J. Edu. Technol. 46, 904–920 (2015)

    Article  Google Scholar 

  20. Sclater, N.: Learning Analytics Explained. Routledge, New York (2017)

    Book  Google Scholar 

  21. Prinsloo, P., Slade, S.: Student vulnerability, agency and learning analytics: an exploration. J. Learn. Anal. 3, 159–182 (2016)

    Google Scholar 

  22. Negash, S., Gray, P.: Business intelligence. In: Handbook on Decision Support Systems, vol. 4, pp. 175–193 (2008)

    Google Scholar 

  23. Schumacher, C., Ifenthaler, D.: Features students really expect from learning analytics. Comput. Hum. Behav. 78, 397–407 (2018)

    Article  Google Scholar 

  24. Popovič, A., Hackney, R., Coelho, P.S., Jaklič, J.: Towards business intelligence systems success: effects of maturity and culture on analytical decision making. Decis. Support Syst. 54, 729–739 (2012)

    Article  Google Scholar 

  25. Janssen, M., van der Voort, H., Wahyudi, A.: Factors influencing big data decision-making quality. J. Bus. Res. 70, 338–345 (2017)

    Article  Google Scholar 

  26. Hancock, P.A., Warm, J.S.: A dynamic model of stress and sustained attention. Hum. Factors 31, 519–537 (1989)

    Article  Google Scholar 

  27. Anderson, J.C., Mueller, J.M.: The effects of experience and data presentation format on an auditing judgment. J. Appl. Bus. Res. 21, 53–63 (2011)

    Article  Google Scholar 

  28. Klerkx, J., Verbert, K., Duval, E.: Learning analytics dashboards. In: Handbook of Learning Analytics, pp. 143–150. Society for Learning Analytics Research (SoLAR) (2017)

    Google Scholar 

  29. Brooke, J.: SUS: a ‘quick and dirty’ usability scale. In: Jordan, P.W., Thomas, B., McClelland, I.L., Weerdmeester, B. (eds.) Usability Evaluation in Industry, pp. 207–212. CRC Press (1996)

    Google Scholar 

  30. Bangor, A., Kortum, P.T., Miller, J.T.: An empirical evaluation of the system usability scale. Int. J. Hum. Comput. Interact. 24, 574–594 (2008)

    Article  Google Scholar 

  31. Moshagen, M., Thielsch, M.: A short version of the visual aesthetics of websites inventory. Behav. Inform. Technol. 32, 1305–1311 (2013)

    Article  Google Scholar 

  32. Cronbach, L.J.: Coefficient alpha and the internal structure of tests. Psychometrika 16, 297–334 (1951)

    Article  MATH  Google Scholar 

  33. McKnight, P.E., Najab, J.: Mann‐Whitney U test. In: Weiner, I.B., Craighead, W.E., Corsini, R.J. (eds.) The Corsini Encyclopedia of Psychology, p. 960. Wiley, Hoboken (2010)

    Google Scholar 

  34. Shapiro, S.S., Wilk, M.B.: Approximations for the null distribution of the W statistic. Technometrics 10, 861 (1968)

    Article  MathSciNet  Google Scholar 

  35. Thielsch, M.T., Spieth, J.-H., Jahn, M., Hirschfeld, G., Koller, F.: Der VisAWI im Praxiseinsatz: Best Practices, neue Benchmarks und neue Entwicklungen. UP14-Vorträge. German UPA (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Osnabrueck University, Katharinenstr. 3, 49074, Osnabrueck, Germany

    Alena Rodda

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  1. Alena Rodda
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Correspondence to Alena Rodda .

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Editors and Affiliations

  1. ESEN, University of Manouba, Manouba, Tunisia

    Rim Jallouli

  2. ESEN, University of Manouba, Manouba, Tunisia

    Mohamed Anis Bach Tobji

  3. University of Sfax, Sfax, Tunisia

    Meriam Belkhir

  4. University of Minho, CICS.NOVA, Braga, Portugal

    Ana Maria Soares

  5. University of Minho, CICS.NOVA, Braga, Portugal

    Beatriz Casais

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Rodda, A. (2023). Student-Centered Design and Evaluation of a Learning Analytics Dashboard. In: Jallouli, R., Bach Tobji, M.A., Belkhir, M., Soares, A.M., Casais, B. (eds) Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2023. Lecture Notes in Business Information Processing, vol 485. Springer, Cham. https://doi.org/10.1007/978-3-031-42788-6_5

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  • DOI: https://doi.org/10.1007/978-3-031-42788-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42787-9

  • Online ISBN: 978-3-031-42788-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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