Skip to main content
SpringerLink
Log in

A Preliminary Model of Learning Analytics to Explore Data Visualization on Educator’s Satisfaction and Academic Performance in Higher Education

  • Conference paper
  • First Online:
Advances in Visual Informatics (IVIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13051))

Included in the following conference series:

  • 1350 Accesses

Abstract

With the rapid proliferation of online learning due to the Covid-19 pandemic, learning management solutions and software has gained an extraordinary importance in tertiary education. This shift has created large amounts of data from online learning systems that need to be translated into meaningful information, hence data visualization has come into prominent focus as a solution that provides a powerful means to drive Learning Analytics to assess and support educators and students alike in decision-making and sense-making activities from the data collected. Although many research works have been published on data visualization focusing on techniques, tools and best practices, there is still a lack of research in the context of online learning to meet this urgent need of quality data visualization for successful decision-making. In this paper, we explore data visualization that is currently used in learning analytics and present an integrated preliminary model based on DeLone and McLean’s IS Success model to examine the role and significance of data visualization by incorporating it as an antecedent to the Information Quality construct of the IS success model, which will support teaching and learning in an online learning environment for improved educators and student performance. This paper adds to the existing literature by incorporating data visualization to support educators decision-making and its performance impact of online learning through the consideration of the IS success model’s elements. This integrated preliminary conceptual model aims to support online teaching and learning by addressing the research gap that has emerged from the expansion of learning analytics in educational technology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mailizar, M., Almanthari, A., Maulina, S., Bruce, S.: Secondary school mathematics teachers’ views on E-learning implementation barriers during the COVID-19 pandemic: the case of Indonesia. Eurasia J. Math. Sci. Technol. Educ. 16(7), em186 (2020)

    Article  Google Scholar 

  2. Kerres, M.: Against all odds: education in germany coping with covid-19. Postdigit. Sci. Educ. 2(3), 690–694 (2020)

    Article  Google Scholar 

  3. Wang, C.J., Ng, C.Y., Brook, R.H.: Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA 323(14), 1341–1342 (2020)

    Article  Google Scholar 

  4. Friendly, M.: A brief history of data visualization. In: Chen, C.H., Hardle, W., Unwin, A. (eds.) Handbook of Data Visualization, pp. 15–56. Springer, Berlin (2008)

    Chapter  Google Scholar 

  5. Kirk, A.: Data Visualization: A Handbook for Data Driven Design. Sage, London (2016)

    Google Scholar 

  6. Kennedy, H., Engebretsen, M.: Data Visualization in Society. University Press, Amsterdam (2020)

    Google Scholar 

  7. Zhang, X., Yue, P., Chen, Y., Lei, H.: An efficient dynamic volume rendering for large-scale meteorological data in a virtual globe. Comput. Geosci. 126, 1–8 (2019)

    Article  Google Scholar 

  8. Khan, A., Mukhtar, H., Ahmad, H.F., Gondal, M.A., Ilyas, Q.M.: Improving usability through enhanced visualization in healthcare. In: 13th International Symposium on Autonomous Decentralized Systems, pp. 39–44. IEEE, Bangkok (2017)

    Google Scholar 

  9. Warestika, N.E., Sugiarto, D., Siswanto, T.: Business intelligence design for data visualization and drug stock forecasting. Intelmatics 1(1), 9–15 (2020)

    Google Scholar 

  10. 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 

  11. Siemens, G., Long, P.: Learning analytics & knowledge (LAK) call for paper. In: 1st International Conference on Learning Analytics and Knowledge. Banff (2011)

    Google Scholar 

  12. Hung, Y., Parsons, P.: Affective Engagement for communicative visualization: quick and easy evaluation using survey instruments. In: Visualization for Communication (VisComm) Workshop. IEEE, Berlin (2018)

    Google Scholar 

  13. Knight, S., et al.: AcaWriter: a learning analytics tool for formative feedback on academic writing. J. Writing Res. 12(1), 141–186 (2020)

    Article  Google Scholar 

  14. Seliana, N., Suroso, A.I., Yuliati, L.N.: Evaluation of e-learning implementation in the university using DeLone and McLean success model. J. Appl. Manage. 18(2), 345–352 (2020)

    Google Scholar 

  15. Gasevic, D., Kovanovic, V., Joksimovic, S.: Piercing the learning analytics puzzle: a consolidated model of a field of research and practice. Learn. Res. Pract. 17(1), 63–78 (2017)

    Article  Google Scholar 

  16. DeLone, W.H., McLean, E.: The DeLone and McLean model of information system success: a ten-year update. J. Manage. Inf. Syst. 19(4), 9–30 (2003)

    Article  Google Scholar 

  17. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)

    Article  Google Scholar 

  18. Petit dit Dariel, O., Wharrad, H., Windle, R.: Exploring the underlying factors influencing e-learning adoption in nurse education. J. Adv. Nurs. 69(6), 1289–1300 (2013)

    Article  Google Scholar 

  19. Vazquez-Ingelmo, A., Garcia-Penalvo, F.J., Theron, R.: Information dashboards and tailoring capabilities – a systematic literature review. IEEE Access 7, 109673–109688 (2019)

    Article  Google Scholar 

  20. Ward, M.O., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications, 2nd edn. CRC Press, Boca Raton (2015)

    Book  MATH  Google Scholar 

  21. Clow, D.: An overview of learning analytics. Teach. High. Educ. 18(6), 683–695 (2013)

    Article  Google Scholar 

  22. Verbert, K., et al.: Learning dashboards: an overview and future research opportunities. Pers. Ubiquit. Comput. 18(6), 1499–1514 (2014)

    Google Scholar 

  23. Paiva, R., Bittencourt, I.I., Lemos, W., Vinicius, A., Dermeval, D.: Visualizing learning analytics and educational data mining outputs. In: Penstein Rosé, C., et al. (eds.) AIED 2018. LNCS (LNAI), vol. 10948, pp. 251–256. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93846-2_46

    Chapter  Google Scholar 

  24. Schaaf, M., et al.: Improving workplace-based assessment and feedback by an E-portoflio enhanced with learning analytics. Educ. Technol. Res. Dev. 65, 359–380 (2017)

    Article  Google Scholar 

  25. Alhadad, S.S.J., Thompson, K., Knight, S., Lewis, M., Lodge, J.M.: Analytics-enabled teaching as design: reconceptualisation and call for research. In: 8th International Conference on Learning Analytics and Knowledge, pp. 427–435. ACM, New York (2018)

    Google Scholar 

  26. Yalcin, M.A., Elmqvist, N., Bederson, B.B.: Keshif: Rapid and expressive tabular data exploration for novices. IEEE Trans. Visual Comput. Graph. 24(8), 2339–2352 (2018)

    Article  Google Scholar 

  27. Roca, J.C., Chiu, C., Martinez, F.J.: Understanding e-learning continuance intention: an extension of the technology acceptance model. Int. J. Hum. Comput. Stud. 64(8), 683–696 (2006)

    Article  Google Scholar 

  28. Vazquez-Ingelmo, A., Garcia-Penalvo, F.J., Theron, R.: Capturing high-level requirements of information dashboards’ components through meta-modeling. In: Conde-Gonzalez, M.A., Rodriguez-Sedano, F.J., Fernandez-Llamas, C., Garcia-Penalvo, F.J. (eds.) TEEM 2019 Proceedings of the Seventh International Conference on Technological Ecossytems for Enhancing Multiculturality, pp. 815–821. ACM, New York (2019)

    Google Scholar 

  29. Park, Y., Jo, I.: Development of the learning analytics dashboard to support students’ learning performance. J. Univ. Comput. Sci. 21(1), 110–133 (2015)

    Google Scholar 

  30. Bakharia, A., Dawson, S.: SNAPP: a bird’s-eye view of temporal participant interaction. In: 1st International Conference on Learning Analytics and Knowledge, pp. 168–173. ACM, Banff (2011)

    Google Scholar 

  31. Arnold, K.E., Pistilli, M.D.: Course signals at purdue: using Learning Analytics to increase student success. In: 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM, Vancouver (2012)

    Google Scholar 

  32. Wise, A., Zhao, Y., Hausknecht, S.: Learning analytics for online discussions: embedded and extracted approaches. J. Learn. Anal. 1(2), 48–71 (2014)

    Article  Google Scholar 

  33. Liu, Z., Nersessian, N.J., Stasko, J.T.: Distributed cognition as a theoretical framework for information visualization. IEEE Trans. Visual Comput. Graph. 14(6), 1172–1180 (2008)

    Google Scholar 

  34. Marbouti, F., Wise, A.F.: Starburst: a new graphical interface to support purposeful attention to others’ posts in online discussions. Educ. Technol. Res. Dev. 64(1), 87–113 (2016)

    Article  Google Scholar 

  35. DeLone, W.H., McLean, E.R.: Information system success: the quest for the dependent variable. Inf. Syst. Res. 3(1), 60–90 (1992)

    Article  Google Scholar 

  36. Uddin, M.D.M., Isaac, O., Alrajawy, I., Maram, M.A.: Do user satisfaction and actual usage of online learning impact students performance? Int. J. Manage. Hum. Sci. 3(2), 60–67 (2019)

    Google Scholar 

  37. Yakubu, M.N., Dasuki, S.: Assessing eLearning system success in Nigeria: an application of the DeLone and McLean information system success model. J. Inf. Technol. Educ. Res. 17, 183–203 (2018)

    Google Scholar 

  38. Aldholay, A., Abdullah, Z., Isaac, O., Mutahar, A.M.: Perspective of Yemeni students n use of online learning: extending the information systems success model with transformational leadership and compatibility. Inf. Technol. People 33(1), 106–128 (2019)

    Article  Google Scholar 

  39. Balaban, I., Stancin, K., Sobodic, A.: Analysis of correlations between indicators influencing successful deployment of ePortfolios. In: 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 788–793. IEEE, Opatijia (2018)

    Google Scholar 

  40. Toquero, C.M.: Challenges and opportunities for higher education amid the COVID-19 pandemic: the philippine context. Pedagogical Res. 5(4), em0063 (2020)

    Article  Google Scholar 

  41. Fishbein, M., Ajzen, I.: Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading, USA (1975)

    Google Scholar 

  42. Alharbi, H., Sandhu, K.: Digital learning analytics recommender system for universities. In: Sandhu, K. (ed.) Digital Innovations for Customer Engagement, Management, and Organizational Improvement, pp. 184–199. IGI Global, Australia (2020)

    Chapter  Google Scholar 

  43. Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M.: A large-scale implementation of predictive learning analytics in higher education: the teachers role and perspective. Educ. Technol. Res. Dev. 67, 1273–1306 (2019)

    Article  Google Scholar 

  44. Rienties, B., Herodotou, C., Olney, T., Schencks, M., Boroowa, A.: Making sense of learning analytics dashboards: a technology acceptance perspective of 95 teachers. Int. Rev. Res. Open Distrib. Learn. 19(5), 187–202 (2018)

    Google Scholar 

  45. Al-Emran, M., Mezhuyev, V., Kamaludin, A.: Technology acceptance model in m-learning context: a systematic review. Comput. Educ. 125, 389–412 (2018)

    Article  Google Scholar 

  46. Estriegana, E., Medina-Merodio, J., Barchino, R.: Student acceptance of virtual laboratory and practical work: an extension of technology acceptance model. Comput. Educ. 135, 1–14 (2019)

    Article  Google Scholar 

  47. Salloum, S.A., Alhamad, A.Q.M., Al-Emran, M., Monen, A.A., Shaalan, K.: Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access 7, 128445–128462 (2019)

    Article  Google Scholar 

  48. Brandon-Jones, A., Kauppi, K.: Examining the antecedents of the technology acceptance model within e-procurement. Int. J. Oper. Prod. Manag. 38(1), 22–42 (2018)

    Article  Google Scholar 

  49. Rahimi, B., Nadri, H., Afshar, H.L., Timpka, T.: A systematic review of the technology acceptance model in health informatics. Appl. Clin. Inform. 9(3), 604–634 (2018)

    Article  Google Scholar 

  50. Chen, C., Xu, X., Arpan, L.: Between the technology acceptance model and sustainable energy technology acceptance model: investigating smart meter acceptance in the United States. Energy Res. Soc. Sci. 25, 93–104 (2017)

    Article  Google Scholar 

  51. Wang, Y., Wang, S., Wang, J., Wei, J., Wang, C.: An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model. Transportation 47(1), 397–415 (2018)

    Article  Google Scholar 

  52. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage. Sci. 46(2), 186–204 (2000)

    Article  Google Scholar 

  53. Venkatesh, V.: Determinants of perceived ease of use: integrating control, intrinsic motivation and emotion into the technology acceptance model. Inf. Syst. Res. 11(4), 342–365 (2000)

    Article  Google Scholar 

  54. Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–315 (2008)

    Article  Google Scholar 

  55. Koceska, N., Koceski, S.: Measuring the impact of online learning on students’ satisfaction and student outcomes using integrated model. In: International Conference on Information Technology and Development of Education, pp. 96–101. ITRO, Serbia (2020)

    Google Scholar 

  56. Al-Fraihat, D., Joy, M., Masa’deh, R., Sinclair, J.: Evaluating e-learning systems success: an empirical study. Comput. Hum. Behav. 102, 67–86 (2020)

    Article  Google Scholar 

  57. Ohliati, J., Abbas, B.S.: Measuring students satisfaction in using learning management system. Int. J. Emerg. Technol. Learn. 14(4), 180–189 (2019)

    Article  Google Scholar 

  58. Al-Sabawy, A.: Measuring E-Learning Systems Success (PhD dissertation). University of Southern Queensland (2013)

    Google Scholar 

  59. Balaban, I., Mu, E., Divjak, B.: Development of an electronic portfolio system success model: an information system approach. Comput. Educ. 60(1), 396–411 (2013)

    Article  Google Scholar 

  60. Zogheib, B., Daniela, L.: Students’ perception of cell phones effect on their academic performance: a latvian and a middle eastern university cases. Technol. Knowl. Learn (2021)

    Google Scholar 

  61. Barbour, C., Schuessler, J.B.: A preliminary framework to guide implementation of the flipped classroom method in nursing education. Nurse Educ. Pract. 34, 36–42 (2019)

    Article  Google Scholar 

  62. Roger, T., Dawson, S., Gasevic, D.: learning analytics and the imperative for theory-driven research. In: Haythornthwaite, C., Andrews, R., Fransman, J., Meyers, E.M. (eds.) The SAGE Handbook of E-learning Research, 2nd edn, pp. 232–250. SAGE (2016)

    Google Scholar 

  63. Tan, J P.L., Yang, S., Koh, E., Jonathan, C.: Fostering 21st century literacies through a collaborative critical reading and learning analytics environment: user-perceived benefits and problematics. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 430–434. ACM, Edinburgh (2016)

    Google Scholar 

  64. Hatala, M., Beheshitha, S.S., Gasevic, D.: Associations between students’ approaches to learning and learning analytics visualizations. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 3–10. ACM, Edinburgh (2016)

    Google Scholar 

  65. Beheshita, S.S., Hatala, M., Gasevic, D., Joksimovic, S.: The role of achievement goal orientations when studying effect of learning analytics visualizations. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 54–63. ACM, Edinburgh (2016)

    Google Scholar 

  66. Islam, N.A.K.M.: Investigating e-learning system usage outcomes in the university context. Comput. Educ. 69, 387–399 (2013)

    Article  Google Scholar 

  67. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a 282 comparison of two theoretical models. Manag. Sci. 35, 983–1003 (1989)

    Google Scholar 

Download references

Acknowledgements

We are sincerely grateful to BOLD RESEARCH GRANT 2021 (BOLD 2021-J510050002/2021054) funded by Universiti Tenaga Nasional (UNITEN), Malaysia to carry out this study.

Author information

Authors and Affiliations

  1. Universiti Tenaga Nasional, Jalan Ikram-Uniten, 43000, Kajang, Selangor, Malaysia

    Nur Maisarah Shahril Khuzairi & Zaihisma Che Cob

Authors
  1. Nur Maisarah Shahril Khuzairi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zaihisma Che Cob
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universiti Tenaga Nasional, Selangor, Malaysia

    Halimah Badioze Zaman

  2. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  3. National Central University, Jhongli, Taiwan

    Timothy K. Shih

  4. Queen Mary University of London, London, UK

    Sergio Velastin

  5. Toyo University, Tokyo, Japan

    Tada Terutoshi

  6. University of Southern Denmark, Odense, Denmark

    Bo Nørregaard Jørgensen

  7. Universiti Tenaga Nasional, Selangor, Malaysia

    Hazleen Aris

  8. Universiti Tenaga Nasional, Selangor, Malaysia

    Nazrita Ibrahim

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shahril Khuzairi, N.M., Che Cob, Z. (2021). A Preliminary Model of Learning Analytics to Explore Data Visualization on Educator’s Satisfaction and Academic Performance in Higher Education. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2021. Lecture Notes in Computer Science(), vol 13051. Springer, Cham. https://doi.org/10.1007/978-3-030-90235-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-90235-3_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90234-6

  • Online ISBN: 978-3-030-90235-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation