Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation

Dabbebi Ines, Iksal Sebastien, Gilliot Jean-Marie, May Madeth, Garlatti Serge

2017

Abstract

Designing Learning Analytic (LA) dashboards can be a challenging and complex task when dealing with abundant data generated from heterogeneous sources with various uses. On top of that, each dashboard is designed in accordance with the user’s needs and their observational objectives. Therefore, understanding the context of LA and its users is compulsory as it is part of the dashboard design approach. Our research effort starts with an exploratory study of different contextual elements that could help us define what an adaptive dashboard is and how it fulfills the user’s needs. To do so, we have conducted a needs assessment to characterize the user profiles, their activities, their visualization preferences and objectives when using a dedicated dashboard. In this paper, we introduce a conceptual model, which will be used to generate a variety of LA dashboards. Our main goal is to provide users with adaptive dashboards, generated accordingly to their context of use while satisfying the users’ requirements. We also discussed the implementation process of our first prototype as well as further improvements.

References

  1. Abras, C., Maloney-Krichmar, D., and Preece, J. (2004). User-centered design. Bainbridge, W. Encyclopedia of Human-Computer Interaction. Thousand Oaks: Sage Publications, pages 445-456.
  2. Ali, L., Hatala, M., Gasevic, D., and Jovanovic, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers and Education, pages 470-489.
  3. Arnold, K.-E. and Pistilli, M.-D. (2012). Course signals at Purdue: using learning analytics to increase student success. The 2nd International Conference on Learning Analytics and Knowledge, pages 267-270.
  4. Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gasevic, D., Mulder, R., and Lockyer, L. (2016). A conceptual framework linking learning design with learning analytics. The Sixth International Conference on Learning Analytics and Knowledge, pages 329-338.
  5. Baldauf, M., Dustdar, S., and Rosenberg, F. (2007). A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2:263-277.
  6. Bouhineau, D., Luengo, V., Mandran, N., Ortega, M., and Wajeman, C. (2013). Conception et mise en place d'un entrepeˆt de traces et processus de traitement EIAH: UnderTracks. 6e Conférence sur les Environnements Informatiques pour l'Apprentissage Humain, pages 41-42.
  7. Bouvier, P., Sehaba, K., and Lavoue, E. (2014). A tracebased approach to identifying users engagement and qualifying their engaged-behaviors in interactive systems: application to a social game. User Modeling and User-Adapted Interaction, 24:413-451.
  8. de Waard, I. (2015). MOOC factors influencing teachers in formal education. Revista mexicana de bachillerato a distancia, 13.
  9. Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5:4-7.
  10. Dyke, G., Lund, K., and Girardot, J.-J. (2009). Tatiana: an environment to support the CSCL analysis process. Proceedings of the 9th international conference on Computer supported collaborative learning - CSCL'09, pages 58-67.
  11. Few, S. (2013). Information Dashboard Design: Displaying data for at-a-glance monitoring. Burlingame, CA: Analytics Press.
  12. Gilliot, J.-M., Garlatti, S., Rebai, I., and Belen-Sapia, M. (2013). Le concept de iMOOC pour une ouverture maˆitris ée. EIAH 2013-6e Conférence sur les Environnements Informatiques pour l'Apprentissage Humain.
  13. Greller, W. and Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology and Society, pages 42-57.
  14. Iksal, S. and Choquet, C. (2007). Modélisation et construction de traces d'utilisation d'une activité d'apprentissage: une approche langage pour la réingénierie d'un EIAH. Revue des Sciences et Technologies de l'Information et de la Communication pour l'Education et la Formation, pages 14-24.
  15. John, B., Thavavel, V., Jayaraj, J., Muthukumar, A., and Jeevanandam, P. K. (2016). Comparative analysis of current methods in searching open education content repositories. The Online Journal of Science and Technology, pages 21-29.
  16. Kelly, K., Heffernan, N., Heffernan, C., Goldman, S., Pellegrino, J., and Soffer Goldstein, D. (2013). Estimating the effect of web-based homework. The international Conference on Artificial Intelligence in Education. Springer, Heidelberg, pages 824-827.
  17. Li, X., Eckert, M., Martinez, J. F., and Rubio, G. (2015). Context aware middleware architectures: Survey and challenges. Sensors (Switzerland), 15(8).
  18. Martinez-Maldonado, R., Pardo, A., Mirriahi, N., Yacef, K., Kay, J., and Clayphan, A. (2016). LATUX: an Iterative Workflow for Designing, Validating and Deploying Learning Analytics Visualisations. Journal of Learning Analytics, 2:9-39.
  19. May, M., George, S., and Prévoˆt, P. (2011). TrAVis to Enhance Students Self-monitoring in Online Learning Supported by Computer-Mediated Communication Tools. Computer Information Systems and Industrial Management Applications, 3:623-634.
  20. Mazza, R. and Dimitrova, V. (2007). CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses. International Journal of Human Computer Studies, 65:125-139.
  21. Mazza, R. and Milani, C. (2004). GISMO: a Graphical Interactive Student Monitoring Tool for Course Management Systems. Technology Enhanced Learning International Conference. Milan, pages 18-19.
  22. Nunes, B. P., Fetahu, B., and Casanova, M. A. (2013). Cite4Me: Semantic Retrieval and Analysis of Scientific Publications. LAK-Data Challenge 7813.
  23. Olmos, M. and Corrin, L. (2012). Academic analytics in a medical curriculum: Enabling educational excellence. Australasian Journal of Educational Technology, 28:1-15.
  24. Park, Y. and Jo, I. H. (2015). Development of the Learning Analytics Dashboard to Support Students Learning Performance. UCS, pages 110-133.
  25. Santos, J. L., Verbert, K., Govaerts, S., and Duval, E. (2013). Addressing Learner Issues with StepUp!: An Evaluation. Proceedings of the Third International Conference on Learning Analytics and Knowledge, pages 14-22.
  26. Scheuer, O. and Zinn, C. (2007). How did the e-learning session go? The Student Inspector. Proc. of the Conf. on Artificial Intelligence in Education, pages 487- 494.
  27. Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Shum, S. B., and Ferguson, R. (2011). Open Learning Analytics : an integrated and modularized platform. Knowledge Creation Diffusion Utilization, pages 1- 20.
  28. Silius, K., Tervakari, A. M., and Kailanto, M. (2013). Visualizations of user data in a social media enhanced web-based environment in higher education. Global Engineering Education Conference., pages 4893-899.
  29. Speier, C., Valacich, J. S., and Vessey, I. (1999). The influence of task interruption on individual decision making: An information overload perspective. Decision Sciences, pages 337-360.
  30. Stodder, D. (2013). Data Visualization and Discovery for Better Business Decisions. TDWI Best Practices Report, Third Quarter, 1:36.
  31. Verbert, K., Duval, E., Klerkx, J., Govaerts, S., and Santos, J. L. (2013). Learning Analytics Dashboard Applications. American Behavioral Scientist, 57:1500-1509.
  32. Wolff, A., Zdrahal, Z., Nikolov, A., and Pantucek, M. (2013). Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. Third Conference on Learning Analytics and Knowledge.
  33. Xhakaj, F., Aleven, V., and McLaren, B. M. (2016). How Teachers Use Data to Help Students Learn: Contextual Inquiry for the Design of a Dashboard. European Conference on Technology Enhanced Learning. Springer International Publishing 2016, pages 340- 354.
  34. Xiaoyan, B., White, D., and Sundaram, D. (2012). Contextual adaptive knowledge visualization environments. Electronic Journal of Knowledge Management, 10:1- 14.
  35. Zarka, R., Champin, P. A., Cordier, A., Egyed-Zsigmond, E., Lamontagne, L., and Mille, A. (2012). Tstore: A web-based system for managing, transforming and reusing traces. ICCBR (2012): True and Story Cases Workshop, pages 173-182.
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Paper Citation


in Harvard Style

Ines D., Sebastien I., Jean-Marie G., Madeth M. and Serge G. (2017). Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-239-4, pages 120-131. DOI: 10.5220/0006325601200131


in Bibtex Style

@conference{csedu17,
author={Dabbebi Ines and Iksal Sebastien and Gilliot Jean-Marie and May Madeth and Garlatti Serge},
title={Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2017},
pages={120-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006325601200131},
isbn={978-989-758-239-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Towards Adaptive Dashboards for Learning Analytic - An Approach for Conceptual Design and Implementation
SN - 978-989-758-239-4
AU - Ines D.
AU - Sebastien I.
AU - Jean-Marie G.
AU - Madeth M.
AU - Serge G.
PY - 2017
SP - 120
EP - 131
DO - 10.5220/0006325601200131