Personalization and Visual Representation through Learning Analytics: A Meaningful Approach to Guide Self-Directed Learners

Authors

  • Gomathi Thiyagarajan Head - Department of Computer Applications, CMR Institute of Technology, Bengaluru Author
  • Dr.S. Prasanna Head - Department of Computer Science, School of Computing Science, VISTAS, Chennai Author

DOI:

https://doi.org/10.61841/j7ppkp82

Keywords:

Learning Analytics, Self-directed Learning, Information Visualization, Personalization, Learning and Development

Abstract

Due to technological advancement and global needs, educators are challenged with creating professionals who can exceptionally apply skill and competence in a changing world. In order to accomplish this, an educator must come up with a structure of self-directed learning techniques. This technique subsequently should enhance the student’s capabilities and provide a platform to self-evaluate their performance and proficiency without compromising their learning frequency. The field of learning analytics has significantly matured in tracing learners’ data from their digital footprint and providing valuable insights for improving the teaching-learning process. The researchers of this study aim to identify how learning analytics can be used as a tool for personalizing learning and what information an analytics dashboard can provide self-directed learners to scale up key competencies and skills. 

Downloads

Download data is not yet available.

References

[1] Arshavskiy M,” Instructional Design for eLearning: Essential guide for designing successful eLearning

courses”, CreateSpace Independent Publishing Platform, 2013.

[2] Ahn J, “What Can We Learn from Facebook Activity? Using Social Learning Analytics to Observe New

Media Literacy Skills”, Proceedings of the Third International Conference on Learning Analytics and

Knowledge, pp. 135–144, 2013.

[3] Lukarov V, Muslim A, Mohamed A, Yousef F, and Wahid U, “Learning Analytics: Challenges and Future

Research Directions”, e-Learning and education(eleed), Iss 10, 2016.

[4] https://www.solaresearch.org/about/what-is-learning-analytics/ retrieved on 27th Feb 2020.

[5] Sin K and Muthu L,” Application of Big Data in Education Data Mining and Learning Analytics-A

Literature Review”, ICTACT Journal on Soft Computing, Vol.5, Iss:4, 2015.

[6] Shum S B and Ferguson R,” Social Learning Analytics”, Journal of Educational Technology & Society. 15,

No. 3, pp. 3-26,2012.

[7] Schumacher C and Ifenthaler D, “Features Students Really Expect from Learning Analytics”, 13th

International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2016), pp. 67–

76, 2016.

[8] Lang C, Siemens G, Wise A, and Gašević D (Eds), “Handbook of Learning Analytics”, Society of Learning

Analytics, 2017.

[9] Anthony J. Onwuegbuzi, Frels,” Seven Steps to a Comprehensive Literature Review: A Multimodal and

Cultural Approach “, 1st Edition, Sage Publication, 2016.

[10] Long P and G. Siemens G, “Penetrating the Fog: Analytics in Learning and Education,” 2011.

[11] Siemens G, “Learning Analytics: Envisioning a Research Discipline and a Domain of Practice”,

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 4–8, 2012.

[12] Muslim A, Chatti M. A, Mahapatra T, and Schroeder U, “A Rule-Based Indicator Definition Tool for

Personalized Learning Analytics,” LAK '16: Proceedings of the Sixth International Conference on Learning

Analytics & Knowledge, pp. 264–273, 2016.

[13] Muslim A, Chatti M. A, Mughal M, and Schroeder U, “The goal-Question-Indicator approach for

personalized learning analytics”, Proceedings of the 9th International Conference on Computer Supported

Education (CSEDU 2017), vol. 1, pp. 371–378, 2017.

[14] Yousef A. M. F, “Personalized Links Recommendation Based on Learning Analytics in MOOCs,” The

Ninth International Conference on Mobile, Hybrid, and On-line Learning, pp. 115–119, 2017.

[15] Dimitrova V, Lau L, Piotrkowicz A, Weerasinghe A, and Mitrovic A, “Using learning analytics to devise

interactive personalized nudges for active video watching,” UMAP '17: Proceedings of the 25th Conference

on User Modeling, Adaptation and Personalization., pp. 22–31, 2017.

[16] Pardo A, Jovanovic J, Dawson S, Gašević D and Mirriahi N, “Using learning analytics to scale the

provision of personalized feedback,” British Journal of Education Technology, vol. 50, no. 1, pp. 128–138,

2019.

[17] Mavroudi A, Giannakos M, and Krogstie J, “Supporting adaptive learning pathways through the use of

learning analytics: developments, challenges, and future opportunities,” Interactive Learning

Environments, vol. 26, no. 2, pp. 206–220, 2018.

[18] Maseleno A, Sabani N, Huda M, Ahmad R, and Jasmi K A, “Demystifying Learning Analytics in

Personalised Learning”, International Journal of Engineering & Technology, Volume 7, No.3, pp. 1124-

1129, 2018.

[19] Vesin, B., Mangaroska, K. & Giannakos, M., “Learning in smart environments: user-centered design and

analytics of an adaptive learning system”, Smart Learning Environment”.Vol. 5, no 24, 2018.

[20] Bodily R and Verbert K, “Trends and issues in student-facing learning analytics reporting systems

research”, In the Proceedings of the 7th International Conference on Learning Analytics and Knowledge

(LAK ’17), Pages 309–318. 2017.

[21] Bodily R, Kay J, Aleven V, Jivet I, Davis D, Xhakaj F, and Verbert K., “Open learner models and learning

analytics dashboards: a systematic review”, Proceedings of the 8th International Conference on Learning

Analytics and Knowledge (LAK ’18), pp. 41–50, 2018.

[22] Jivet I, Scheffel M, Specht M, and Drachsler H, “License to evaluate: Preparing learning analytics

dashboards for educational practice,” Proceedings of the 8th International Conference on Learning

Analytics and Knowledge (LAK ’18), pp. 31–40, 2018.

[23] Seaton J.X., Chang M., Graf S,” Integrating a Learning Analytics Dashboard in an Online Educational Game. In: Tlili A., Chang M. (eds) Data Analytics Approaches in Educational Games and Gamification

Systems”. Smart Computing and Intelligence. Springer, Singapore, 2019.

[24] Aljohani, N.R., Daud, A., Abbasi, R.A., Alowibdi, J.S., Basheri, M., & Aslam, M.A.” An integrated

framework for course adapted student learning analytics dashboard”. Computer. Hum. Behav., 92, 679-690,

2019.

[25] Harvey A J and Keyes H, “How do I compare thee? An evidence-based approach to the presentation of

class comparison information to students using Dashboard,” Innovations in Education and Teaching, pp. 1–

12, 2019.

[26] Molenaar I, Horvers A, Dijkstra R, and Baker R, “Designing Dashboards to support learners’ SelfRegulated Learning,”, Companion Proceedings 9th International Conference on Learning Analytics &

Knowledge (LAK19), pp. 764-775, 2019.

[27] Matcha W, Ahmad Uzir N, Gasevic D and Pardo A, "A Systematic Review of Empirical Studies on

Learning Analytics Dashboards: A Self-Regulated Learning Perspective," In IEEE Transactions on

Learning Technologies. vol. 1382, pp. 1–1, 2019.

Downloads

Published

31.07.2020

How to Cite

Thiyagarajan, G., & S. , P. (2020). Personalization and Visual Representation through Learning Analytics: A Meaningful Approach to Guide Self-Directed Learners. International Journal of Psychosocial Rehabilitation, 24(5), 3298-3303. https://doi.org/10.61841/j7ppkp82