IMPACT OF LEARNING ANALYTICS TOWARDS STUDENTS PERFORMANCE

Authors

  • Syarifah Rabiyah Al Adawiah binti Syed Badrul Hisham Management Department, UTMSPACE, Kuala Lumpur, Malaysia
  • Madihah binti Md Fadhil Management Department, UTMSPACE, Kuala Lumpur, Malaysia
  • Nazriah binti Rasul Department of Geomatic & Built Environment, UTMSPACE, Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.20319/pijss.2019.53.457474

Keywords:

Learning Analytics, Technology Management, UTMSPACE, Kuala Lumpur

Abstract

The fast pace of big data analytics advancement makes it necessary for any organization to coincide it with their management and measurement process.  It has become essential for education sectors to analyze this for the development of both learning and academic activities (Shikha. A, 2014). Learning analytics (LA) is the measurement and analysis of the collection of data with regards to learners and their context for making learning more effective. LA is much concern with improving learner’s success. Four dimensions have been identified; data and environment, stakeholders, objectives, and methods. This paper investigates the impact of learning analytics on student’s performance. The focus group was students in Technology Management program at UTM SPACE, Kuala Lumpur. Two research objective has been identified; (i) to find the level of LA understanding among academic staff and (ii) to investigate the relationship between learning analytics and student performance. The research focused on (i) data collection and population at Centre of Diploma Studies, UTM SPACE, KL; (ii) the selected sample will be students in Technology Management’s program; (iii) the research focused on learning analytics with main focus on course assessment reports of core course which are (a) technology management and (b) operation management.

References

Baker, R.S.J.d., Barnes, T. & Beck, J.E. (2008). Proceedings of the 1st International Conference on Educational Data Mining.

Boud, D., Keogh, R., Walker, D. (1985). Reflection: Turning Experience into Learning. In: Promoting Reflection in Learning. Routledge Falmer, New York, pp. 18-40.

Buerck, J. P., & Mudigonda, S. P. (2014). A resource-constrained approach to implementing analytics in an institution of higher education: An experience report. Journal of Learning Analytics, 1(1), 129–139. https://doi.org/10.18608/jla.2014.11.7

Campbell, J.P., DeBlois, P.B.& Oblinger, D.G (2017). Academic Analytics. A New Tool for a New Era. Educause Review, 4(42), 40.

Chatti, M.A., Dychkoff, A.L., Schroeder. U., & Thus, H. (2012). A Reference Model for Learning Analytics. International Journal of Technology Enhanced Learning, 4(5), pg 318-331. https://doi.org/10.1504/IJTEL.2012.051815

Dietz-Uhler,B., & Hurn.E.Janet, (2013). Using Learning Analytics to Predict (and Improve) Student Success: A Faculty Perspective. Journal of Interactive Online Learning. Volume 12, Number 1, Spring 2013, ISSN: 1541-4914.

Elias, T. (2011). Learning Analytics: Definitions, Processes and Potential. Retrieved February 10, from http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf

Greller, W.S.F., & Drachsler, H. (2012). Translating Learning into Numbers: A generic Framework for Learning Analytics. Educational Technology & Society, 3(15), 45-27.

Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques. San Francisco, CA: Elsevier.

Henderson, C., Beach, A., & Finkelstein, N. (2011). Facilitating change in undergraduate STEM instructional practices: An analytic review of the literature. Journal of Research in Science Teaching, 48(8), 952–984. https://doi.org/10.1002/tea.20439

Henn. M., Weinstein. M., and Foard. N (2005), A Short Introduction to Social Research, SAGE

Johnson, L., Smith, R., Willis, H., Levine, A. & Haywood, K. (2011). The 2011 Horizon Report. Austin, Texas: The New Media Consortium.

Jung, C.G., (1971), Psychological Types, Princeton University Press, Princeton, NJ.

Krejcie, R.V., & Morgan, D.W., (1970), Determining Sample Size for Research Activities. Educational and Psychological Measurement. https://doi.org/10.1177/001316447003000308

Liu, B. (2006). Web Data Mining. Berlin Heidelberg: Springer. https://doi.org/10.4018/jdwm.2006070104 https://doi.org/10.4018/jdwm.2006070103

Molenda, M., (2003). In Search of The Elusive ADDIE Model: Performance Improvement. https://doi.org/10.1002/pfi.4930420508

Molina. G.M., & Bansil. G.A., (2018). Correlation between Confidence and Performance of Engineering Students in Solid Menstruation, PEOPLE: International Journal of Social Sciences, Vol.4 No.1. https://doi.org/10.20319/pijss.2018.41.87104

Norris, D.M (2011). Seven Things You Should Know About First Generation Learning Analytics. EDUCAUSE, Learning Initiative Briefing.

Romero, C., & Ventura, S., (2007). Educational Data Mining: a Survey from 1995 to 2005. Expert Systems with Applications. 33(1), 135-146. https://doi.org/10.1016/j.eswa.2006.04.005

Saptarshi. R. (2013). Big Data in Education. CGRAVITY, Issues 20.

Siemens, G., & Long, P. (2011). Penetrating the Fog, Analytics in Learning and Education. EDUCAUSE Review, Vol.46, Issues 4.

Shika, A (2014). Big Data Analytics in the Education Sector: Need, Opportunities and Challenges. International Journal of Research in Computer and Communication Technology, Vol.3, Issues 11.

Shum, B.S (2012). Learning Analytics Policy Brief. UNESCO, Institute of Information Technology in Education.

Verbert.K.,Manouselis.N.,Ochoa.X.,Wolpers.M.,&Drachsler.H.,(2012).Context-aware recommender systems for learning: A survey and future challenges, IEE Transactions on Learning Technologies 5 (4), 318-335. https://doi.org/10.1109/TLT.2012.11

Ware, P. (1983). Personality adaptations. Transactional Analysis Journal, Vol. 13 No. 1, pp. 11-19. https://doi.org/10.1177/036215378301300104

Wayne W.E (2010). Performance Dashboards: Measuring, Monitoring, and Managing Your Business. 2nd Edition, Wiley Production.

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Published

2019-12-23

How to Cite

Hisham, S. R. A. S. B., Md Fadhil, M., & Rasul, N. (2019). IMPACT OF LEARNING ANALYTICS TOWARDS STUDENTS PERFORMANCE. PEOPLE: International Journal of Social Sciences, 5(3), 457–474. https://doi.org/10.20319/pijss.2019.53.457474