ABSTRACT
Every time a student interacts during their learning, they leave behind a digital footprint. The process of using this data to improve learning and teaching is called as Learning Analytics. Researches in this field grow and are more popular, specifically that usage of data outside the Virtual Learning Environment. Although often proposed data in previous research use students' personal data, their perception of the usage of those data is still underexplored. This study investigates higher education students' understanding of how useful the proposed data might be helpful as their input. Our study reveals that each degree-level student response differently regarding the usefulness each data sources. Therefore, we need to consider students' perception when we design personal learning analytics for students, so the app can fit to their preference and needs.
- Ali, L., Hatala, M., Gašević, D., & Jovanović, J. 2012. A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470--489.Google ScholarDigital Library
- Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. 2013. A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318--331.Google Scholar
- Chen, X., Sin, S. C. J., Theng, Y. L., & Lee, C. S. (2015). Why students share misinformation on social media: Motivation, gender, and study-level differences. The Journal of Academic Librarianship, 41(5), 583--592.Google ScholarCross Ref
- Cruz-Benito, J., Therón, R., García-Peñalvo, F. J., & Lucas, E. P. 2015. Discovering usage behaviors and engagement in an Educational Virtual World. Computers in Human Behavior, 47, 18--25.Google ScholarDigital Library
- Davis, F. D. 1993. User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475--487.Google ScholarDigital Library
- Dawson, S., Bakharia, A., & Heathcote, E. (2010, May). SNAPP: Realising the affordances of real-time SNA within networked learning environments. Networked Learning. In Proceedings of the 7th International Conference on Networked Learning, pages 125--133, Denmark, Aalborg, 2010.Google Scholar
- Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. 2017, March. Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 188--197). ACM.Google ScholarDigital Library
- Duval, E. 2011. Attention please!: learning analytics for visualization and recommendation. Learning Analytics and Knowledge, 11, 9--17.Google Scholar
- Kitto, K., Cross, S., Waters, Z., & Lupton, M. 2015, March. Learning analytics beyond the LMS: the connected learning analytics toolkit. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 11--15). ACM.Google ScholarDigital Library
- Newland, B., Martin, L., Ringan, N. 2015. Learning Analytics in UK HE 2015: A HeLF Survey Report. Heads of eLearning Forum. https://helfuk.blogspot.co.uk/2015/10/uk-he-learning- analytics-survey-2015.html (last accessed: 23/08/2019)Google Scholar
- Sclater, N., Peasgood, A., & Mullan, J. 2016. Learning analytics in higher education. London: JISC.Google Scholar
- Simsek, D. 2012. Learning Analytics Beyond Learning Management Systems: Proposing a Learner Dashboard which collects, analyses and reports learner-generated data from social networking sites. Unpublished master dissertation, University of Southampton.Google Scholar
- Shum, S.B. and Ferguson, R., 2012. Social learning analytics. Journal of Educational Technology & Society, 15(3), pp.3--26.Google Scholar
- Yan, M. 2018, July. Patient Perceptions of the Use of a Technology-Augmented Healthcare System for the Self-care of Type 2 Diabetes Mellitus and/or Hypertension. In International Conference on Applied Human Factors and Ergonomics (pp. 234--239). Springer, Cham.Google Scholar
- Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Journal of Educational Technology & Society, 10(2), 71--83.Google Scholar
- Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166--183.Google ScholarCross Ref
Index Terms
- Students' Perception on Data Sources from Outside Virtual Learning Environment for Learning Analytics
Recommendations
Personality Learning Analytics System in Intelligent Virtual Learning Environment
ICIT '18: Proceedings of the 6th International Conference on Information Technology: IoT and Smart CityIn this paper, the researchers propose a conceptual for system architecture of learning analytics process in the intelligent learning environment. Within this concept, today's competitive business environment need for businesses in order to implement ...
Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment
AbstractTechnology enhanced learning (TEL) such as online learning environment with adaptive technologies has gained growing interest in recent past in the field of teaching and learning. In this context, mobile learning has got much momentum ...
Highlights- Big data analytics in the cloud to support learning analytics for mobile learning.
Comments