Connecting the Gap Between Formal and Informal Attributes Within Formal Learning with Data Mining Techniques
Formal and informal attributes are two distinct forms of learning which famed on the basis of the learning content, by where, when, and how learning happened. Formal attributes is a traditional learning which has official course work which should be completed in specified time. This study aimed at evaluating the challenges that students face while working for achieving good grades in exams. Data mining techniques are used to identify the challenges. The methods of collection working in this study were qualitative which involved testing and comparing.
KeywordsFormal attributes Informal attributes Data mining Rapid miner PCA K-Means clustering
- 1.Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 231–240). New York, NY, USA: ACM. https://doi.org/10.1145/2567574.2567585.
- 2.R. Ferguson, ‘‘Learning analytics: drivers, developments and challenges’’ International Journal of Technology Enhanced Learning (2012).Google Scholar
- 3.D. Clow, E. Makriyanni, ‘‘iSpot analysed: participatory learning and reputation’’ Proceedings of the 1st International Conference Learning Analytics and Knowledge (2011).Google Scholar
- 4.Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., & Schroeder, U. (2014). Learning analytics: Challenges and future research directions. E-learn Educ (Eleed) J, 10, 1–16.Google Scholar
- 5.Jacobs, R. L. (2003). Structured on-the-job training: Unleashing employee expertise in the workplace. San Francisco: Berrett-Koehler.Google Scholar
- 7.Siemens, G., & d Baker, R. S. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252–254). ACM.Google Scholar
- 9.Hall, R. (2009). Towards a fusion of formal and informal attributes environments: The impact of the read/write web. Electronic Journal of E-Learning, 7(1), 29–40. Retrieved from http://www.ejel.org/volume7/issue1.
- 10.Sefton-Green, J. (2004). Literature review in informal attributes with technology outside school. Retrieved from https://www.nfer.ac.uk/publications/FUTL72/FUTL72.pdf.
- 11.Boustedt, J., Eckerdal, A., McCartney, R., Sanders, K., Thomas, L., & Zander, C. (2011). Students’ perceptions of the differences between formal and informal attributes. In Proceedings of the SeventhInternational Workshop on Computing Education Research (pp. 61–68). New York, NY: ACM. https://doi.org/10.1145/2016911.2016926.
- 13.Seddon, F., & Biasutti, M. (2009). Participant approaches to and reflections on learning to play a 12-bar blues in an asynchronous e-learning environment. International Journal of Music Education, 27(3), 189–203.Google Scholar
- 15.Rust, C., O’Donovan, B., & Price, M. (2005). A social constructivist assessment process model: How the research literature shows us this could be best practice. Assessment & Evaluation in Higher Education, 30(3), 231–240.Google Scholar
- 16.Sabitha, A. S., Mehrotra, D., Bansal, A., & Sharma, B. K. (2016a). A naive bayes approach for converging learning objects with open educational resources. Education and InformationTechnologies, 21(6), 1753–1767.Google Scholar
- 17.Sabitha, A. S., Mehrotra, D., & Bansal, A. (2016b). An ensemble approach in converging contents of LMS and KMS. Education and Information Technologies, 1–22.Google Scholar
- 18.Sabitha, A. S., Mehrotra, D., & Bansal, A. (2015). Delivery of learning knowledge objects using fuzzy clustering. Education and information technologies, 1–21.Google Scholar
- 19.Sabitha, S., Mehrotra, D., & Bansal, A. (2014). A data mining approach to improve re-accessibility and delivery of learning knowledge objects.Interdisciplinary Journal of E-Learning and Learning Objects, 10, 247–268.Google Scholar
- 20.Zhang, Y., Tangwongsan, K., & Tirthapura, S. (2017). Streaming Algorithms for k-Means Clustering with Fast Queries. arXiv preprint arXiv:1701.03826.