A Predictive Model to Evaluate Student Performance

Author:

Sorour Shaymaa E.12,Mine Tsunenori3,Goda Kazumasa4,Hirokawa Sachio5

Affiliation:

1. Graduate School of Information Science and Electrical Engineering

2. Faculty of Specific Education, Kafr Elsheik University

3. Faculty of Information Science and Electrical Engineering, Kyushu University

4. Kyushu Institute of Information Science

5. Research Institute for Information Technology, Kyushu University

Publisher

Information Processing Society of Japan

Subject

General Computer Science

Reference31 articles.

1. [1] Abdousa, M.: A predictive Study of Learner Satisfaction and Outcomes in Face-to-Face, Satellite Broadcast, and Live Video-Streaming Learning Environments, The Internet and Higher Education, Vol.13, No.4, pp.248-257 (2010).

2. [2] Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R. and Honrao, V.: Predicting Students' Using ID3 and C4.5 Classification Algorithms, International Journal of Data Mining and Knowledge Management Process (IJDKP), Vol.3, No.5, pp.39-52 (2013).

3. [3] Ananiadou, S.: National Centre for Text Mining: Introduction to Tools for Researchers, (online), available from <http://www.jisc.ac.uk/publications/publications/> (2008).

4. [4] Antai, R., Fox, C. and Kruschwitz, U.: The Use of Latent Semantic Indexing to Cluster Documents into Their Subject Areas., Fifth Language Technology Conference. Springer. ASME Design Engineering Technical conferences, DETC2001/DTM (2011).

5. [5] Baker, R.S.J.D.: Data Mining for Education, International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier (2009).

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