INFORMATION CHANGE THE WORLD

International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.5, No.5, Jun. 2013

Student Learning Ability Assessment using Rough Set and Data Mining Approaches

Full Text (PDF, 610KB), PP.1-11


Views:204   Downloads:4

Author(s)

A. Kangaiammal,R. Silambannan,C. Senthamarai,M.V. Srinath

Index Terms

Learning Ability;Pre-test;Post-test;Continuous Assessment;Rough Set Approach;Decision System

Abstract

All learners are not able to learn anything and everything complete. Though the learning mode and medium are different in e-learning mode and in classroom learning, similar activities are required in both the modes for teachers to observe and assess the learner(s). Student performance varies considerably depending upon whether a task is presented as a multiple-choice question, an open-ended question, or a concrete performance task [3]. Due to the dominance of e-learning, there is a strong need for an assessment which would report the learning ability of a learner based on the learning skills under various stages. This paper focuses on assessment through multiple choice questions at the beginning and at the end of learning. The learning activities of the learner are tracked during the learning phase through a Continuous Assessment test to realize the understanding level of the learner. The scores recorded in the database is analyzed using a Rough Set Approach based Decision System. The effectiveness of teaching learning process indicates the learning ability of the learner, presented in a Graphical form. It is evident from the results that the entry behavior and the behavior while learning determine the actual learning. Students generate internal opinion as they monitor their engagement with learning activities and tasks and also assess progress towards goals. Those who are effective at self-regulation, however, produce better feedback or are able to use the self-opinion they generate to achieve their desired goals. The tool developed assists the teacher to be aware of the learning ability of learners before preparing the content and the presentation structure towards complete learning. In other words, the developed tool helps the learner to self-assess the learning ability and thereby identify and focus to gain the lacking skills.

Cite This Paper

A. Kangaiammal,R. Silambannan,C. Senthamarai,M.V. Srinath,"Student Learning Ability Assessment using Rough Set and Data Mining Approaches",IJMECS, vol.5, no.5, pp.1-11, 2013.DOI: 10.5815/ijmecs.2013.05.01

Reference

[1]Adibi, M., “The effect of information and communication technology on the educational improvement of secondary students”, (Unpublished master’s thesis). Islamic Azad University-Garmsar Branch, 2010.

[2]Angelo, Th. A. and K. P. Cross, “Classroom Assessment Techniques”, San Francisco: Jossey-Bass, 2nd edition, 1993.

[3]Baxter, G. P., and R. J. Shavelson, “Science performance assessments: Benchmarks and surrogates”, International Journal of Educational Research 21 (3): 279–98, 1994.

[4]David J. Nicol, Debra Macfarlane-Dick, “Formative assessment and self-regulated learning: A model and seven principles of good feedback practice Studies in Higher Education”, Vol 31(2), 199-218, 2006.

[5]Etienne, P. and Van Den Stock, A., “E-learning-assistant: Situation-based learning in education”, Computer and Education Journal, 39(3), 224-226, 2010.

[6]European Communities, “Classification of Learning Activities - Manual: Methods and Nomenclature”, ISSN 1725-0056, ISBN 92-79-01806-X. Luxembourg: Office for Official Publications of the European Communities, 2006.

[7]George J., “Social grid platform for collaborative online learning on blogosphere: A case study of eLearning. Expert Systems with Applications”, 36, 2177–2186, 2009.

[8]Han, J., Kamber, M. Data Mining Concepts and Techniques, Morgan Kaufmann Publisher, 2001.

[9]In-Sook Lee, “Learners' Perceptions and Learning Styles in the Integrated Mode of Web-based Environment”, AECT International Convention Feb. 16-20 Long Beach, USA, 2000.

[10]Kangaiammal A., Malliga P, Jayalakshmi P and Sambanthan TG, “Problem Centric Instrumental Approach for different Delivery Modes of Computer Science Subjects”, The Indian Journal of Technical Education, Volume 33 No.3 pg 31-39, July –Sep 2010, ISSN 0971-3034.

[11]Kangaiammal A., “A Study on the Competencies through Institutionalized and Distance Modes of MCA Programme”, Ph.D thesis, University of Madras. (Permitted by the University to publish research materials of the thesis), Oct. 2008.

[12]Malliga. P, Sambanthan TG, “ Effectiveness of Problem Centric Approach in e-content of Computer Science and Engineering”, Conference Proceedings of International Conference on e- Resources in Higher Education, Issues, Development, Opportunities and Challenges, BU, Tiruchirappali, Feb 2010, pg 149-153, ISBN 978-81-908078-9-0.

[13]Office of Assessment, “Teaching and Learning. (2010). Developing appropriate assessment tasks”. In Teaching and Learning at Curtin 2010. (Pp.22-44). Curtin University: Perth, 2010.

[14]Pawlak, Z, “Rough Set”, International Journal of Computer and Information Sciences 341–356, 1982.

[15]Quinlan, J.R., “C4.5: Programs for Machine Learning”. Morgan Kaufman. 1993.

[16]Thomas R. Guskey, “How classroom assessments can improve learning from educational leadership”, Volume 60, No.5, 7-11, 2003.