Student Learning Ability Assessment using Rough Set and Data Mining Approaches

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.