On identifying advanced, average and slow learners: Case study

Abstract

Engineering education is dynamically changing its face by transforming from conventional passive learning to active learning across the globe. Active learning is a wellproven technique to enrich the understanding level of learners. However, the understanding, comprehension, and intellectual capabilities of every individual are different. It is highly important to identify these capabilities and provide the required knowledge feeding to intensify the curiosity of a learner in a specific course. Since the same course may not be understood by all the learners at the same depth, it is required to characterize the learning abilities of every learner. Thus, the learner community can be classified into advanced, average, and slow learners. In this paper, a strategy based on Bloom’s Taxonomy (revised) is proposed for identifying advanced, average and slow learners in a course. For that, firstly, an online open-book test (OBT) covering all the levels of revised Bloom’s Taxonomy is innovatively designed for each course. Secondly, the OBT is conducted as per schedule and question-wise, and thereby, Bloom’s Taxonomy cognitive level-wise assessment is completed for all the courses. Thirdly, coursewise students are ranked from highest to lowest performance levels using statistical tools. Finally, using predecided thresholds, learners in a course are identified as advanced, average and slow. The application of this learner classification approach is demonstrated by considering the case of courses in the second, third, and final year of the Electrical Engineering program. The presented approach has substantial potential to be used for designing and planning course delivery to accommodate all types of learners.