Abstract:
The learning objectives, learning activities and
assessment are very much interrelated. Assessment helps to
evaluate students learning achievement. Poorly designed
assessments usually fail to examine the achievement of intended
learning outcome of a course. There are different taxonomies that
have been developed to identify the level of the assessment being
practiced such as Bloom’s and SOLO. In this research we have
studied the use of WordNet with Cosine similarity algorithm for
classifying a given exam question according to Bloom’s taxonomy
learning levels. WordNet similarity algorithm depends on the
extracted verbs from exam question. Cosine similarity algorithm
was based on identification of question patterns of exam
question. It consists of tag pattern generation module, grammar
generation module, parser generation and cosine similarity
checking module. This algorithm was helpful to classify the exam
question where verbs were not present in exam questions. Exam
questions taken from courses at the Department of Computing
and Information Systems at Wayamba University were used as a
basis for a performance comparison, with the autonomous system
providing classifications that were consistent with those provided
by domain experts on approximately 71% of occasions.
Citation:
K. Jayakodi, M. Bandara and D. Meedeniya, "An automatic classifier for exam questions with WordNet and Cosine similarity," 2016 Moratuwa Engineering Research Conference (MERCon), 2016, pp. 12-17, doi: 10.1109/MERCon.2016.7480108.