Assessment Patterns in Computing Education

Abstract

Computing education aims to develop computational thinking skills in students. It requires many higher order thinking skills such as analysis, synthesis, logical thinking etc. Quality and effectiveness of computing education is achieved by focusing these skills in the three stages of education - planning of learning-objectives, teaching and assessment. Assessment is the most important stage among these three because it determines the success of the other two stages also. But, do the current assessment patterns really test students’ higher order thinking skills? A preliminary study has been conducted in this regard on university question papers of a Master course in Computer Applications (MCA) in the Mahatma Gandhi University, India. The study used the well-known Bloom’s Taxonomy (BT) on cognitive domain as the framework. It has six levels such as Knowledge, Comprehension, Application, Analysis, Synthesis and Evaluation and each level has a set of keywords to represent the cognitive level. Questions have been collected from the MCA course for the past five academic years, from various subjects such as, Programming in C, Data Base Management Systems, Data Structures, Operating Systems, Java and Web Programming, Software Engineering, Data mining and Warehousing, Linux Internals, Computer Graphics etc. A total of 510 questions were analysed using BT keywords and were mapped to the respective cognitive levels based on the question cues. The study found that the questions mostly test their lower cognitive skills such as memorization and comprehension. The findings of the study warrants further investigation on the assessment system of various computing courses.



Author Information
Renumol V.G., Cochin University of Science and Technology, India
Rekha Sunny T, SCMS School of Technology and Management, India

Paper Information
Conference: ACE2014
Stream: Higher education

This paper is part of the ACE2014 Conference Proceedings (View)
Full Paper
View / Download the full paper in a new tab/window


Comments & Feedback

Place a comment using your LinkedIn profile

Comments

Share on activity feed

Powered by WP LinkPress

Share this Research

Posted by James Alexander Gordon