Author:
Ibrahim A. Hameed
Affiliation:
Norwegian University of Science and Technology (NTNU), Norway
Keyword(s):
Automatic Assessment, Academic Assessment, Students’ Achievement, Efficient and Effective Assessment, Question Importance, Question Complexity, Artificial Intelligence in Education.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
e-Learning
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Learning and Teaching Systems
;
Intelligent Tutoring Systems
;
Learning/Teaching Methodologies and Assessment
;
Smart Devices as Collaborative Learning Tools
;
Ubiquitous Learning
Abstract:
Examination is one of the common assessment methods to assess the level of knowledge of students. Assessment methods probably have a greater influence on how and what students learn than any other factor. Assessment is used to discriminate not only between different students but also between different levels of thinking. Due to the increasing trends in class sizes and limited resources for teaching, the need arises for exploring other assessment methods. Multiple-Choice Questions (MCQs) have been highlighted as the main way of coping with the large group teaching, ease of use, testing large number of students on a wide range of course material, in a short time and with low grading costs. MCQs have been criticised for encouraging surface learning and its unfairness. MCQs have a variety of scoring options; the most widely used method is to compute the score by only focusing on the responses that the student made. In this case, the number of correct responses is counted, the number of i
ncorrect answers is counted and a final score is reported as either the number of the correct answers or the number of correct answers minus the number of incorrect answers. The disadvantages of this approach are that other dimensions such as importance and complexity of questions are not considered, and in addition, it cannot discriminate between students with equal total score. In this paper, a method to automatically evaluate MCQs considering importance and complexity of each question and providing a fairer way to discriminating between students with equal total scores is presented.
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