Harnessing Learning Analytics to Improve Online Quiz Equity

Abstract

The use of online learning management systems (LMS) such as Blackboard, Canvas and Moodle is becoming a norm in higher education. In general, these systems provide tools to facilitate active learning such as discussion forums and student assessments such as online quizzes. Studies have shown that students who do pre-class online quizzes that encourage preparatory reading perform better in examinations. The Singapore University of Social Sciences (SUSS) is a university that caters primarily to working adults. In line with self-directed lifelong learning, it uses pre-class online quizzes to encourage students to self-study. SUSS uses Canvas as the learning management system to implement these quizzes. Students sit for 3 pre-class quizzes and need to obtain at least 12 out of 20 questions correct for the first pre-class quiz before class starts to be allowed to attend class. This motivates students to engage the course materials before the classes start. The faculty will usually prepare a bank of questions from which the questions for the pre-class quizzes will be randomly drawn for each student. The level of difficulty for the questions varies. This raises the issue of quiz equity despite the random allocation of quiz questions. In this context, this study investigates how the current method of question allocation has affected the equity of the quiz. It also proposes a solution to mitigate quiz inequity. With an integration of learning analytics and problem solving, we hope to provide a different approach to implementing online quizzes that will be more equitable.

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Author Information
Jess Wei Chin Tan, Singapore University of Social Sciences, Singapore
Chong Hui Tan, Singapore University of Social Sciences, Singapore
Hian Chye Koh, Singapore University of Social Sciences, Singapore

• Category: Assessment Theories & Methodologies

Posted by James Alexander Gordon

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