Diagnosing and remediating mathematical common student errors in e-assessment questions (A case study)

Sikurajapathi, Bopitiye

Abstract

Mistakes and misconceptions in mathematics are as common as those in any real life situation. The errors frequently made by a myriad of students, known as Common Students Errors (CSEs), heavily influence their ability to learn and retain mathematics. Identifying and correcting CSEs on handwritten work is straightforward since teachers have access to the students’ intermediate workings. One major drawback of this approach is the time and effort it takes to mark and to get feedback back to the individual student.

In recent years, e-Assessments have become a pivotal method of providing mathematical assessments in education. However, there is increasing concern that e-Assessment cannot act like a human-marker to identify and correct CSEs in mathematical e-Assessment questions. Therefore, how to diagnose and remediate CSEs in e-Assessment questions has long been a question of great interest in research in e-Assessments.

This thesis explores a method to diagnose and remediate CSEs in Engineering Mathematics (EM) e-Assessment questions. Firstly, this study contributes to existing knowledge of mathematical CSEs by systematically gathering and taxonomically compiling CSEs in EM e-Assessment questions and producing an interactive book. Secondly, different features of e-Assessment questions, to capture mathematical CSEs and provide enhanced feedback to correct those CSEs, are developed. Thirdly, new light is shed on our understanding of parameter selection restrictions of some mathematical e-Assessment questions which have at least one CSE. Fourthly, it offers some important insights into students’ perceptions on the CSE enhanced feedback by analysing a questionnaire. Finally, it provides strong empirical confirmation that the CSE enhanced feedback has successfully corrected some mathematical CSEs in the majority of students who participated in this study.

The outcomes of this thesis contribute in several ways to our understanding of mathematical CSEs and addressing them in e-Assessment questions, and provides a basis for further research.

• Thesis Type: Thesis
• Deposit Date: Mar 6, 2023
• Publicly Available Date: Oct 12, 2023
• Public URL: https://uwe-repository.worktribe.com/output/10505487
• Award Date: Oct 12, 2023