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Literature Review of Analyzing and Predicting Students’ Performance in Examinations

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Conference

2023 Fall Mid Atlantic Conference: Meeting our students where they are and getting them where they need to be

Location

Ewing, New Jersey

Publication Date

October 27, 2023

Start Date

October 27, 2023

End Date

January 10, 2024

Page Count

17

DOI

10.18260/1-2--45126

Permanent URL

https://peer.asee.org/45126

Download Count

37

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Paper Authors

biography

Sakhi Aggrawal Purdue University at West Lafayette (PPI) Orcid 16x16 orcid.org/0000-0002-2274-0152

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Sakhi Aggrawal is a Graduate Research Fellow in Computer and Information Technology department at Purdue University. She completed her master’s degree in Business Analytics from Imperial College London and bachelor’s degree in Computer and Information Technology and Organizational Leadership from Purdue University. She worked in industry for several years with her latest jobs being as project manager at Google and Microsoft. Her research interests include: workforce development, engineering education, project management and teamwork. Her current research focuses on integrating project management processes in undergraduate education. Her main goal is to understand how work management and product development practices widely used in industry can be modified and adapted to streamline undergraduate STEM education.

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Abstract

Student dropout continues to be a critical problem in education. The sooner students at risk of dropping out are identified, the sooner necessary measures can be taken to support and guide them. Schools and universities are implementing data science methods to analyze available data, identify patterns, and extract information to support decision-making and effective student learning. However, student learning is often difficult to assess. Examinations are a popular assessment tool for testing students’ knowledge, skills, and aptitude among others. Student performance in examinations can indicate their risk of dropping out, therefore, it becomes critical to analyze and predict their examination performance. In this literature review, we focus on exploring and analyzing existing work in this field to develop fundamental domain knowledge and avoid duplicative work. The specific research question was: What types of knowledge (i.e., conceptual learning, problem-solving, and model building) already exist in relation to analyzing and predicting students’ performance in examinations? The steps followed for performing this literature review were: (1) identifying the scope and research questions, (2) defining the inclusion and exclusion search criteria of literature, and (3) classifying and cataloging the literature sources that relate to analyzing and predicting students’ performance in examinations. The final data set is comprised of a total of 10 papers that meet our criteria.

Aggrawal, S. (2023, October), Literature Review of Analyzing and Predicting Students’ Performance in Examinations Paper presented at 2023 Fall Mid Atlantic Conference: Meeting our students where they are and getting them where they need to be, Ewing, New Jersey. 10.18260/1-2--45126

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