ERIC Number: EJ1415619
Record Type: Journal
Publication Date: 2024
Pages: 19
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-2050-7003
EISSN: EISSN-1758-1184
Available Date: N/A
Assessing Impact of Problem-Based Learning Using Data Mining to Extract Learning Patterns
Shilpa Bhaskar Mujumdar; Haridas Acharya; Shailaja Shirwaikar; Prafulla Bharat Bafna
Journal of Applied Research in Higher Education, v16 n2 p610-628 2024
Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India. Design/methodology/approach: Study employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions. Findings: The administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance. Research limitations/implications: This study is limited to course students from one institute and does not consider external factors. Practical implications: Researchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions. Originality/value: 1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.
Descriptors: Problem Based Learning, Information Retrieval, Data Analysis, Pattern Recognition, Learning Processes, Undergraduate Students, Grade Point Average, Academic Achievement, Attendance, Student Characteristics, Gender Differences, Educational Background, Mathematics
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A