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Discovering Insights in Learning Analytics Through a Mixed-Methods Framework: Application to Computer Programming Education
Aim/Purpose: This article proposes a framework based on a sequential explanatory mixed-methods design in the learning analytics domain to enhance the models used to support the success of the learning process and the learner. The framework consists of three main phases: (1) quantitative data analysi...
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| Published in: | Journal of information technology education 2023, Vol.22, p.339-372 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Online Access: | Get full text |
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| Summary: | Aim/Purpose: This article proposes a framework based on a sequential explanatory mixed-methods design in the learning analytics domain to enhance the models used to support the success of the learning process and the learner. The framework consists of three main phases: (1) quantitative data analysis; (2) qualitative data analysis; and (3) integration and discussion of results. Furthermore, we illustrated the application of this framework by examining the relationships between learning process metrics and academic performance in the subject of Computer Programming coupled with content analysis of the responses to a students’ perception questionnaire of their learning experiences in this subject.
Background: There is a prevalence of quantitative research designs in learning analytics, which limits the understanding of students’ learning processes. This is due to the abundance and ease of collection of quantitative data in virtual environments and learning management systems compared to qualitative data.
Methodology: This study uses a mixed-methods, non-experimental, research design. The quantitative phase of the framework aims to analyze the data to identify behaviors, trends, and relationships between measures using correlation or regression analysis. On the other hand, the qualitative phase of the framework focuses on conducting a content analysis of the qualitative data. This framework was applied to historical quantitative and qualitative data from students’ use of an automated feedback and evaluation platform for programming exercises in a programming course at the National University of Colombia during 2019 and 2020. The research question of this study is: How can mixed-methods research applied to learning analytics generate a better understanding of the relationships between the variables generated throughout the learning process and the academic performance of students in the subject of Computer Programming?
Contribution: The main contribution of this work is the proposal of a mixed-methods learning analytics framework applicable to computer programming courses, which allows for complementing, corroborating, or refuting quantitatively evidenced results with qualitative data and generating hypotheses about possible causes or explanations for student behavior. In addition, the results provide a better understanding of the learning processes in the Computer Programming course at the National University of Colombia.
Findings: A framework based on sequen |
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| ISSN: | 1547-9714 1539-3585 |
| DOI: | 10.28945/5182 |