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 analysis; (2) qualitative data analysis; and (3) integration and discussion of results. Furthermore, we illus-trated 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' per-ception 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 environ-ments and learning management systems compared to qualitative data. Methodology This study uses a mixed-methods, non-experimental, research design. The quan-titative phase of the framework aims to analyze the data to identify behaviors, trends, and relationships between measures using correlation or regression anal-ysis. On the other hand, the qualitative phase of the framework focuses on con-ducting a content analysis of the qualitative data. This framework was applied to