Optimising Educational Outcomes: Data and Process Analysis Approaches with Attention to Self-Directed Learning

Abstract

The swift advancements in technology and the corresponding job market impose increasingly challenging and dynamic requirements on workers. This is a significant obstacle for higher education institutions in adequately preparing their students for contemporary expectations and equipping them to tackle future difficulties. Today's students are "digital natives", and they distinctly absorb knowledge and employ new strategies to learn compared to earlier generations. Hence, it is of utmost significance for higher education institutions to comprehend the student learning process. Learning management systems (LMS) can offer substantial assistance in this endeavor, as they facilitate comprehension of students' learning process, while log files also offer unbiased insights into individual adaptation. This study aims to investigate the learning mechanisms of Business Informatics students at Corvinus University of Budapest by analyzing Moodle's educational data. The objective of the study was to acquire a more comprehensive understanding of the learning patterns exhibited by students in higher education through the utilization of an extensive collection of log files. The central idea revolved around examining the behavioral, motivational, and interest-related dimensions of learning as indicators of self-directed learning. These were examined using two primary methodologies: data analysis and process analysis. The findings indicate that distinct learning patterns exist regarding data and learning processes. Additionally, there are variations in time management and information consumption habits. The results of this study have practical implications for identifying learning patterns and developing tailored interventions to enhance educational achievements.