Topic 1 Introduction to digital and non-digital tools and approaches for analyzing data on learners behaviors / attitude and performance

Understanding analysis of student behaviour

In recent years, learning management systems (LMSs) have played a fundamental role in higher education teaching models. A new line of research has been opened relating to the analysis of student behavior within an LMS, in the search for patterns that improve the learning process. Current e-learning platforms allow for recording student activity, thereby enabling the exploration of events generated in the use of LMS tools.

This paper presents a case study conducted at the Catholic University of Murcia, where student behavior in the past four academic years was analyzed according to learning modality (that is, on-campus, online, and blended), considering the number of accesses to the LMS, tools employed by students and their associated events. Given the difficulty of managing the large volume of data generated by users in the LMS (up to 70 GB in this study), statistical and association rule techniques were performed using a Big Data framework, thus speeding up the statistical analysis of the data. The obtained results are demonstrated using visual analytic techniques, and evaluated in order to detect trends and deficiencies in the use of the LMS by students.

Understanding education data mining and learning analytics

The practice and application of education data mining and learning analytics has become the focus of educational researchers. However, it is still a difficult task to explore the law of group learning and the characteristics of individual learning. In this study, the online learning logs of 1,088 students from 22 classes were analyzed from the aspects of their login behaviors, resource utilization, quizzes, interactive behaviors, and academic achievement. To address these issues, multiple methods, including statistical analysis, visualization social network analysis and correlation analysis, were used to analyze the process and results of online learning. The results reveal the characteristics of group behavior of online learners and highlight the key factors that influence the learning process and outcomes of individual learners. From the view of students, these factors include the length and allocation of online time, the effective utilization of resources, social interaction, online learning support and services, etc. From the perspective of teachers, the factors include the management of online teaching, the appropriateness of learning resources, the effectiveness of online intervention strategies, the accurate feedback for online learners, etc. Therefore, learning analysis technology can not only standardize the assessment of learning outcomes, but can also focus more attention on the standardization of learning process assessment. It also identifies the main factors that affect the online learning outcomes and the group characteristics of online learners. At the same time, it provides the learners with personalized learning diagnosis reports which can both help learners understand their own learning status and promote instructors’ accurate teaching and reasonable evaluation