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

By analyzing the overall learning situation, we can provide personalized diagnostic reports for each student on the basis of the existing data. These reports include the learning process, outcomes, problems, and recommendation through visual presentation for the whole semester. Visual comparison of the results also includes personal and class average grades, so that students can clearly understand their own performance. What’s more, they are provided with evidence of learning reflection on the learning platform. The teachers therefore have a comprehensive and detailed understanding of the students’ learning status in a data-driven assessment. It can also provide valuable reference for improving teaching methods, curriculum construction, learning resource development, learning activities design, supervision, and intervention of the learning process. At the same time, it provides useful information to optimize the allocation of resources for academic administrators. As shown in Figure 9, a student was selected as a sample to show his personal learning diagnosis report. It provides a clear description of the student online learning process and outcome, which can help the student to know the problems in his/her online learning behavior.

As shown in Figure 9, the student’s learning diagnosis report is composed of six aspects:

  • Access Frequency Analysis
  • Resource utilization rate
  • Completion of quizzes
  • Forum interaction.
  • Score of all kinds of achievements.
  • The distribution of comprehensive performance

MOODLE data gathering description

Education institutions often use learning management systems (LMS), such as Moodle, Edmodo, Canvas, Schoology, Blackboard Learn, and others. When accessing these systems with their personal account, each student’s activity is recorded in a log file. Besides analyzing the raw data from log files directly, there is an option to use Moodle plugins that provide learning analytics and enable the faster analysis of students’ behavior on LMS.

Example of The Moodle Activity Viewer (MAV) – Heatmaps of Student Activity representing visually number of student click percentage by scaling colors

Further reading

Analysis of student behavior in learning management systems through a Big Data framework

Magdalena Cantabella, Raquel Martínez-España, Belén Ayuso, Juan Antonio Yáñez, Andrés Muñoz

What Learning Analytics Tells Us: Group Behavior Analysis and Individual Learning Diagnosis based on Long-Term and Large-Scale Data

Jia-Hua Zhang, Ye-Xing Zhang, Qin Zou and Sen Huang

Analyzing students online learning behavior in blended courses using Moodle

Rosalina Rebucas Estacio (College of Computer Studies and Engineering, Jose Rizal University, Mandaluyong City, Philippines)

Rodolfo Callanta Raga Jr (College of Computer Studies and Engineering, Jose Rizal University, Mandaluyong City, Philippines)

Analysis of Student Behavior and Success Based on Logs in Moodle Nikola Kadoić* and Dijana Oreški**

The Moodle Activity Viewer (MAV) – Heatmaps of Student Activity