Analyzing students online learning behavior in blended courses using Moodle

Abstract

For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The purpose of this paper is to describe a proposal for a data-driven investigation aimed at determining whether students' learning behavior can be extracted and visualized from action logs recorded by Moodle. The paper also tried to show whether there is a correlation between the activity level of students in online environments and their academic performance with respect to final grade. Design/methodology/approach -The analysis was carried out using log data obtained from various courses dispensed in a university using a Moodle platform. The study also collected demographic profiles of students and compared them with their activity level in order to analyze how these attributes affect students' level of activity in the online environment.Findings -This work has shown that data mining algorithm like vector space model can be used to aggregate the action logs of students and quantify it into a single numeric value that can be used to generate visualizations of students' level of activity. The current investigation indicates that there is a lot of variability in terms of the correlation between these two variables.Practical implications -The value presented in the study can help instructors monitor course progression and enable them to rapidly identify which students are not performing well and adjust their pedagogical strategies accordingly. Originality/value -A plan to continue the work by developing a complete dashboard style interface that instructors can use is already underway. More data need to be collected and more advanced processing tools are necessary in order to obtain a better perspective on this issue.

[Retracted] Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

“…In other words, the key challenge is to find the relevant dynamic characteristics of an individual student for a specific prediction task which would later help properly adapting the learning object for them [13]. Among the most important dynamic characteristics of students in a course are their learning behavior or preferences with regard to accessing learning resources, engaging with peers, and taking assessment tests [14]. Multiple research has concluded that students' activity data logged in different LMSs (like Moodle) could be used to represent their learning behavior in online courses (e.g., [14,15]).…”

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

“…At the same time, although the Moodle system provides some reporting tools, it does not provide specific features which can enable educators to directly monitor and evaluate the activities of students in relation to the structure and contents of the course and how it affects the learning process (Zorrilla, Millan, & Menasalvas, 2005). Based on experience, this keeps instructors from making meaningful sense and use of this data (Estacio & Raga, 2017 This paper describes a pilot study that discusses and illustrates the use of a novel approach in analyzing the log data generated by Moodle in a blended learning context. The proposed technique can be used to process and break down the multidimensional log data collected by the LMS in order to generate graphical representations that provide a profile of students' activities online, both individually and within a group.…”

Training Female Entrepreneurs: A Self-Governed or Tutor-Driven Learning Process?

“…Its quantification is not required in terms of the research question. Constructing separate models for each type of interactions accounts for students' preferences towards different participation forms and learning styles (Estacio and Raga Jr, 2017) and multiple viewpoints in the behavior analysis (Dobashi, 2017).…”

“…Following broadly validated recommendations (Hernández García et al, 2017, Gašević et al, 2016, Davies and Graff, 2005, Estacio and Raga Jr, 2017, we operate with interactions' frequency instead of their duration. The argumentation behind is the absence of information in the latter upon the fact that the agent actually performed the activity within the registered time, which becomes especially problematic when the resources are available for download.…”