A Conceptual Model for Measuring and Supporting Self-Regulated Learning using Educational Data Mining on Learning Management Systems

Author(s):  
Eric ARAKA ◽  
Elizaphan MAINA ◽  
Rhoda GITONGA ◽  
Robert OBOKO
2022 ◽  
pp. 199-218
Author(s):  
Chandana Aditya

There is a pressing need for data management and learning management systems. Educational data mining and learning analytics are two related aspects of educational technology that promote an overall effective teaching-learning system. The news media has the potential to act as a tool of learning analytics since they can easily access information at a mass scale. There are instances of leading newspapers organizing different educational programs where students from all the social layers have an opportunity to participate. A review of the programs reveals that all the programs collect and analyze educational data, which can form a research base of learning analytics. This chapter presents the description of three such educational programs organized by the leading media houses of India. This chapter also reflects on the contribution to learning management systems and educational data mining for the improvement of the overall educational system.


Author(s):  
Sarah E. Heath ◽  
Beau Shine

While self-regulated learning is a standard model for online coursework, this approach emphasizes the applicability of Learning Management System (LMS) usage in face-to-face and hybrid course formats. Self-regulated learning has become an important component of education, both as a primary tool in online coursework and as a supplemental resource in face-to-face courses. (Boekaerts, 1999).  Yet despite its importance, research suggests that rather than utilizing the full potential of learning management and course management systems, instructors primarily use LMS and CMS as a delivery mode for course content (Boekarts, 1997; Vovides et al., 2007).  Such underutilization not only minimizes the capacities of such systems, but limits the opportunities for students to engage in multimodal self-regulated learning. This paper offers three specific techniques to improve self-regulated learning via LMS: flipped learning, chunking, and micro-learning.  Research findings have led to support for each of the above-mentioned techniques (Nwosisi et al., 2016; Miller, 1956; Major & Calandrino, 2018). The authors provide examples of techniques used in their own courses, how each facilitates self-regulated learning, and how utilizing the full capabilities of learning management systems engages students in multimodal self-regulated learning.  Common findings and recommendations will also be noted, with the goal of providing a framework for instructors to apply each technique via learning management systems in their own courses.


Author(s):  
Owen McGrath

Free and Open Source Software (FOSS)/Open Educational Systems development projects abound in higher education today. Many universities worldwide have adopted open source software like ATutor and Moodle as an alternative to commercial or homegrown systems. The move to open source learning management systems entails many special considerations, including usage analysis facilities. The tracking of users and their activities poses major technical and analytical challenges within web-based systems. This paper examines how user activity tracking challenges are met with data mining techniques, particularly web usage mining methods, in four different open learning management systems: ATutor, LON-CAPA, Moodle, and Sakai. As examples of data mining technologies adapted within widely used systems, they represent important first steps for moving educational data mining outside the research laboratory. Moreover, as examples of different open source development contexts, exemplify the potential for programmatic integration of data mining technology processes in the future. As open systems mature in the use of educational data mining, they move closer to the long-sought goal of achieving more interactive, personalized, adaptive learning environments online on a broad scale.


2010 ◽  
Vol 2 (1) ◽  
pp. 65-75
Author(s):  
Owen McGrath

Free and Open Source Software (FOSS)/Open Educational Systems development projects abound in higher education today. Many universities worldwide have adopted open source software like ATutor and Moodle as an alternative to commercial or homegrown systems. The move to open source learning management systems entails many special considerations, including usage analysis facilities. The tracking of users and their activities poses major technical and analytical challenges within web-based systems. This paper examines how user activity tracking challenges are met with data mining techniques, particularly web usage mining methods, in four different open learning management systems: ATutor, LON-CAPA, Moodle, and Sakai. As examples of data mining technologies adapted within widely used systems, they represent important first steps for moving educational data mining outside the research laboratory. Moreover, as examples of different open source development contexts, exemplify the potential for programmatic integration of data mining technology processes in the future. As open systems mature in the use of educational data mining, they move closer to the long-sought goal of achieving more interactive, personalized, adaptive learning environments online on a broad scale.


Author(s):  
Eric Araka ◽  
Robert Oboko ◽  
Elizaphan Maina ◽  
Rhoda K. Gitonga

Self-regulated learning is attracting tremendous researches from various communities such as information communication technology. Recent studies have greatly contributed to the domain knowledge that the use self-regulatory skills enhance academic performance. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Modern learning management systems (LMS) allow storage of datasets on student activities. Subsequently, it is now possible to use Educational Data Mining to extract learner patterns which can be used to support SRL. This chapter discusses the current tools for measuring and promoting SRL on e-learning platforms and a conceptual model grounded on educational data mining for implementation as a solution to promoting SRL strategies.


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