Analyzing the log patterns of adult learners in LMS using learning analytics

Jo, Il-Hyun; Kim, Dongho; Yoon, Meehyun

doi:10.1145/2567574.2567616

Cited by 39 publications

(27 citation statements)

References 20 publications

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“…Mason () accentuated that if learners have enough time to be absorbed in learning in order to become familiar with subjects, a meaningful discussion would be possible. In prior study (Jo, Kim, & Yoon, ), time investment was calculated as a total log‐in time within the learning management system (LMS), obtained by adding up each time spent per visit. Likewise, time spent on board was suggested on the basis of an assumption that students who spend more time within the discussion board per access can be regarded as an active learner participating deeply in discussion.…”

Section: Three Interaction Modes and Analytic Methods In Aodmentioning

confidence: 99%

Three interaction patterns on asynchronous online discussion behaviours: A methodological comparison

Park

Lee³

2017

Computer Assisted Learning

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An asynchronous online discussion (AOD) is one format of instructional methods that facilitate student-centered learning. In the wealth of AOD research, this study evaluated how students' behavior on AOD influences their academic outcomes. This case study compared the differential analytic methods including web log mining, social network analysis and content analysis which were selected by three interaction patterns: person to system (P2S), person to person (P2P) and person to content (P2C) interaction. Forty-three undergraduate students participated in an online discussion forum for 12 weeks. Multiple regression analyses with the predictor variables from P2S, P2P and P2C and with a criterion variable of a final grade indicated several interesting findings. For P2S analysis, visits on board (VOB) had a significant variable to predict final grades. Also, the result of P2P analysis proved that in-degree and out-degree centrality predicted final grades. The P2C results based on cognitive presence represent that students' messages were mostly affiliated to the exploration and integration levels and also predicted the final grades. This study ultimately demonstrated the effectiveness of using multiple analytic methodologies to address and facilitate students' participation at AOD.

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Section: Three Interaction Modes and Analytic Methods In Aodmentioning

confidence: 99%

Three interaction patterns on asynchronous online discussion behaviours: A methodological comparison

Park

Lee³

2017

Computer Assisted Learning

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“…Therefore, there is a need for institutions to measure the quality and intensity of LMS usage. In fact, studies have shown that there is a correlation between LMS usage with students' performance (Filippidi et al, 2010;Jo et al, 2014;Macfadyen & Dawson, 2010;Whitmer, 2012) as well as students' satisfaction with courses offered via LMS (Naveh et al, 2012).…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…They found that total studying time, interaction with peers, regularity of learning interval, and number of downloads had significant effect on students' academic perfomance in online learning environment. Jo et al (2014) identified "candidates for proxy variables" from the log data set that could be used as factors to determine students' performances in courses offered via Moodle. The authors used the identified variables to pilot them in commercial eLearning course with a total of 200 students.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Using Learning Analytics to Predict Students’ Performance in Moodle Learning Management System: A Case of Mbeya University of Science and Technology

Mwalumbwe

Mtebe

2017

E J Info Sys Dev Countries

113

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The past decade has seen the rapid adoption and use of various Learning Management Systems (LMS) in Africa, and Tanzania in particular. Institutions have been spending thousands of dollars to implement these systems in a bid to improve the quality of education as well as increasing students' enrolments through distance and blended learning. However, the impact of these system on improving students' performance has been a popular subject of research in recent years. Studies have been relying on data from users' opinions and subjective interpretation through surveys to determine the effectiveness of LMS usage on students' learning performance. The use of such data is normally subject to the possibility of distortion or low reliability. Therefore, this study designed and developed Learning Analytics tool and used the tool to determine the causation between LMS usage and students' performance. Data from LMS log of two courses delivered at Mbeya University of Science and Technology (MUST) were extracted using developed Learning Analytics tool and subjected into linear regression analysis with students' final results. The study found that discussion posts, peer interaction, and exercises were determined to be significant factors for students' academic achievement in blended learning at MUST. Nonetheless, time spend in the LMS, number of downloads, and login frequency were found to have no significant impact on students' learning performance. The implications of these results on improving students' learning are discussed.

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“…Specific moments in the semester claim more attention from practitioners interested in contributing to the learners' success. Lastly, Jo, Kim, and Yoon () sketch a process of producing proxy variables that represent adult learners' time management strategies to notice if regularity of the learning interval is correlative with and predict students' performance; whilst Brooks, Thompson, and Teasley () design a method that analyses interactions between learners and resources to generate features suitable for building predictive models of student success.…”

Section: Applicationsmentioning

confidence: 99%

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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Analyzing the log patterns of adult learners in LMS using learning analytics

Cited by 39 publications

References 20 publications

Three interaction patterns on asynchronous online discussion behaviours: A methodological comparison

Three interaction patterns on asynchronous online discussion behaviours: A methodological comparison

Using Learning Analytics to Predict Students’ Performance in Moodle Learning Management System: A Case of Mbeya University of Science and Technology

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

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