Mining LMS data to develop an “early warning system” for educators: A proof of concept

Macfadyen, Leah P.; Dawson, Shane

doi:10.1016/j.compedu.2009.09.008

Cited by 821 publications

(598 citation statements)

References 14 publications

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“…These information include users visits, number of downloads, LMS tools accessed, messages read or posted, and content pages visited (Macfadyen & Dawson, 2010). According to Whitmer (2012), such information explain over four times the variation in final grades compared to traditional student characteristic variables, and that combining both types of variables increase the quality of predicting learning performance by more than 70%.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…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%

“…In this case, we can identify struggling students in need of academic support (Macfadyen & Dawson, 2010). We can also use Learning Analytics tools to assess the quality of online postings (Nistor et al, 2015) and visualize usage behaviors in the system (Scheffel et al, 2011).…”

Section: Literature Review and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

114

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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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Section: Literature Review and Related Workmentioning

confidence: 99%

Section: Literature Review and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

114

View full text Add to dashboard Cite

show abstract

“…Many learning analytics applications use data generated from learner activities, such as the number of clicks (Siemens, 2013;Wolff, Zdrahal, Nikolov, & Pantucek, 2013), learner participation in discussion forums (Agudo-Peregrina, Iglesias-Pradas, Conde-González, & Hernández-García, 2014;Macfadyen & Dawson, 2010), or (continuous) computer-assisted formative assessments (Author A, , 2012bWolff et al, 2013). User behaviour data are frequently supplemented with background data retrieved from learning management systems (LMS) (Macfadyen & Dawson, 2010) and other student admission systems, such as accounts of prior education (Arbaugh, 2014;Richardson, 2012).…”

Section: Introductionmentioning

confidence: 99%

In search for the most informative data for feedback generation: Learning analytics in a data-rich context

Tempelaar

Rienties

Giesbers

2015

Computers in Human Behavior

339

269

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Abstract. Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick's theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In a large introductory quantitative methods module, 922 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation.

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“…The "smorgasboard" approach to gathering any and all data related to a given student has been advocated for by influential learning analytics advocates (Diaz & Brown, 2012, p. 13) and encouraged by researchers who want to cross-pollinate data sources to create comprehensive learning analytics technologies (Campbell & Oblinger, 2007;Macfayden & Dawson, 2010;Mazza & Dimitrova, 2007). But such an approach elicits worries related to student privacy, especially when the technology can begin to capture such a complete dossier of student information, track virtual and physical movements, and influence behavior to the point of threatening personal autonomy.…”

Section: Introductionmentioning

confidence: 99%

Learning analytics & FERPA: Issues of student privacy and new boundaries of student data

Jones

2013

Proc of Assoc for Info

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Mining LMS data to develop an “early warning system” for educators: A proof of concept

Cited by 821 publications

References 14 publications

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

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

In search for the most informative data for feedback generation: Learning analytics in a data-rich context

Learning analytics & FERPA: Issues of student privacy and new boundaries of student data

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