Using learning analytics to identify successful learners in a blended learning course

Kotsiantis, Sotiris; Τσέλιος, Νικόλαος; Filippidi, Andromahi; Komis, Vassilis

doi:10.1504/ijtel.2013.059088

Cited by 48 publications

(50 citation statements)

References 30 publications

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“…It was revealed that an (ir)regularity of the learning interval showed to have correlation with learning performance. Kotsiantis et al (2013) used Learning Analytics tool called Moodle Parser to collect data from logs and to identify successful learners in blended learning course through students activities in Moodle. The study found that students' failures were associated with their negative attitudes and perceptions towards Moodle.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…The variables used as input the development of the Learning Analytics tool were solicited from previous research such as in Kotsiantis et al, 2013;Mödritscher et al, 2013;Yu & Jo, 2014). These variables are shown in Table 1.…”

Section: Design and Development Of Learning Analytics Toolmentioning

confidence: 99%

“…Counting a number of posts a student has contributed in the discussion forums (Davies & Graff, 2005;Kotsiantis et al, 2013;Yu & Jo, 2014) With variables in Table 1, the Learning Analytics tool was linked with the LMS database so that real time information can be generated. The user interface was designed so that user can run the system with minimal computer knowledge.…”

Section: Number Of Forum Postsmentioning

confidence: 99%

“…Given the penetration and continue adoption of LMS throughout higher education in Tanzania, it was necessary to find out if these systems have impact on students' performance. Therefore, the use of Learning Analytics tool which extracts data from LMS database files provide a lot of information about students activities in the system and such information can be used to provide informed decisions about possible problems and suitable ways to address them more effectively (Kotsiantis et al, 2013).…”

Section: 4mentioning

confidence: 99%

“…Since most of learners activities take place in the LMS, Learning Analytics tools can potentially utilize log data to provide crucial information on how learning processes occur throughout the login duration . Consequently, they can provide institutions informed decisions about possible problems related to students' learning (Kotsiantis et al, 2013;Phillips et al, 2011). Compared with subjective methods such as surveys and questionnaires, Learning Analytics can capture learners' authentic interactions in real time.…”

Section: Introductionmentioning

confidence: 99%

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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: Design and Development Of Learning Analytics Toolmentioning

confidence: 99%

Section: Number Of Forum Postsmentioning

confidence: 99%

Section: 4mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Algorithms for the Development of Deep Learning Models for Classification and Prediction of Learner Behaviour in MOOCs

Fotso

Batchakui

Nkambou

et al. 2022

Artificial Intelligence for Data Science in Theory and Practice

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Tool-Mediated HCI Modeling Instruction in a Campus-Based Software Quality Course

Katsanos¹,

Xenos²,

Τσέλιος³

2018

Human-Computer Interaction. Theories, Methods, and Human Issues

Self Cite

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The Keystroke Level Model (KLM) and Fitts' Law constitute core teaching subjects in most HCI courses, as well as many courses on software design and evaluation. The KLM Form Analyzer (KLM-FA) has been introduced as a practitioner's tool to facilitate web form design and evaluation, based on these established HCI predictive models. It was also hypothesized that KLM-FA can also be used for educational purposes, since it provides step-by-step tracing of the KLM modeling for any web form filling task, according to various interaction strategies or users' characteristics. In our previous work, we found that KLM-FA supports teaching and learning of HCI modeling in the context of distance education. This paper reports a study investigating the learning effectiveness of KLM-FA in the context of campus-based higher education. Students of a software quality course completed a knowledge test after the lecture-based instruction (pre-test condition) and after being involved in a KLM-FA mediated learning activity (post-test condition). They also provided posttest ratings for their educational experience and the tool's usability. Results showed that KLM-FA can significantly improve learning of the HCI modeling. In addition, participating students rated their perceived educational experience as very satisfactory and the perceived usability of KLM-FA as good to excellent.

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Using learning analytics to identify successful learners in a blended learning course

Cited by 48 publications

References 30 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

Algorithms for the Development of Deep Learning Models for Classification and Prediction of Learner Behaviour in MOOCs

Tool-Mediated HCI Modeling Instruction in a Campus-Based Software Quality Course

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