Analysing engagement in an online management programme and implications for course design

Wells, Marc; Wollenschlaeger, Alex; Lefèvre, David; Magoulas, George D.; Poulovassilis, Alexandra

doi:10.1145/2883851.2883894

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…Ding et al [ 48 ] studied the effect of gamification on student engagement in online discussion forums. Wells et al [ 49 ] studied student engagement and performance data using the LMS platform and concluded that student engagement increased as the exam approached. Additionally, they found a positive correlation between student performance and student engagement.…”

Section: Related Workmentioning

confidence: 99%

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Hussain

Zhu

Zhang

et al. 2018

Computational Intelligence and Neuroscience

264

167

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Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level, final results, score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which included dataplus, forumng, glossary, oucollaborate, oucontent, resources, subpages, homepage, and URL during the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.

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Section: Related Workmentioning

confidence: 99%

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Hussain

Zhu

Zhang

et al. 2018

Computational Intelligence and Neuroscience

264

167

View full text Add to dashboard Cite

show abstract

“…The generic ML/DL approaches used in educational settings target prediction of learners' dropouts [18], [19], performance prediction [20]), predicting learners' engagement [21], [22], and failures prediction [23], [24], etc. Marbouti et al, used linear regression (LR) to assess learners at-risk of failure [11].…”

Section: Related Workmentioning

confidence: 99%

Improving M-Learners’ Performance Through Deep Learning Techniques by Leveraging Features Weights

et al. 2020

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Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive Mlearning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse Mlearners thus helping M-learners in enhancing their study behavior.

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“…Visual analysis has also been extensively used to analyze inter-page movement and usage patterns. Wells et al [17] visualized (among other things) student usage of discussion forums. Similar work has been performed by Kop et al [18] who created graphs of the social interactions between MOOC participants, as well as by Hocheiser & Schneiderman [19] and Stones & Sobol [20].…”

Section: B Analyzing Students' Use Of Online Learning Materialsmentioning

confidence: 99%

Using and Collecting Fine-Grained Usage Data to Improve Online Learning Materials

Leppänen

Leinonen

Ihantola

et al. 2017

2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering Education and Training Track (ICSE-SE

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As educators seek to create better learning materials, knowledge about how students actually use the materials is priceless. The advent of online learning materials has allowed tracking of student movement on levels not previously possible with on-paper materials: server logs can be parsed for details on when students opened certain pages. But such data is extremely coarse and only allows for rudimentary usage analysis. How do students move within the course pages? What do they read in detail and what do they glance over? Traditionally, answering such questions has required complex setups with eye tracking labs.In this paper we investigate how fine-grained data about student movement within an online learning material can be used to improve said material in an informed fashion. Our data is collected by a JavaScript-component that tracks which elements of the online learning material are visible on the student's browser window as they study. The data is collected in situ, and no software needs to be installed on the student's computer. We further investigate how such data can be combined with data from a separate learning environment in which students work on course assignments and if the types of movements made by the students are correlated with student self-regulation metrics or course outcomes.Our results indicate that the use of rather simple and noninvasive tracking of students' movements in course materials allows material creators to quickly see major problem-areas in their materials and to highlight sections that students keep returning to. In addition, when the tracking data is combined with student course assignment data, inferring meaningful assignment-specific areas within the course material becomes possible. Finally, we determine that high-level statistics of user movements are not correlated with course outcomes or certain self-regulation related metrics.

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Analysing engagement in an online management programme and implications for course design

Cited by 11 publications

References 6 publications

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Improving M-Learners’ Performance Through Deep Learning Techniques by Leveraging Features Weights

Using and Collecting Fine-Grained Usage Data to Improve Online Learning Materials

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