Studying engagement and performance with learning technology in an African classroom

Mutahi, Juliet; Kinai, Andrew; Bore, Nelson; Diriye, Abdigani; Weldemariam, Komminist

doi:10.1145/3027385.3027395

Cited by 21 publications

(14 citation statements)

References 6 publications

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“…The study used a cross-lagged modeling technique and found that course design and student perception variables greatly affected student engagement in the course. Mutahi et al [ 4 ] conducted a study to investigate the relationship between a student's final score and the student's engagement in material using a statistical technique and found that students who had high levels of engagement for quizzes and materials earned higher grades on the final exam. Aguiar et al [ 42 ] developed an early-warning system using engagement-related input features and found that these variables are highly predictive of student-retention problems.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Atherton et al [ 44 ] showed that students who access web-based system study materials daily and undergo regular assessments achieve high exam scores. Other research results show that high-engagement students tend to earn good grades on course quizzes and assessments [ 4 ]. Rodgers [ 56 ] found that student interactions with an e-learning system were significantly correlated with course outcomes.…”

Section: Related Workmentioning

confidence: 99%

“…In the traditional approach to education, teachers take various steps to appraise students' levels of performance, motivation, and engagement [ 4 ], such as conducting exams, checking student attendance, and monitoring studying via security cameras. However, in web-based platforms, there are no face-to-face meetings, and it is difficult to determine student engagement levels in online activities such as participating in discussion forums or watching videos.…”

Section: Introductionmentioning

confidence: 99%

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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

263

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%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

263

167

View full text Add to dashboard Cite

show abstract

“…In the state-of-art method to education, trainers take various measures to assess students' levels of performance, motivation, and engagement [4], such as checking student attendance, conducting exams, and monitoring studying via CCTV cameras. However, in online based learning system there are no face-to-face meetings, and it is challenging to determine student engagement levels in online activities such as watching videos or participating in discussion forums.…”

Section: Introductionmentioning

confidence: 99%

Student risk identification learning model using machine learning approach

Susheelamma¹,

Ravikumar²

2019

IJECE

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<p class="Abstract">Several challenges are associated with online based learning systems, the most important of which is the lack of student motivation in various course materials and for various course activities. Further, it is important to identify student who are at risk of failing to complete the course on time. The existing models applied machine learning approach for solving it. However, these models are not efficient as they are trained using legacy data and also failed to address imbalanced data issues for both training and testing the classification approach. Further, they are not efficient for classifying new courses. For overcoming these research challenges, this work presented a novel design by training the learning model for identifying risk using current courses. Further, we present an XGBoost classification algorithm that can classify risk for new courses. Experiments are conducted to evaluate performance of proposed model. The outcome shows the proposed model attain significant performance over stat-of-art model in terms of ROC, F-measure, Precision and Recall.</p>

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“…The study found that learners' course interest, orientation, course design, and learners' perception features greatly influence learners' performance and engagement in the course. Mutahi et al used ML and statistical techniques to determine the relationship between learners' engagement and learners' final performance score [38]. They found that learners' having high levels of engagement in reading learning content, taking quizzes, submitting assignments earned higher grades in final examinations.…”

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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Studying engagement and performance with learning technology in an African classroom

Cited by 21 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

Student risk identification learning model using machine learning approach

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

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