On the Need for Fine-Grained Analysis of Gender Versus Commenting Behaviour in MOOCs

Alshehri, Mohammad; Foss, Jonathan G. K.; Cristea, Alexandra I.; Kayama, Mizue; Shi, Lei; Alamri, Ahmed; Tsakalidis, Adam

doi:10.1145/3234825.3234833

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…When a learner joins FutureLearn for the first time, they are directly prompted to complete an optional survey about their characteristics. Existing learners are also prompted to complete this data if missing (Alshehri et al 2018). All questions on the survey are optional and they aim to extract certain information about a learner's gender, age group, education level, country, employment status and employment area.…”

Section: Methodology Data Collectionmentioning

confidence: 99%

“…All questions on the survey are optional and they aim to extract certain information about a learner's gender, age group, education level, country, employment status and employment area. In parallel, the system generates logs to correlate unique IDs and time stamps to learners, recording learner activities, such as weekly-based steps visited, completed, comments added, or question attempts (Alshehri et al 2018). The current study is analysing data extracted from a total of 23 runs spread over 5 MOOC courses, on 4 distinct topic areas, all delivered through FutureLearn, by the University of Warwick, these topic areas are: These courses were delivered repeatedly in consecutive years (2013-2017), thus we have data on several 'runs' for each course.…”

Section: Methodology Data Collectionmentioning

confidence: 99%

“…In addition, Chua et al (Chua et al 2017) and Tubman et al (Tubman et al 2016) analysed learner commenting behaviours, having explored patterns of discussion that occur in MOOCs. More recently, (Alshehri et al 2018;Cristea et al 2018b) examined how basic learner characteristics, such as gender, can influence learning behaviours, such as the patterns of making comments on different learning steps. More recently, (Alamri et al 2019;Cristea et al 2018c) addressed early dropout issue using the student's first registration date on the course and two learner activity-based features: time spent by learner and number of access from the first week data only.…”

Section: Activities-based Predictionmentioning

confidence: 99%

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Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs

Alshehri

Alamri

Cristea

et al. 2021

Int J Artif Intell Educ

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Since their ‘official’ emergence in 2012 (Gardner and Brooks 2018), massive open online courses (MOOCs) have been growing rapidly. They offer low-cost education for both students and content providers; however, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). The most recent literature on MOOCs focuses on identifying factors that contribute to student success, completion level and engagement. One of the MOOC platforms’ ultimate targets is to become self-sustaining, enabling partners to create revenues and offset operating costs. Nevertheless, analysing learners’ purchasing behaviour on MOOCs remains limited. Thus, this study aims to predict students purchasing behaviour and therefore a MOOCs revenue, based on the rich array of activity clickstream and demographic data from learners. Specifically, we compare how several machine learning algorithms, namely RandomForest, GradientBoosting, AdaBoost and XGBoost can predict course purchasability using a large-scale data collection of 23 runs spread over 5 courses delivered by The University of Warwick between 2013 and 2017 via FutureLearn. We further identify the common representative predictive attributes that influence a learner’s certificate purchasing decisions. Our proposed model achieved promising accuracies, between 0.82 and 0.91, using only the time spent on each step. We further reached higher accuracy of 0.83 to 0.95, adding learner demographics (e.g. gender, age group, level of education, and country) which showed a considerable impact on the model’s performance. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenue.

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Section: Methodology Data Collectionmentioning

confidence: 99%

Section: Methodology Data Collectionmentioning

confidence: 99%

Section: Activities-based Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs

Alshehri

Alamri

Cristea

et al. 2021

Int J Artif Intell Educ

Self Cite

View full text Add to dashboard Cite

show abstract

“…When a learner joins FutureLearn for a given course, the system generates logs to correlate unique IDs and time stamps to learners, recording learner activities, such as weekly-based steps visited, completed, comments added, or question attempted [23].…”

Section: Data Collectionmentioning

confidence: 99%

Predicting Certification in MOOCs Based on Students’ Weekly Activities

Alshehri

Alamri

Cristea

2021

Intelligent Tutoring Systems

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Massive Open Online Courses (MOOCs) have been growing rapidly, offering low-cost knowledge for both learners and content providers. However, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). This can impact seriously the business model of MOOCs. Nevertheless, MOOC research on learners' purchasing behaviour on MOOCs remains limited. Thus, the umbrella question that this work tackles is if learner's data can predict their purchasing decision (certification). Our fine-grained analysis attempts to uncover the latent correlation between learner activities and their decision to purchase. We used a relatively large dataset of 5 courses of 23 runs obtained from the less studied MOOC platform of FutureLearn to: (1) statistically compare the activities of non-paying learners with course purchasers, (2) predict course certification using different classifiers, optimising for this naturally strongly imbalanced dataset. Our results show that learner activities are good predictors of course purchasibility; still, the main challenge was that of early prediction. Using only student number of step accesses, attempts, correct and wrong answers, our model achieve promising accuracies, ranging between 0.81 and 0.95 across the five courses. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenue.

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“…Other studies, e.g. [2,38], used demographical indicators such as gender and age to predict student engagement; [37,40] focused on the use of learning platform's features in order to analyze learning behavior patterns. Different from these previous studies, in this study, we selected both students' demographical data and their interaction (activity logs) data for the clustering process.…”

Section: Subgroup Clusteringmentioning

confidence: 99%

Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes

Shi

Yang

Toda

2020

Lecture Notes in Information Systems and Organisation

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This chapter demonstrates a temporal analysis in Massive Open Online Courses (MOOCs), towards identifying at-risk students through analyzing their demographical changes. At-risk students are those who tend to drop out from the MOOCs. Previous studies have shown that how students interact in MOOCs could be used to identify at-risk students. Some studies considered student diversity by looking into subgroup behavior. However, most of them lack consideration of students' demographical changes. Towards bridging the gap, this study clusters students based on both their interaction with the MOOCs (activity logs) and their characteristics and explores their demographical changes along the MOOCs progress. The result shows students' demographical characteristics (membership of subgroups) changed significantly in the first half of the course and stabilized in the second half. Our findings provide insight into how students may be engaged in MOOCs and suggest the improvement of identifying at-risk students based on the temporal data.

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On the Need for Fine-Grained Analysis of Gender Versus Commenting Behaviour in MOOCs

Cited by 11 publications

References 13 publications

Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs

Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs

Predicting Certification in MOOCs Based on Students’ Weekly Activities

Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes

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