دكتوراه
العلوم والتقنية
Durham University
مجال التميز | تميز دراسي وبحثي |
البحوث المنشورة |
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البحث (1): | |
عنوان البحث: | Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs |
رابط إلى البحث: | https://link.springer.com/article/10.1007/s40593-021-00246-2 |
تاريخ النشر: | 23/03/2021 |
موجز عن البحث: | 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. |
المؤتمرات العلمية |
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المؤتمر (1): | |
عنوان المؤتمر: | International Conference on Intelligent Tutoring System |
تاريخ الإنعقاد: | 03/06/2020 |
مكان الإنعقاد: | Greece |
طبيعة المشاركة: | Paper presentation |
عنوان المشاركة: | Is MOOC Learning Different for Dropouts? A Visually-Driven, Multi-granularity Explanatory ML Approach |
ملخص المشاركة: | Millions of people have enrolled and enrol (especially in the Covid-19 pandemic world) in MOOCs. However, the retention rate of learners is notoriously low. The majority of the research work on this issue focuses on predicting the dropout rate, but very few use explainable learning patterns as part of this analysis. However, visual representation of learning patterns could provide deeper insights into learners’ behaviour across different courses, whilst numerical analyses can – and arguably, should – be used to confirm the latter. Thus, this paper proposes and compares different granularity visualisations for learning patterns (based on clickstream data) for both course completers and non-completers. In the large-scale MOOCs we analysed, across various domains, our fine-grained, fish-eye visualisation approach showed that non-completers are more likely to jump forward in their learning sessions, often on a ‘catch-up’ path, whilst completers exhibit linear behaviour. For coarser, bird-eye granularity visualisation, we observed learners’ transition between types of learning activity, obtaining typed transition graphs. The results, backed up by statistical significance analysis and machine learning, provide insights for course instructors to maintain engagement of learners by adapting the course design to not just ‘dry’ predicted values, but explainable, visually viable paths extracted. |
الرابط: | https://link.springer.com/chapter/10.1007/978-3-030-49663-0_42 |
المؤتمر (2): | |
عنوان المؤتمر: | International Conference on Intelligent Tutoring Systems |
تاريخ الإنعقاد: | 30/05/2019 |
مكان الإنعقاد: | Jamaica |
طبيعة المشاركة: | Paper presentation |
عنوان المشاركة: | Predicting MOOCs dropout using only two easily obtainable features from the first week’s activities |
ملخص المشاركة: | While Massive Open Online Course (MOOCs) platforms provide knowledge in a new and unique way, the very high number of dropouts is a significant drawback. Several features are considered to contribute towards learner attrition or lack of interest, which may lead to disengagement or total dropout. The jury is still out on which factors are the most appropriate predictors. However, the literature agrees that early prediction is vital to allow for a timely intervention. Whilst feature-rich predictors may have the best chance for high accuracy, they may be unwieldy. This study aims to predict learner dropout early-on, from the first week, by comparing several machine-learning approaches, including Random Forest, Adaptive Boost, XGBoost and GradientBoost Classifiers. The results show promising accuracies (82%–94%) using as little as 2 features. We show that the accuracies obtained outperform state of the art approaches, even when the latter deploy several features. |
الرابط: | https://link.springer.com/chapter/10.1007/978-3-030-22244-4_20 |