Outputs (21)

Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs (2023)
Presentation / Conference Contribution
Alrajhi, L., Pereira, F. D., Cristea, A. I., & Alamri, A. (2023, September). Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs. Paper presented at HT '23: 34th ACM Conference on Hypertext and Social Media, Rome Italy

Determining when instructor intervention is needed, based on learners’ comments and their urgency in massive open online course (MOOC) environments, is a known challenge. To solve this challenge, prior art used autonomous machine learning (ML) models... Read More about Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs.

The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation (2023)
Journal Article
Cristea, A. I., Alamri, A., Alshehri, M., Dwan Pereira, F., Toda, A. M., Harada T. de Oliveira, E., & Stewart, C. (2024). The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation. User Modeling and User-Adapted Interaction, 34(2), 323-374. https://doi.org/10.1007/s11257-023-09374-x

Massive Online Open Course (MOOC) platforms are considered a distinctive way to deliver a modern educational experience, open to a worldwide public. However, student engagement in MOOCs is a less explored area, although it is known that MOOCs suffer... Read More about The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation.

Adopting Automatic Machine Learning for Temporal Prediction of Paid Certification in MOOCs (2022)
Book Chapter
Alshehri, M., Alamri, A., & Cristea, A. I. (2022). Adopting Automatic Machine Learning for Temporal Prediction of Paid Certification in MOOCs. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education (717-723). Springer Verlag. https://doi.org/10.1007/978-3-031-11644-5_73

Massive Open Online Course (MOOC) platforms have been growing exponentially, offering worldwide low-cost educational content. Recent literature on MOOC learner analytics has been carried out around predicting either students’ dropout, academic perfor... Read More about Adopting Automatic Machine Learning for Temporal Prediction of Paid Certification in MOOCs.

Intervention Prediction in MOOCs Based on Learners’ Comments: A Temporal Multi-input Approach Using Deep Learning and Transformer Models (2022)
Book Chapter
Alrajhi, L., Alamri, A., & Cristea, A. I. (2022). Intervention Prediction in MOOCs Based on Learners’ Comments: A Temporal Multi-input Approach Using Deep Learning and Transformer Models. In S. Crossley, & E. Popescu (Eds.), Intelligent Tutoring Systems (227-237). Springer Verlag. https://doi.org/10.1007/978-3-031-09680-8_22

High learner dropout rates in MOOC-based education contexts have encouraged researchers to explore and propose different intervention models. In discussion forums, intervention is critical, not only to identify comments that require replies but also... Read More about Intervention Prediction in MOOCs Based on Learners’ Comments: A Temporal Multi-input Approach Using Deep Learning and Transformer Models.

MOOCSent: a Sentiment Predictor for Massive Open Online Courses (2021)
Presentation / Conference Contribution
Alsheri, M. A., Alrajhi, L. M., Alamri, A., & Cristea, A. I. (2021, September). MOOCSent: a Sentiment Predictor for Massive Open Online Courses. Presented at 29th International Conference on Information systems and Development (ISD2021), Valencia, Spain

One key type of Massive Open Online Course (MOOC) data is the learners’ social interaction (forum). While several studies have analysed MOOC forums to predict learning outcomes, analysing learners’ sentiments in education and, specifically, in MOOCs,... Read More about MOOCSent: a Sentiment Predictor for Massive Open Online Courses.

Forum-based Prediction of Certification in Massive Open Online Courses (2021)
Presentation / Conference Contribution
Alsheri, M. A., Alamri, A., Cristea, A. I., & Stewart, C. D. (2021, September). Forum-based Prediction of Certification in Massive Open Online Courses. Presented at 29th International Conference on Information systems and Development (ISD2021), Valencia, Spain

Massive Open Online Courses (MOOCs) have been suffering a very level of low course certification (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate), although MOOC platforms have been offer... Read More about Forum-based Prediction of Certification in Massive Open Online Courses.

Predicting Certification in MOOCs based on Students’ Weekly Activities (2021)
Book Chapter
Alshehri, M., Alamri, A., & Cristea, A. I. (2021). Predicting Certification in MOOCs based on Students’ Weekly Activities. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (173-185). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_20

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 studen... Read More about Predicting Certification in MOOCs based on Students’ Weekly Activities.

Urgency Analysis of Learners’ Comments: An Automated Intervention Priority Model for MOOC (2021)
Book Chapter
Alrajhi, L., Alamri, A., Pereira, F. D., & Cristea, A. I. (2021). Urgency Analysis of Learners’ Comments: An Automated Intervention Priority Model for MOOC. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (148-160). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_18

Recently, the growing number of learners in Massive Open Online Course (MOOC) environments generate a vast amount of online comments via social interactions, general discussions, expressing feelings or asking for help. Concomitantly, learner dropout,... Read More about Urgency Analysis of Learners’ Comments: An Automated Intervention Priority Model for MOOC.

A Recommender System Based on Effort: Towards Minimising Negative Affects and Maximising Achievement in CS1 Learning (2021)
Book Chapter
Pereira, F. D., Junior, H. B., Rodriquez, L., Toda, A., Oliveira, E. H., Cristea, A. I., …Isotani, S. (2021). A Recommender System Based on Effort: Towards Minimising Negative Affects and Maximising Achievement in CS1 Learning. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (466-480). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_51

Programming online judges (POJs) are autograders that have been increasingly used in introductory programming courses (also known as CS1) since these systems provide instantaneous and accurate feedback for learners’ codes solutions and reduce instruc... Read More about A Recommender System Based on Effort: Towards Minimising Negative Affects and Maximising Achievement in CS1 Learning.

MOOC next week dropout prediction: weekly assessing time and learning patterns (2021)
Book Chapter
Alamri, A., Sun, Z., Cristea, A. I., Steward, C., & Pereira, F. D. (2021). MOOC next week dropout prediction: weekly assessing time and learning patterns. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (119-130). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_15

Although Massive Open Online Course (MOOC) systems have become more prevalent in recent years, associated student attrition rates are still a major drawback. In the past decade, many researchers have sought to explore the reasons behind learner attri... Read More about MOOC next week dropout prediction: weekly assessing time and learning patterns.