Laila Alrajhi's Outputs (10)

Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums (2023)
Journal Article
Alrajhi, L., Alamri, A., Pereira, F. D., Cristea, A. I., & Oliveira, E. H. T. (2024). Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums. User Modeling and User-Adapted Interaction, 34(3), 797-852. https://doi.org/10.1007/s11257-023-09381-y

In MOOCs, identifying urgent comments on discussion forums is an ongoing challenge. Whilst urgent comments require immediate reactions from instructors, to improve interaction with their learners, and potentially reducing drop-out rates—the task is d... Read More about Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums.

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.

A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs (2022)
Book Chapter
Alrajhi, L., Pereira, F. D., Cristea, A. I., & Aljohani, T. (2022). A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (424-427). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_84

Deciding upon instructor intervention based on learners’ comments that need an urgent response in MOOC environments is a known challenge. The best solutions proposed used automatic machine learning (ML) models to predict the urgency. These are ‘black... Read More about A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs.

Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs (2022)
Book Chapter
Aljohani, T., Cristea, A. I., & Alrajhi, L. (2022). Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (396-399). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_78

Automatically identifying the learner gender, which serves as this paper’s focus, can provide valuable information to personalised learners’ experiences in MOOCs. However, extracting the gender from learner-generated data (discussion forum) is a chal... Read More about Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification 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.

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.

Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums (2021)
Presentation / Conference Contribution
Yu, J., Alrajhi, L., Harit, A., Sun, Z., Cristea, A. I., & Shi, L. (2021, June). Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums. Presented at Intelligent Tutoring Systems, Athens, Greece / Virtual

Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. However, due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support. Learners may post the... Read More about Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums.

Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs (2020)
Presentation / Conference Contribution
Alharbi, K., Alrajhi, L., Cristea, A. I., Bittencourt, I. I., Isotani, S., & James, A. (2020, December). Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs. Presented at 16th International Conference, ITS 2020

Welfare and economic development is directly dependent on the availability of highly skilled and educated individuals in society. In the UK, higher education is accessed by a large percentage of high school graduates (50% in 2017). Still, in Brazil,... Read More about Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs.

Classification of Instructor Intervention in MOOC Environment (2019)
Presentation / Conference Contribution
Alrajhi, L. (2019, December). Classification of Instructor Intervention in MOOC Environment. Paper presented at Early Career Researcher Conference 2019, Liverpool

Massive Open Online Courses (MOOCs) are one of the latest initiatives in open education. Their platforms contain many courses on different subject domain. For each such course, there are thousands of students and their comments to each part of the co... Read More about Classification of Instructor Intervention in MOOC Environment.