A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data (2021)
Conference Proceeding
Sun, Z., Harit, A., Yu, J., Cristea, A., & Al Moubayed, N. (2021). A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data. . https://doi.org/10.1109/ijcnn52387.2021.9533981

This research focuses on semi-supervised classification tasks, specifically for graph-structured data under datascarce situations. It is known that the performance of conventional supervised graph convolutional models is mediocre at classification ta... Read More about A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data.

MOOCSent: a Sentiment Predictor for Massive Open Online Courses (2021)
Conference Proceeding
Alsheri, M. A., Alrajhi, L. M., Alamri, A., & Cristea, A. I. (2021). MOOCSent: a Sentiment Predictor for Massive Open Online Courses.

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.

Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model (2021)
Journal Article
Pereira, F. D., Fonseca, S. C., Oliveira, E. H., Cristea, A. I., Bellhauser, H., Rodrigues, L., …Carvalho, L. S. (2021). Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model. IEEE Access, 9, 117097-117119. https://doi.org/10.1109/access.2021.3105956

Predicting student performance as early as possible and analysing to which extent initial student behaviour could lead to failure or success is critical in introductory programming (CS1) courses, for allowing prompt intervention in a move towards all... Read More about Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model.

Forum-based Prediction of Certification in Massive Open Online Courses (2021)
Conference Proceeding
Alsheri, M. A., Alamri, A., Cristea, A. I., & Stewart, C. D. (2021). Forum-based Prediction of Certification in Massive Open Online Courses.

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.

Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations (2021)
Journal Article
Aljohani, T., & Cristea, A. I. (2021). Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations. Frontiers in Research Metrics and Analytics, 6, Article 673928. https://doi.org/10.3389/frma.2021.673928

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic charac... Read More about Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations.

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.

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.

Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification (2021)
Book Chapter
Aljohani, T., & Cristea, A. I. (2021). Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (136-147). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_17

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. These platforms also bring incredible diversity of learners in terms of their traits. A research... Read More about Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification.

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.

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.

Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums (2021)
Conference Proceeding
Yu, J., Alrajhi, L., Harit, A., Sun, Z., Cristea, A. I., & Shi, L. (2021). Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums. In A. I. Cristea, & C. Troussos (Eds.), Intelligent Tutoring Systems (78-90). https://doi.org/10.1007/978-3-030-80421-3_10

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.

Agent-based Simulation of the Classroom Environment to Gauge the Effect of Inattentive or Disruptive Students (2021)
Conference Proceeding
Alharbi, K., Cristea, A. I., Shi, L., Tymms, P., & Brown, C. (2021). Agent-based Simulation of the Classroom Environment to Gauge the Effect of Inattentive or Disruptive Students. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (211-223). https://doi.org/10.1007/978-3-030-80421-3_23

The classroom environment is a major contributor to the learning process in schools. Young students are affected by different details in their academic progress, be it their own characteristics, their teacher’s or their peers’. The combination of the... Read More about Agent-based Simulation of the Classroom Environment to Gauge the Effect of Inattentive or Disruptive Students.

Wide-Scale Automatic Analysis of 20 Years of ITS Research (2021)
Conference Proceeding
Hodgson, R., Cristea, A., Shi, L., & Graham, J. (2021). Wide-Scale Automatic Analysis of 20 Years of ITS Research. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (8-21). https://doi.org/10.1007/978-3-030-80421-3_2

The analysis of literature within a research domain can provide significant value during preliminary research. While literature reviews may provide an in-depth understanding of current studies within an area, they are limited by the number of studies... Read More about Wide-Scale Automatic Analysis of 20 Years of ITS Research.

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs (2021)
Conference Proceeding
Sun, Z., Harit, A., Yu, J., Cristea, A. I., & Shi, L. (2021). A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs. In A. Cristea, & C. Troussas (Eds.), . https://doi.org/10.1007/978-3-030-80421-3_4

Massive Open Online Course (MOOC) systems have become prevalent in recent years and draw more attention, a.o., due to the coronavirus pandemic’s impact. However, there is a well-known higher chance of dropout from MOOCs than from conventional off-lin... Read More about A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs.

Encouraging Teacher-sourcing of Social Recommendations Through Participatory Gamification Design (2021)
Book Chapter
Toda Yacobson, E., Cristea, A., & Alexandron, G. I. (2021). Encouraging Teacher-sourcing of Social Recommendations Through Participatory Gamification Design. In A. Cristea, & C. I. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (418-429). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_46

Teachers and learners who search for learning materials in open educational resources (OER) repositories greatly benefit from feedback and reviews left by peers who have activated these resources in their class. Such feedback can also fuel social-bas... Read More about Encouraging Teacher-sourcing of Social Recommendations Through Participatory Gamification Design.

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach (2021)
Journal Article
Almuqren, L., Alrayes, F. S., & Cristea, A. I. (2021). An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach. Future Internet, 13(7), Article 175. https://doi.org/10.3390/fi13070175

With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing... Read More about An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach.

A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns (2021)
Conference Proceeding
Li, Z., Shi, L., Cristea, A. I., & Zhou, Y. (2021). A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns. . https://doi.org/10.1145/3461778.3462135

Recently, methods enabling humans and Artificial Intelligent (AI) agents to collaborate towards improving the efficiency of Reinforcement Learning - also called Collaborative Reinforcement Learning (CRL) - have been receiving increasing attention. In... Read More about A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns.

Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours (2021)
Conference Proceeding
Alharbi, K., Cristea, A. I., Shi, L., Tymms, P., & Brown, C. (2021). Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours. In I. Roll, M. Danielle, S. Sergey, L. Rose, & D. Vania (Eds.), Artificial Intelligence in Education Lecture Notes in Computer Science (48-53). https://doi.org/10.1007/978-3-030-78270-2_8

Schoolchildren's academic progress is known to be affected by the classroom environment. It is important for teachers and administrators to under-stand their pupils' status and how various factors in the classroom may affect them, as it can help them... Read More about Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours.

Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study (2021)
Book Chapter
Drousiotis, E., Pentaliotis, P., Shi, L., & Cristea, A. I. (2021). Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Artificial Intelligence in Education (139-144). Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_25

This study aims to explore and improve ways of handling a continuous variable dataset, in order to predict student dropout in MOOCs, by implementing various models, including the ones most successful across various domains, such as recurrent neural n... Read More about Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study.

AraCust: a Saudi Telecom Tweets corpus for sentiment analysis (2021)
Journal Article
Almuqren, L., & Cristea, A. (2021). AraCust: a Saudi Telecom Tweets corpus for sentiment analysis. PeerJ Computer Science, 7, Article e510. https://doi.org/10.7717/peerj-cs.510

Comparing Arabic to other languages, Arabic lacks large corpora for Natural Language Processing (Assiri, Emam & Al-Dossari, 2018; Gamal et al., 2019). A number of scholars depended on translation from one language to another to construct their corpus... Read More about AraCust: a Saudi Telecom Tweets corpus for sentiment analysis.