Skip to main content

Research Repository

Advanced Search

Outputs (48)

Exploring Time-Coded Comments on YouTube Music Videos of ‘Top 40’ Pop 2000–20 (2023)
Book Chapter
Bell, E. (2023). Exploring Time-Coded Comments on YouTube Music Videos of ‘Top 40’ Pop 2000–20. In H. Rogers, J. Freitas, & J. F. Porfírio (Eds.), YouTube and Music: Online Culture and Everyday Life (255-276). Bloomsbury. https://doi.org/10.5040/9781501387302.0024

As part of a larger project to understand the way that structural features of the design and implementation of radio technology influences its audiences – calling this the medium’s ‘physiognomy’ – Theodor Adorno opened the mailbags of the radio stati... Read More about Exploring Time-Coded Comments on YouTube Music Videos of ‘Top 40’ Pop 2000–20.

How easy is it to eXtend Reality? A Usability Study of Authoring Toolkits (2022)
Book Chapter
Thanyadit, S., Heintz, M., Law, E. L., & Mangina, E. (2022). How easy is it to eXtend Reality? A Usability Study of Authoring Toolkits. In T. Ahram, & R. Taiar (Eds.), Human Interaction & Emerging Technologies (IHIET 2022): Artificial Intelligence & Future Applications. AHFE International. https://doi.org/10.54941/ahfe1002707

Extended Reality (XR) has the potential to be a very successful teaching tool because it enables students to engage with a learning environment that incorporates both physical and virtual objects. Nevertheless, preparing an XR lesson requires technic... Read More about How easy is it to eXtend Reality? A Usability Study of Authoring Toolkits.

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.

Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data (2022)
Book Chapter
Drousiotis, E., Pentaliotis, P., Shi, L., & Cristea, A. I. (2022). Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data. In Artificial Intelligence in Education (256-268). Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_21

Along with the exponential increase of students enrolling in MOOCs [26] arises the problem of a high student dropout rate. Researchers worldwide are interested in predicting whether students will drop out of MOOCs to prevent it. This study explores a... Read More about Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data.

Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews (2022)
Book Chapter
Xiao, C., Shi, L., Cristea, A., Li, Z., & Pan, Z. (2022). Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews. In M. Rodrigo, N. Matsuda, A. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education (294-306). Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_24

Online course reviews have been an essential way in which course providers could get insights into students’ perceptions about the course quality, especially in the context of massive open online courses (MOOCs), where it is hard for both parties to... Read More about Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews.

An AI-Based Feedback Visualisation System for Speech Training (2022)
Book Chapter
Wynn, A. T., Wang, J., Umezawa, K., & Cristea, A. I. (2022). An AI-Based Feedback Visualisation System for Speech Training. 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 (510-514). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_104

This paper proposes providing automatic feedback to support public speech training. For the first time, speech feedback is provided on a visual dashboard including not only the transcription and pitch information, but also emotion information. A meth... Read More about An AI-Based Feedback Visualisation System for Speech Training.

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.

SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour (2022)
Book Chapter
Li, Z., Shi, L., Cristea, A., Zhou, Y., Xiao, C., & Pan, Z. (2022). SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour. In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (348-351). Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_67

Lacking behavioural data between students and an Intelligent Tutoring System (ITS) has been an obstacle for improving its personalisation capability. One feasible solution is to train “sim students”, who simulate real students’ behaviour in the ITS.... Read More about SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour.

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.

MOOCs Paid Certification Prediction Using Students Discussion Forums (2022)
Book Chapter
Alshehri, M., & Cristea, A. I. (2022). MOOCs Paid Certification Prediction Using Students Discussion Forums. 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 (542-545). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_111

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 MOOCs Paid Certification Prediction Using Students Discussion Forums.

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.

MEMORABLE: A Multi-playEr custoMisable seriOus Game fRAmework for cyBer-security LEarning (2022)
Book Chapter
Wang, J., Hodgson, R., & Cristea, A. I. (2022). MEMORABLE: A Multi-playEr custoMisable seriOus Game fRAmework for cyBer-security LEarning. In S. Crossley, & E. Popescu (Eds.), Intelligent Tutoring Systems (313-322). Springer Verlag. https://doi.org/10.1007/978-3-031-09680-8_29

In this paper, we propose an educational game framework allowing instructors to customise the game’s learning content in the context of cyber-security, with the aim of ensuring learners are engaged with educational games. This can further support the... Read More about MEMORABLE: A Multi-playEr custoMisable seriOus Game fRAmework for cyBer-security LEarning.

Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods (2022)
Book Chapter
Drousiotis, E., Shi, L., Spirakis, P. G., & Maskell, S. (2022). Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods. In L. Iliadis, C. Jayne, A. Tefas, & E. Pimenidis (Eds.), Engineering Applications of Neural Networks: 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings (90-102). Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_8

Decision Forests have attracted the academic community’s interest mainly due to their simplicity and transparency. This paper proposes two novel decision forest building techniques, called Maximal Information Coefficient Forest (MICF) and Pearson’s C... Read More about Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods.

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.

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.

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.

An Evaluation of a Meaningful Discovery Learning Support System for Supporting E-book User in Pair Learning (2021)
Book Chapter
Wang, J., & Ogata, H. (2021). An Evaluation of a Meaningful Discovery Learning Support System for Supporting E-book User in Pair Learning. In Intelligent Tutoring Systems (ITS 2021) (107-111). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_13

In this paper, an experiment was conducted to study the learning performance when learning new knowledge in groups with an e-book system and a meaningful discovery learning support environment. The participants studied target new knowledge with an e-... Read More about An Evaluation of a Meaningful Discovery Learning Support System for Supporting E-book User in Pair Learning.

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.

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.