Outputs (127)

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.

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.

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.

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.

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.

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.

A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics (2022)
Presentation / Conference Contribution
Hodgson, R., Wang, J. W., Cristea, A., Matsuzaki, F., & Kubota, H. (2022, July). A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics. Presented at 15th International Conference on Educational Data Mining, Durham, England

In the academic process, comprehension and analysis of liter- ature is essential, however, time-consuming. Reviewers may encounter difficulties in identifying relevant literature, given the considerable volume of available texts. It is arduous not on... Read More about A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics.

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.

Gamification suffers from the novelty effect but benefits from the familiarization effect: Findings from a longitudinal study (2022)
Journal Article
Rodrigues, L., Pereira, F. D., Toda, A. M., Palomino, P. T., Pessoa, M., Carvalho, L. S. G., Fernandes, D., Oliveira, E. H., Cristea, A. I., & Isotani, S. (2022). Gamification suffers from the novelty effect but benefits from the familiarization effect: Findings from a longitudinal study. International Journal of Educational Technology in Higher Education, 19(1), https://doi.org/10.1186/s41239-021-00314-6

There are many claims that gamification (i.e., using game elements outside games) impact decreases over time (i.e., the novelty effect). Most studies analyzing this effect focused on extrinsic game elements, while fictional and collaborative competit... Read More about Gamification suffers from the novelty effect but benefits from the familiarization effect: Findings from a longitudinal study.