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Outputs (387)

The Analysis of Crowd Dynamics: From Observations to Modelling (2009)
Book Chapter
Zhan, B., Remagnino, P., Monekosso, D., & Velastin, S. (2009). The Analysis of Crowd Dynamics: From Observations to Modelling. In C. Mumford, & L. Jain (Eds.), COMPUTATIONAL INTELLIGENCE: COLLABORATION, FUSION AND EMERGENCE (441-472)

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.

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.

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.

A machine learning driven solution to the problem of perceptual video quality metrics (2020)
Book Chapter
Katsigiannis, S., Rabah, H., & Ramzan, N. (2020). A machine learning driven solution to the problem of perceptual video quality metrics. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

The advent of high-speed internet connections, advanced video coding algorithms, and consumer-grade computers with high computational capabilities has led videostreaming-over-the-internet to make up the majority of network traffic. This effect has le... Read More about A machine learning driven solution to the problem of perceptual video quality metrics.

Machine learning-based affect detection within the context of human-horse interaction (2020)
Book Chapter
Althobaiti, T., Katsigiannis, S., West, D., Rabah, H., & Ramzan, N. (2020). Machine learning-based affect detection within the context of human-horse interaction. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

This chapter focuses on the use of machine learning techniques within the field of affective computing, and more specifically for the task of emotion recognition within the context of human-horse interaction. Affective computing focuses on the detect... Read More about Machine learning-based affect detection within the context of human-horse interaction.

Information Retrieval from Electronic Health Records (2020)
Book Chapter
Al-Qahtani, M., Katsigiannis, S., & Ramzan, N. (2020). Information Retrieval from Electronic Health Records. In M. A. Imran, R. Ghannam, & Q. H. Abbasi (Eds.), Engineering and technology for healthcare (117-128). Wiley-IEEE Press

Ontology Technique and Meaningful Learning Support Environments (2019)
Book Chapter
Wang, J. (2019). Ontology Technique and Meaningful Learning Support Environments. In D. Sampson, J. M. Spector, D. Ifenthaler, P. Isaías, & S. Sergis (Eds.), Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment (215-229). Springer Verlag. https://doi.org/10.1007/978-3-030-15130-0_11

In this chapter, we present two ontology-driven learning support systems, which intend to provide meaningful learning environment: a customizable language learning support system (CLLSS) and a visualization learning support system for e-book users (V... Read More about Ontology Technique and Meaningful Learning Support Environments.

Exploring Navigation Styles in a FutureLearn MOOC (2020)
Book Chapter
Shi, L., Cristea, A. I., Toda, A. M., & Oliveira, W. (2020). Exploring Navigation Styles in a FutureLearn MOOC. In V. Kumar, & C. Troussas (Eds.), Intelligent Tutoring Systems (45-55). Springer Verlag. https://doi.org/10.1007/978-3-030-49663-0_7

This paper presents for the first time a detailed analysis of fine-grained navigation style identification in MOOCs backed by a large number of active learners. The result shows 1) whilst the sequential style is clearly in evidence, the global style... Read More about Exploring Navigation Styles in a FutureLearn MOOC.

Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities (2019)
Book Chapter
Alamri, A., Alshehri, M., Cristea, A. I., Pereira, F. D., Oliveira, E., Shi, L., & Stewart, C. (2019). Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities. In A. Coy, Y. Hayashi, & M. Chang (Eds.), Intelligent tutoring systems. ITS 2019 (163-173). Springer Verlag. https://doi.org/10.1007/978-3-030-22244-4_20

While Massive Open Online Course (MOOCs) platforms provide knowledge in a new and unique way, the very high number of dropouts is a significant drawback. Several features are considered to contribute towards learner attrition or lack of interest, whi... Read More about Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities.

What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval (2017)
Book Chapter
Zhou, Y., Demidova, E., & Cristea, A. (2017). What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval. In N. Nguyen, R. Kowalczyk, A. Pinto, & J. Cardoso (Eds.), Transactions on Computational Collective Intelligence XXVI (2010-231). Springer Verlag. https://doi.org/10.1007/978-3-319-59268-8_10

Representation of influential entities, such as celebrities and multinational corporations on the web can vary across languages, re- flecting language-specific entity aspects, as well as divergent views on these entities in different communities. An... Read More about What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval.