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)
Outputs (387)
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_13In 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_25This 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.
Integrating Multimodal Learning Analytics and Inclusive Learning Support Systems for People of All Ages
Book Chapter
Tamura, K., Lu, M., Konomi, S., Hatano, K., Inaba, M., Oi, M., …Yamada, Y. Integrating Multimodal Learning Analytics and Inclusive Learning Support Systems for People of All Ages. In Cross-Cultural Design. Culture and Society. https://doi.org/10.1007/978-3-030-22580-3_35
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_15Although 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_18Recently, 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_17Massive 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. IETThe 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. IETThis 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
The Learning Behaviors Analysis in a Language Learning Support System – A Pilot Study (2020)
Book Chapter
Wang, J., Chao, C., & Liu, L. (2020). The Learning Behaviors Analysis in a Language Learning Support System – A Pilot Study. In HCI International 2020 - Posters. https://doi.org/10.1007/978-3-030-50729-9_50
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_11In 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.
5G: Disruption in Media and Entertainment (2019)
Book Chapter
Katsigiannis, S., Ahmad, W., & Ramzan, N. (2019). 5G: Disruption in Media and Entertainment. In Enabling 5G Communication Systems to Support Vertical Industries (179-190). Wiley-IEEE Press. https://doi.org/10.1002/9781119515579.ch8
Active Learning of Hyperparameters: An Expected Cross Entropy Criterion for Active Model Selection (2014)
Report
Kulick, J., Lieck, R., & Toussaint, M. (2014). Active Learning of Hyperparameters: An Expected Cross Entropy Criterion for Active Model Selection. [No known commissioning body]
A Real-Time Video Encoding Scheme Based on the Contourlet Transform (2013)
Book Chapter
Katsigiannis, S., Papaioannou, G., & Maroulis, D. (2013). A Real-Time Video Encoding Scheme Based on the Contourlet Transform. In Design and Architectures for Digital Signal Processing. https://doi.org/10.5772/51735
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_7This 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.
Contextual Gamification of Social Interaction – Towards Increasing Motivation in Social E-learning
Book Chapter
Shi, L., Cristea, A., Hadzidedic, S., & Dervishalidovic, N. Contextual Gamification of Social Interaction – Towards Increasing Motivation in Social E-learning. In Advances in Web-Based Learning – ICWL 2014. https://doi.org/10.1007/978-3-319-09635-3_12
Students as Customers: Participatory Design for Adaptive Web 3.0 (2014)
Book Chapter
Shi, L., Cristea, A. I., & Stewart, C. (2015). Students as Customers: Participatory Design for Adaptive Web 3.0. In P. Isaías, P. Kommers, & T. Issa (Eds.), The Evolution of the Internet in the Business Sector: Web 1.0 to Web 3.0 (306-331). IGI Global. https://doi.org/10.4018/978-1-4666-7262-8.ch014
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_20While 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_10Representation 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.