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Outputs (10)

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs (2021)
Presentation / Conference Contribution
Sun, Z., Harit, A., Yu, J., Cristea, A. I., & Shi, L. (2021, June). A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs. Presented at Intelligent Tutoring Systems, Athens, Greece / Virtual

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.

Wide-Scale Automatic Analysis of 20 Years of ITS Research (2021)
Presentation / Conference Contribution
Hodgson, R., Cristea, A., Shi, L., & Graham, J. (2021, June). Wide-Scale Automatic Analysis of 20 Years of ITS Research. Presented at Intelligent Tutoring Systems, Athens, Greece / Virtual

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.

Agent-based Simulation of the Classroom Environment to Gauge the Effect of Inattentive or Disruptive Students (2021)
Presentation / Conference Contribution
Alharbi, K., Cristea, A. I., Shi, L., Tymms, P., & Brown, C. (2021, June). Agent-based Simulation of the Classroom Environment to Gauge the Effect of Inattentive or Disruptive Students. Presented at Intelligent Tutoring Systems, Athens, Greece / Virtual

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.

Early Predictor for Student Success Based on Behavioural and Demographical Indicators (2021)
Presentation / Conference Contribution
Drousiotis, E., Shi, L., Maskell, S., Cristea, A. I., & Troussas, C. (2021, December). Early Predictor for Student Success Based on Behavioural and Demographical Indicators. Presented at International Conference on Intelligent Tutoring Systems 2021

As the largest distance learning university in the UK, the Open University has more than 250,000 students enrolled, making it also the largest academic institute in the UK. However, many students end up failing or withdrawing from online courses, whi... Read More about Early Predictor for Student Success Based on Behavioural and Demographical Indicators.

Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums (2021)
Presentation / Conference Contribution
Yu, J., Alrajhi, L., Harit, A., Sun, Z., Cristea, A. I., & Shi, L. (2021, June). Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums. Presented at Intelligent Tutoring Systems, Athens, Greece / Virtual

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.

A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns (2021)
Presentation / Conference Contribution
Li, Z., Shi, L., Cristea, A. I., & Zhou, Y. (2023, June). A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns. Presented at ACM Designing Interactive Systems (DIS), Virtual

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.

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.

Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours (2021)
Presentation / Conference Contribution
Alharbi, K., Cristea, A. I., Shi, L., Tymms, P., & Brown, C. (2021, June). Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours. Presented at Artificial Intelligence in Education, Utrecht

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.

Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs (2021)
Journal Article
Xing, B., Si, H., Chen, J., Ye, M., & Shi, L. (2021). Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs. CCF Transactions on Pervasive Computing and Interaction, 3, 147-169. https://doi.org/10.1007/s42486-021-00064-4

Visual aesthetics is vital in determining the usability of the graphical user interface (GUI). It can strengthen the competitiveness of interactive online applications. Human aesthetic preferences for GUI are implicit and linked to various aspects of... Read More about Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs.