Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation (2023)
Journal Article
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2023). Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation. AI open, 4, 19-32. https://doi.org/10.1016/j.aiopen.2023.05.001

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence... Read More about Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation.

PICA-PICA: Exploring a Customisable Smart STEAM Educational Approach via a Smooth Combination of Programming, Engineering and Art (2023)
Conference Proceeding
Nagai, T., Klem, S., Kayama, M., Asuke, T., Meccawy, M., Wang, J., …Shi, L. (2023). PICA-PICA: Exploring a Customisable Smart STEAM Educational Approach via a Smooth Combination of Programming, Engineering and Art. . https://doi.org/10.1109/educon54358.2023.10125184

The STEAM approach in education has been gaining increasing popularity over the last decade. This is due to its potential in enhancing students' learning, when teaching arts and scientific disciplines together. This paper introduces the PICA-PICA con... Read More about PICA-PICA: Exploring a Customisable Smart STEAM Educational Approach via a Smooth Combination of Programming, Engineering and Art.

Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention (2022)
Conference Proceeding
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Al Moubayed, N., & Shi, L. (2022). Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention. . https://doi.org/10.1109/bigdata55660.2022.10020791

Medical visual question answering (Med-VQA) is to answer medical questions based on clinical images provided. This field is still in its infancy due to the complexity of the trio formed of questions, multimodal features and expert knowledge. In this... Read More about Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention.

Efficient Uncertainty Quantification for Multilabel Text Classification (2022)
Conference Proceeding
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022). Efficient Uncertainty Quantification for Multilabel Text Classification. . https://doi.org/10.1109/ijcnn55064.2022.9892871

Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty qua... Read More about Efficient Uncertainty Quantification for Multilabel Text Classification.

Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification (2022)
Conference Proceeding
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. . https://doi.org/10.1109/ijcnn55064.2022.9892257

Graph neural networks (GNNs) have attracted extensive interest in text classification tasks due to their expected superior performance in representation learning. However, most existing studies adopted the same semi-supervised learning setting as the... Read More about Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification.

INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations (2022)
Conference Proceeding
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022). INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations. . https://doi.org/10.1109/ijcnn55064.2022.9892336

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decisionmaking, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a s... Read More about INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations.

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.

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.

Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums (2021)
Conference Proceeding
Yu, J., Alrajhi, L., Harit, A., Sun, Z., Cristea, A. I., & Shi, L. (2021). Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums. In A. I. Cristea, & C. Troussos (Eds.), Intelligent Tutoring Systems (78-90). https://doi.org/10.1007/978-3-030-80421-3_10

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.

Agent-based Simulation of the Classroom Environment to Gauge the Effect of Inattentive or Disruptive Students (2021)
Conference Proceeding
Alharbi, K., Cristea, A. I., Shi, L., Tymms, P., & Brown, C. (2021). Agent-based Simulation of the Classroom Environment to Gauge the Effect of Inattentive or Disruptive Students. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (211-223). https://doi.org/10.1007/978-3-030-80421-3_23

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.

Wide-Scale Automatic Analysis of 20 Years of ITS Research (2021)
Conference Proceeding
Hodgson, R., Cristea, A., Shi, L., & Graham, J. (2021). Wide-Scale Automatic Analysis of 20 Years of ITS Research. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (8-21). https://doi.org/10.1007/978-3-030-80421-3_2

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.

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs (2021)
Conference Proceeding
Sun, Z., Harit, A., Yu, J., Cristea, A. I., & Shi, L. (2021). A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs. In A. Cristea, & C. Troussas (Eds.), . https://doi.org/10.1007/978-3-030-80421-3_4

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.

A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns (2021)
Conference Proceeding
Li, Z., Shi, L., Cristea, A. I., & Zhou, Y. (2021). A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns. . https://doi.org/10.1145/3461778.3462135

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.

Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours (2021)
Conference Proceeding
Alharbi, K., Cristea, A. I., Shi, L., Tymms, P., & Brown, C. (2021). Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours. In I. Roll, M. Danielle, S. Sergey, L. Rose, & D. Vania (Eds.), Artificial Intelligence in Education Lecture Notes in Computer Science (48-53). https://doi.org/10.1007/978-3-030-78270-2_8

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.

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.

Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course (2020)
Conference Proceeding
Yu, J., Aduragba, O. T., Sun, Z., Black, S., Stewart, C., Shi, L., & Cristea, A. (2020). Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course. In International Conference on Computer Science & Education (ICCSE) (182-187). https://doi.org/10.1109/iccse49874.2020.9201631

Social media is ubiquitous, a continuous part of our daily lives; it offers new ways of communication. This is especially crucial in education, where various online systems make use of (perceived) public or private communication, as a means to suppor... Read More about Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course.

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.

Analysing Gamification Elements in Educational Environments Using an Existing Gamification Taxonomy (2019)
Journal Article
Toda, A. M., Klock, A. C., Oliveira, W., Palomino, P. T., Rodrigues, L., Shi, L., …Cristea, A. I. (2019). Analysing Gamification Elements in Educational Environments Using an Existing Gamification Taxonomy. Smart Learning Environments, 6(1), https://doi.org/10.1186/s40561-019-0106-1

Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design and analysis of gamified str... Read More about Analysing Gamification Elements in Educational Environments Using an Existing Gamification Taxonomy.

A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation (2019)
Conference Proceeding
Toda, A., Oliveira, W., Klock, A., Shi, L., Bittencourt, I. I., Gasparini, I., …Palomino, P. (2019). A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation. . https://doi.org/10.1109/icalt.2019.00028

Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design of gamified strategies. This... Read More about A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation.