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All Outputs (119)

Partially-Supervised Metric Learning via Dimensionality Reduction of Text Embeddings using Transformer Encoders and Attention Mechanisms (2024)
Journal Article
Hodgson, R., Wang, J., Cristea, A. I., & Graham, J. (2024). Partially-Supervised Metric Learning via Dimensionality Reduction of Text Embeddings using Transformer Encoders and Attention Mechanisms. IEEE Access, https://doi.org/10.1109/access.2024.3403991

Real-world applications of word embeddings to downstream clustering tasks may experience limitations to performance, due to the high degree of dimensionality of the embeddings. In particular, clustering algorithms do not scale well when applied to hi... Read More about Partially-Supervised Metric Learning via Dimensionality Reduction of Text Embeddings using Transformer Encoders and Attention Mechanisms.

Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features (2023)
Journal Article
Yacobson, E., Toda, A. M., Cristea, A. I., & Alexandron, G. (2024). Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features. Computers & Education, 210, Article 104960. https://doi.org/10.1016/j.compedu.2023.104960

Open Educational Resources (OER) repositories provide teachers with a wide range of learning resources (LRs), enabling them to design various learning sequences. However, search & select in large OER repositories can be a daunting task for teachers.... Read More about Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features.

Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums (2023)
Journal Article
Alrajhi, L., Alamri, A., Pereira, F. D., Cristea, A. I., & Oliveira, E. H. T. (2023). Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums. User Modeling and User-Adapted Interaction, https://doi.org/10.1007/s11257-023-09381-y

In MOOCs, identifying urgent comments on discussion forums is an ongoing challenge. Whilst urgent comments require immediate reactions from instructors, to improve interaction with their learners, and potentially reducing drop-out rates—the task is d... Read More about Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums.

Sim-GAIL: A generative adversarial imitation learning approach of student modelling for intelligent tutoring systems (2023)
Journal Article
Li, Z., Shi, L., Wang, J., Cristea, A. I., & Zhou, Y. (2023). Sim-GAIL: A generative adversarial imitation learning approach of student modelling for intelligent tutoring systems. Neural Computing and Applications, 35(34), 24369-24388. https://doi.org/10.1007/s00521-023-08989-w

The continuous application of artificial intelligence (AI) technologies in online education has led to significant progress, especially in the field of Intelligent Tutoring Systems (ITS), online courses and learning management systems (LMS). An impor... Read More about Sim-GAIL: A generative adversarial imitation learning approach of student modelling for intelligent tutoring systems.

Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs (2023)
Presentation / Conference
Alrajhi, L., Pereira, F. D., Cristea, A. I., & Alamri, A. (2023, September). Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs. Paper presented at HT '23: 34th ACM Conference on Hypertext and Social Media, Rome Italy

Determining when instructor intervention is needed, based on learners’ comments and their urgency in massive open online course (MOOC) environments, is a known challenge. To solve this challenge, prior art used autonomous machine learning (ML) models... Read More about Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs.

Evaluation of a hybrid AI-human recommender for CS1 instructors in a real educational scenario (2023)
Conference Proceeding
Dwan Pereira, F., Oliveira, E., Rodrigues, L., Cabral, L., Oliveira, D., Carvalho, L., …Ferreira Mello, R. (2023). Evaluation of a hybrid AI-human recommender for CS1 instructors in a real educational scenario. In Responsive and Sustainable Educational Futures: 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Aveiro, Portugal, September 4–8, 2023, Proceedings (308-323). https://doi.org/10.1007/978-3-031-42682-7_21

Automatic code graders, also called Programming Online Judges (OJ), can support students and instructors in introduction to programming courses (CS1). Using OJs in CS1, instructors select problems to compose assignment lists, whereas students submit... Read More about Evaluation of a hybrid AI-human recommender for CS1 instructors in a real educational scenario.

Using deep learning to analyze the psychological effects of COVID-19 (2023)
Journal Article
Almeqren, M. A., Almegren, M., Alhayan, F., Cristea, A. I., & Pennington, D. R. (2023). Using deep learning to analyze the psychological effects of COVID-19. Frontiers in Psychology, 14, Article 962854. https://doi.org/10.3389/fpsyg.2023.962854

Problem: Sentiment Analysis (SA) automates the classification of the sentiment of people’s attitudes, feelings or reviews employing natural language processing (NLP) and computational approaches. Deep learning has recently demonstrated remarkable suc... Read More about Using deep learning to analyze the psychological effects of COVID-19.

The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation (2023)
Journal Article
Cristea, A. I., Alamri, A., Alshehri, M., Dwan Pereira, F., Toda, A. M., Harada T. de Oliveira, E., & Stewart, C. (2024). The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation. User Modeling and User-Adapted Interaction, 34(2), 323-374. https://doi.org/10.1007/s11257-023-09374-x

Massive Online Open Course (MOOC) platforms are considered a distinctive way to deliver a modern educational experience, open to a worldwide public. However, student engagement in MOOCs is a less explored area, although it is known that MOOCs suffer... Read More about The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation.

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.

Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges (2023)
Journal Article
Pereira, F. D., Fonseca, S. C., Wiktor, S., Oliveira, D. B., Cristea, A. I., Benedict, A., …Oliveira, E. H. (2023). Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges. IEEE Access, 11, https://doi.org/10.1109/access.2023.3247189

Online judges (OJ) are a popular tool to support programming learning. However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems. This... Read More about Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges.

How Personalization Affects Motivation in Gamified Review Assessments (2023)
Journal Article
Rodrigues, L., Palomino, P. T., Toda, A. M., Klock, A. C., Pessoa, M., Pereira, F. D., …Isotani, S. (2023). How Personalization Affects Motivation in Gamified Review Assessments. International Journal of Artificial Intelligence in Education, https://doi.org/10.1007/s40593-022-00326-x

Personalized gamification aims to address shortcomings of the one-size-fits-all (OSFA) approach in improving students’ motivations throughout the learning process. However, studies still focus on personalizing to a single user dimension, ignoring mul... Read More about How Personalization Affects Motivation in Gamified Review Assessments.

Effect of emotions and personalisation on cancer website reuse intentions (2023)
Working Paper
Hadzidedic, S., Cristea, A., & Watson, D. (2023). Effect of emotions and personalisation on cancer website reuse intentions

The effect of emotions and personalisation on continuance use intentions in online health services is underexplored. Accordingly, we propose a research model for examining the impact of emotion- and personalisation-based factors on cancer website reu... Read More about Effect of emotions and personalisation on cancer website reuse intentions.

Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry (2022)
Conference Proceeding
Hodgson, R., Wang, J., Cristea, A. I., & Graham, J. (2022). Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry. . https://doi.org/10.1109/iiai-aai-winter58034.2022.00018

Barriers to the delivery of journalistic content to suitable media outlets present difficulties to both journalists and publishing houses. These may take the form of barriers to the identification of key individuals to whom the content is relevant, a... Read More about Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry.

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.

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.

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.

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.