All Outputs (72)

Integrating Speech Input in Educational Immersive Virtual Reality Applications: A Systematic Review (2024)
Presentation / Conference Contribution
Alghamdi, N., & Cristea, A. I. (2024, August). Integrating Speech Input in Educational Immersive Virtual Reality Applications: A Systematic Review. Presented at 2024 IEEE 12th International Conference on Intelligent Systems (IS), Varna, Bulgaria

The topic of immersive virtual reality (IVR) in education has gained increasing attention in recent years, due to its potential to enhance learner outcomes and to mitigate learning costs. As we can capture a multitude of information from speech and g... Read More about Integrating Speech Input in Educational Immersive Virtual Reality Applications: A Systematic Review.

Virtual Reality (VR) in Safety Education: A Case Study of Mining Engineering (2024)
Presentation / Conference Contribution
Chang, H., Pan, Z., & Cristea, A. I. (2024, July). Virtual Reality (VR) in Safety Education: A Case Study of Mining Engineering. Presented at AIED 2024: Artificial Intelligence in Education, Recife, Brazil

Safety education and training are vital in the mining industry. However, traditional training relies on passive modalities, such as lectures, videos and brochures. These suffer from sever limitations - poor reproducibility, inefficient resource utili... Read More about Virtual Reality (VR) in Safety Education: A Case Study of Mining Engineering.

Reducing University Students’ Exam Anxiety via Mindfulness-Based Cognitive Therapy in VR with Real-Time EEG Neurofeedback (2024)
Presentation / Conference Contribution
Pan, Z., Cristea, A. I., & Li, F. W. B. (2024, July). Reducing University Students’ Exam Anxiety via Mindfulness-Based Cognitive Therapy in VR with Real-Time EEG Neurofeedback. Presented at AIED 2024: Artificial Intelligence in Education, Recife, Brazil

This research aims to develop and evaluate a novel approach to reduce university students’ exam anxiety and teach them how to better manage it using a personalised, emotion-informed Mindfulness-Based Cognitive Therapy (MBCT) method, delivered within... Read More about Reducing University Students’ Exam Anxiety via Mindfulness-Based Cognitive Therapy in VR with Real-Time EEG Neurofeedback.

Natural Language Processing for a Personalised Educational Experience in Virtual Reality (2024)
Presentation / Conference Contribution
Alghamdi, N., & Cristea, A. I. (2024, July). Natural Language Processing for a Personalised Educational Experience in Virtual Reality. Presented at Artificial Intelligence in Education (AIED 2024), Recife, Brazil

Virtual Reality (VR) is a technology that creates a simulated immersive environment, allowing users to be more engaged and interactive. The user can interact with a VR environment using head-mounted displays, hand controllers, and, in some cases, spe... Read More about Natural Language Processing for a Personalised Educational Experience in Virtual Reality.

Doctoral Colloquium—How Interactivity and Presence Affect Learning in Immersive Virtual Reality: A Mixed Methods Study Design (2024)
Presentation / Conference Contribution
Fern, N., Cristea, A. I., Nolan, S., & Stewart, C. (2024, June). Doctoral Colloquium—How Interactivity and Presence Affect Learning in Immersive Virtual Reality: A Mixed Methods Study Design. Presented at 10th International Conference of the Immersive Learning Research Network, Glasgow, Scotland, UK

This doctoral colloquium paper describes a mixed-methods study to investigate the impact of high interactivity Immersive Virtual Reality (iVR) materials on learning in higher education. It is motivated by the changing landscape of iVR technology and... Read More about Doctoral Colloquium—How Interactivity and Presence Affect Learning in Immersive Virtual Reality: A Mixed Methods Study Design.

Paraphrase Generation and Identification at Paragraph-Level (2024)
Presentation / Conference Contribution
Al Saqaabi, A., Stewart, C., Akrida, E., & Cristea, A. I. (2024, June). Paraphrase Generation and Identification at Paragraph-Level. Presented at Generative Intelligence and Intelligent Tutoring Systems ITS 2024, Thessaloniki, Greece

Towards Neuro-Enhanced Education: A Systematic Review of BCI-Assisted Development for Non-academic Skills and Abilities (2024)
Presentation / Conference Contribution
Pan, Z., & Cristea, A. I. (2024, June). Towards Neuro-Enhanced Education: A Systematic Review of BCI-Assisted Development for Non-academic Skills and Abilities. Presented at ITS 2024: Generative Intelligence and Intelligent Tutoring Systems, Thessaloniki, Greece

Students’ success in the 21st century demands not only strong academic skills but also well-developed Non-academic Skills and Abilities (NaSAs) such as critical thinking, concentration, and emotion regulation. The emerging field of Brain-Computer Int... Read More about Towards Neuro-Enhanced Education: A Systematic Review of BCI-Assisted Development for Non-academic Skills and Abilities.

Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs (2023)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
Dwan Pereira, F., Oliveira, E., Rodrigues, L., Cabral, L., Oliveira, D., Carvalho, L., Gasevic, D., Cristea, A., Dermeval, D., & Ferreira Mello, R. (2023, September). Evaluation of a hybrid AI-human recommender for CS1 instructors in a real educational scenario. Presented at Eighteenth European Conference on Technology Enhanced Learning: ECTEL 2023, Aveiro, Portugal

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.

PICA-PICA: Exploring a Customisable Smart STEAM Educational Approach via a Smooth Combination of Programming, Engineering and Art (2023)
Presentation / Conference Contribution
Nagai, T., Klem, S., Kayama, M., Asuke, T., Meccawy, M., Wang, J., Cristea, A. I., Stewart, C. D., & Shi, L. (2023, May). PICA-PICA: Exploring a Customisable Smart STEAM Educational Approach via a Smooth Combination of Programming, Engineering and Art. Presented at 2023 IEEE Global Engineering Education Conference (EDUCON), Kuwait

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.

Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance (2023)
Presentation / Conference Contribution
Aduragba, T. O., Yu, J., Cristea, A. I., & Long, Y. (2023, April). Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance. Presented at WWW '23: The ACM Web Conference 2023, Austin, Texas

People often use disease or symptom terms on social media and online forums in ways other than to describe their health. Thus the NLP health mention classification (HMC) task aims to identify posts where users are discussing health conditions literal... Read More about Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance.

Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry (2022)
Presentation / Conference Contribution
Hodgson, R., Wang, J., Cristea, A. I., & Graham, J. (2022, December). Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry. Presented at 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), Phuket, Thailand

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)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Al Moubayed, N., & Shi, L. (2022, December). Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention. Presented at IEEE Big Data, Osaka, Japan

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.

Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification (2022)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022, July). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

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)
Presentation / Conference Contribution
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

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.

Efficient Uncertainty Quantification for Multilabel Text Classification (2022)
Presentation / Conference Contribution
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). Efficient Uncertainty Quantification for Multilabel Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

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.

A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics (2022)
Presentation / Conference Contribution
Hodgson, R., Wang, J. W., Cristea, A., Matsuzaki, F., & Kubota, H. (2022, July). A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics. Presented at 15th International Conference on Educational Data Mining, Durham, England

In the academic process, comprehension and analysis of liter- ature is essential, however, time-consuming. Reviewers may encounter difficulties in identifying relevant literature, given the considerable volume of available texts. It is arduous not on... Read More about A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics.

A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data (2021)
Presentation / Conference Contribution
Sun, Z., Harit, A., Yu, J., Cristea, A., & Al Moubayed, N. (2021, July). A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data. Presented at IEEE International Joint Conference on Neural Network (IJCNN2021), Virtual

This research focuses on semi-supervised classification tasks, specifically for graph-structured data under datascarce situations. It is known that the performance of conventional supervised graph convolutional models is mediocre at classification ta... Read More about A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data.

MOOCSent: a Sentiment Predictor for Massive Open Online Courses (2021)
Presentation / Conference Contribution
Alsheri, M. A., Alrajhi, L. M., Alamri, A., & Cristea, A. I. (2021, September). MOOCSent: a Sentiment Predictor for Massive Open Online Courses. Presented at 29th International Conference on Information systems and Development (ISD2021), Valencia, Spain

One key type of Massive Open Online Course (MOOC) data is the learners’ social interaction (forum). While several studies have analysed MOOC forums to predict learning outcomes, analysing learners’ sentiments in education and, specifically, in MOOCs,... Read More about MOOCSent: a Sentiment Predictor for Massive Open Online Courses.

Forum-based Prediction of Certification in Massive Open Online Courses (2021)
Presentation / Conference Contribution
Alsheri, M. A., Alamri, A., Cristea, A. I., & Stewart, C. D. (2021, September). Forum-based Prediction of Certification in Massive Open Online Courses. Presented at 29th International Conference on Information systems and Development (ISD2021), Valencia, Spain

Massive Open Online Courses (MOOCs) have been suffering a very level of low course certification (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate), although MOOC platforms have been offer... Read More about Forum-based Prediction of Certification in Massive Open Online Courses.