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All Outputs (28)

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.

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.

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.

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.

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.

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.

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.

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.

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.

Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course (2020)
Presentation / Conference Contribution
Yu, J., Aduragba, O. T., Sun, Z., Black, S., Stewart, C., Shi, L., & Cristea, A. (2020, December). Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course. Presented at 2020 15th International Conference on Computer Science & Education (ICCSE), Delft, Netherlands

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.

Social Interactions Clustering MOOC Students: An Exploratory Study (2020)
Presentation / Conference Contribution
Shi, L., Cristea, A. I., Toda, A. M., Oliveira, W., Ahmad, A., Chang, M., Sampson, D. G., Huang, R., Hooshyar, D., Chen, N.-S., Kinshuk, & Pedaste, M. (2020, December). Social Interactions Clustering MOOC Students: An Exploratory Study. Presented at The 20th International Conference on Advanced Learning Technologies (ICALT), Tartu, Estonia

An exploratory study on social interactions of MOOC students in FutureLearn was conducted, to answer "how can we cluster students based on their social interactions?" Comments were categorized based on how students interacted with them, e.g., how a s... Read More about Social Interactions Clustering MOOC Students: An Exploratory Study.

Demographical Changes of Student Subgroups in MOOCs: Towards Predicting At-Risk Students (2019)
Presentation / Conference Contribution
Yang, B., Shi, L., Toda, A., Siarheyeva, A., Barry, C., Lang, M., Linger, H., & Schneider, C. (2019, August). Demographical Changes of Student Subgroups in MOOCs: Towards Predicting At-Risk Students. Presented at 28th International Conference on Information Systems Development (ISD2019), Toulon, France

Past studies have shown that student engagement in Massive Open Online Courses (MOOCs) could be used to identify at-risk students (students with drop-out tendency). Some studies have further considered student diversity by looking into subgroup behav... Read More about Demographical Changes of Student Subgroups in MOOCs: Towards Predicting At-Risk Students.

Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students (2019)
Presentation / Conference Contribution
Shi, L., Cristea, A. I., Toda, A., Oliveira, W., Siarheyeva, A., Barry, C., Lang, M., Linger, H., & Schneide, C. (2019, August). Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students. Presented at 28th International Conference on Information Systems Development (ISD2019), Toulon, France

Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students. The advent of complex “big data” from MOOC platforms is a challenging yet rewarding opportunity to deeply understand how students are engaged in MOOCs. Past research,... Read More about Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students.

A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation (2019)
Presentation / Conference Contribution
Toda, A., Oliveira, W., Klock, A., Shi, L., Bittencourt, I. I., Gasparini, I., Isotani, S., Cristea, A. I., & Palomino, P. (2019, July). A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation. Presented at International Conference on Advanced Learning Technologies and Technology-enhanced Learning, Maceió, Brazil

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.

How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers (2018)
Presentation / Conference Contribution
Cristea, A., Alshehri, M., Alamri, A., Kayama, M., Stewart, C., & Shi, L. (2018, October). How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers. Presented at 27th International Conference on Information Systems Development (ISD2018)., Lund

Data-intensive analysis of massive open online courses (MOOCs) is popular. Researchers have been proposing various parameters conducive to analysis and prediction of student behaviour and outcomes in MOOCs, as well as different methods to analyse and... Read More about How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers.