Staff profile

Bio

Alexandra I. Cristea is Professor, Deputy Executive Dean of the Faculty of Science, Founder of the Artificial Intelligence in Human Systems research group in the Department of Computer Science at Durham University, and lead of the SCENE lab. She is Alan Turing Academic Liaison for Durham, N8 CIR Digital Humanities team lead for Durham and member of the IEEE European Public Policy on ICT, and Senior Common Room honorary member as prior Advisory Board Member at the Ustinov College. Her research includes web science, learning analytics, user modelling and personalisation, semantic web, social web, authoring, with over 350 papers on these subjects (over 7100 citations on Google Scholar, h-index 43). Especially, her work on gamification for education - over 340 citations -  and frameworks for adaptive systems has influenced many researchers and is highly cited  - over 230 citations. She was classified within the top 50 researchers in the world in the area of educational computer-based research according to  Microsoft Research (2015-02-10). Prof. Cristea has been highly active and has an influential role in international research projects. She leads and has led various projects - the JANET and JANET 2.0 (Joint Lab in Learning Analytics for Personalised Science Teaching) project series ('20-'23; '24-'25); funded by the Weizman Institute and the British Council;  the Predictive and prescriptive analytics for the media industry project series collaboration with Distinctive Publishing ('18-'19; '19-'23; '24-'25);the ATI@Durham Research Network (2022); the Epistemological Engine (2021-22);Newton funded workshop on Higher Education for All ('14-'18), Santander funded Education for disadvantaged pupils ('14-18'), Warwick-funded project APLIC ('11-;12), EU Minerva projects ALS (06-09) and EU Minerva ADAPT (’02-’05); as well as participated as university PI in several EU FP7 projects - BLOGFOREVER (’11-’13), GRAPPLE (’08- ’11), PROLEARN (’07) and as co-PI in the Warwick-funded Engaging Young People with Assistance Technologies (’13-’15) also featured by the BBC. Recently she has taken giving back to the community to a different level, leading the MESSENGER (Women inSTEM and Cultural Diversity) (2022); Empowering women in science through mentoring and exchanging experiences (2021-22) (UK-Brazil Gender Equality Partnership funded by the British Council), and co-leading the TechUP  project series  (2019-2020: training 100 women in computer science from various (BAME) backgrounds)(TechUPOnline 2020)(Bootcamp 2021)(Nominet-funded '22-'25). She has been keynote/invited speaker, chair, organiser, co-organiser, panellist and program committee member of various conferences in her research field (including, for example,  AIED, ECTEL, ITS, UMAP, ED-MEDIA, Hypertext, Adaptive Hypermedia, EDM, ICCE, ICAI). She was an Associate Editor of the ACM Computing Surveys, Associate Editor of Frontiers in Artificial Intelligence and the IEEE Transactions on Learning Technologies, co-editor of the Advanced Technologies and Learning Journal and executive peer reviewer of the IEEE LTTF Education Technology and Society Journal. She acted as UNESCO expert for adaptive web-based education at a high-level (Ministry of Education and Educational institutes) meeting of East European countries, educational invited expert for the Romanian prime minister, as well as EU expert for H2020, FP7, FP6, eContentPlus. She has interacted with various international and local media (she has given a recent live radio interview to Power 106FM in Jamaica; work from her lab has been publicised by Free Radio Coventry & Warwickshire, Birmingham Post, Birmingham Mail, phys.org, The Daily Dot, Mirror, Vice Motherboard, BBC News, Pinterest, Globenewswire, Romanian TV). She is a BCS fellow, a HEA fellow, IEEE Senior Member and IEEE CS member, EATEL (European Association of Technology Enhanced Learning) founding member, ACM member.

News

Delighted to have host a return conference in Durham, ECTEL 2025, 20th anniversary edition, organised by its gerning society EATEL, both resulting from our European project PROLEARN 20 years ago!

Happy to serve as PC Chair to AIED 2025.

... and prior to this:

Delighted to have hosted two major conferences in Durham during the Intelligence in Education Week at Durham event: AIED 2022 and EDM 2022!

Special Issues:

• Special Issue "Learner–Computer Interaction and Intelligent Tutoring Systems" in MDPI, Education Sciences
• Special Issue "Artificial Intelligence Techniques for Personalized Educational Software" in Frontiers in AI, AI for Human Learning and Behavior Change

 

Publications

• Google Scholar

 

• Adaptive, personalised web
• Applied AI
• Learner Analytics Data Analytics
• Semantic Web
• Social Web
• User Modelling
• VR, XR, CR
• Web Science

• 2022: Member of IEEE EPPC Working Group on ICT:
• 2022: External Examiner Imperial MSc in Advanced Computing:
• 2022: Associate Editor of ACM Computing Surveys:
• 2021: EPSRC College Member:
• 2021: Alan Turing Academic Liaison for Durham:
• 2020: Associate Editor of Frontiers in Artificial Intelligence:
• 2019: N8 CIR Digital Humanities team lead for Durham:
• 2019: Advisory Board Member at Ustinov College:
• 2018: Associate Editor of the IEEE Transactions on Learning Technologies:
• 2016: IEEE Senior Member:
• 2015: Fellow of the Higher Education Academy in Britain:
• 2011: Fellow of the British Computing Society:

Chapter in book

• Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews
  
  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 (pp. 294-306). Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_24
• MOOCs Paid Certification Prediction Using Students Discussion Forums
  
  Alshehri, M., & Cristea, A. I. (2022). MOOCs Paid Certification Prediction Using Students Discussion Forums. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (pp. 542-545). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_111
• Intervention Prediction in MOOCs Based on Learners’ Comments: A Temporal Multi-input Approach Using Deep Learning and Transformer Models
  
  Alrajhi, L., Alamri, A., & Cristea, A. I. (2022). Intervention Prediction in MOOCs Based on Learners’ Comments: A Temporal Multi-input Approach Using Deep Learning and Transformer Models. In S. Crossley & E. Popescu (Eds.), Intelligent Tutoring Systems (pp. 227-237). Springer Verlag. https://doi.org/10.1007/978-3-031-09680-8_22
• Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data
  
  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 (pp. 256-268). Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_21
• MEMORABLE: A Multi-playEr custoMisable seriOus Game fRAmework for cyBer-security LEarning
  
  Wang, J., Hodgson, R., & Cristea, A. I. (2022). MEMORABLE: A Multi-playEr custoMisable seriOus Game fRAmework for cyBer-security LEarning. In S. Crossley & E. Popescu (Eds.), Intelligent Tutoring Systems (pp. 313-322). Springer Verlag. https://doi.org/10.1007/978-3-031-09680-8_29
• SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour
  
  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 (pp. 348-351). Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_67
• Adopting Automatic Machine Learning for Temporal Prediction of Paid Certification in MOOCs
  
  Alshehri, M., Alamri, A., & Cristea, A. I. (2022). Adopting Automatic Machine Learning for Temporal Prediction of Paid Certification in MOOCs. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education (pp. 717-723). Springer Verlag. https://doi.org/10.1007/978-3-031-11644-5_73
• An AI-Based Feedback Visualisation System for Speech Training
  
  Wynn, A. T., Wang, J., Umezawa, K., & Cristea, A. I. (2022). An AI-Based Feedback Visualisation System for Speech Training. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (pp. 510-514). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_104
• Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs
  
  Aljohani, T., Cristea, A. I., & Alrajhi, L. (2022). Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (pp. 396-399). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_78
• A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs
  
  Alrajhi, L., Pereira, F. D., Cristea, A. I., & Aljohani, T. (2022). A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (pp. 424-427). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_84
• Urgency Analysis of Learners’ Comments: An Automated Intervention Priority Model for MOOC
  
  Alrajhi, L., Alamri, A., Pereira, F. D., & Cristea, A. I. (2021). Urgency Analysis of Learners’ Comments: An Automated Intervention Priority Model for MOOC. In A. I. Cristea & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (pp. 148-160). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_18
• Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification
  
  Aljohani, T., & Cristea, A. I. (2021). Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification. In A. I. Cristea & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (pp. 136-147). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_17
• Predicting Certification in MOOCs based on Students’ Weekly Activities
  
  Alshehri, M., Alamri, A., & Cristea, A. I. (2021). Predicting Certification in MOOCs based on Students’ Weekly Activities. In A. I. Cristea & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (pp. 173-185). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_20
• MOOC next week dropout prediction: weekly assessing time and learning patterns
  
  Alamri, A., Sun, Z., Cristea, A. I., Steward, C., & Pereira, F. D. (2021). MOOC next week dropout prediction: weekly assessing time and learning patterns. In A. I. Cristea & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (pp. 119-130). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_15
• Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study
  
  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 (pp. 139-144). Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_25
• A Recommender System Based on Effort: Towards Minimising Negative Affects and Maximising Achievement in CS1 Learning
  
  Pereira, F. D., Junior, H. B., Rodriquez, L., Toda, A., Oliveira, E. H., Cristea, A. I., Oliveira, D. B., Carvalho, L. S., Fonseca, S. C., Alamri, A., & Isotani, S. (2021). A Recommender System Based on Effort: Towards Minimising Negative Affects and Maximising Achievement in CS1 Learning. In A. I. Cristea & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (pp. 466-480). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_51
• Encouraging Teacher-sourcing of Social Recommendations Through Participatory Gamification Design
  
  Toda Yacobson, E., Cristea, A., & Alexandron, G. I. (2021). Encouraging Teacher-sourcing of Social Recommendations Through Participatory Gamification Design. In A. Cristea & C. I. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (pp. 418-429). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_46
• Exploring Navigation Styles in a FutureLearn MOOC
  
  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 (pp. 45-55). Springer Verlag. https://doi.org/10.1007/978-3-030-49663-0_7
• Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities
  
  Alamri, A., Alshehri, M., Cristea, A. I., Pereira, F. D., Oliveira, E., Shi, L., & Stewart, C. (2019). Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities. In A. Coy, Y. Hayashi, & M. Chang (Eds.), Intelligent tutoring systems. ITS 2019. (pp. 163-173). Springer Verlag. https://doi.org/10.1007/978-3-030-22244-4_20
• What's new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval
  
  Zhou, Y., Demidova, E., & Cristea, A. (2017). What’s new? Analysing language-specific Wikipedia entity contexts to support entity-centric news retrieval. In N. Nguyen, R. Kowalczyk, A. Pinto, & J. Cardoso (Eds.), Transactions on Computational Collective Intelligence XXVI. (pp. 2010-231). Springer Verlag. https://doi.org/10.1007/978-3-319-59268-8_10
• Multifaceted open social learner modelling
  
  Shi, L., Cristea, A., & Hadzidedic, S. (2014). Multifaceted open social learner modelling. In P. Elvira, R. W. Lau, K. Pata, H. Leung, & L. Mart (Eds.), Advances in Web-Based Learning – ICWL 2014, 13th International Conference, Tallinn, Estonia, August 14-17, 2014, Proceedings. (pp. 32-42). Springer Verlag. https://doi.org/10.1007/978-3-319-09635-3_4

Conference Paper

• Integrating Speech Input in Educational Immersive Virtual Reality Applications: A Systematic Review
  
  Alghamdi, N., & Cristea, A. I. (2024). Integrating Speech Input in Educational Immersive Virtual Reality Applications: A Systematic Review. In 2024 IEEE 12th International Conference on Intelligent Systems (IS) (pp. 1-8). IEEE. https://doi.org/10.1109/is61756.2024.10705165
• Virtual Reality (VR) in Safety Education: A Case Study of Mining Engineering
  
  Chang, H., Pan, Z., & Cristea, A. I. (2024). Virtual Reality (VR) in Safety Education: A Case Study of Mining Engineering. In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (pp. 382-387). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64312-5_47
• Reducing University Students’ Exam Anxiety via Mindfulness-Based Cognitive Therapy in VR with Real-Time EEG Neurofeedback
  
  Pan, Z., Cristea, A. I., & Li, F. W. B. (2024). Reducing University Students’ Exam Anxiety via Mindfulness-Based Cognitive Therapy in VR with Real-Time EEG Neurofeedback. In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (pp. 418-423). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64312-5_52
• Doctoral Colloquium—How Interactivity and Presence Affect Learning in Immersive Virtual Reality: A Mixed Methods Study Design
  
  Fern, N., Cristea, A. I., Nolan, S., & Stewart, C. (2024). Doctoral Colloquium—How Interactivity and Presence Affect Learning in Immersive Virtual Reality: A Mixed Methods Study Design. In 2024 10th International Conference of the Immersive Learning Research Network (iLRN) Proceedings - Selected Academic Contributions (pp. 103-111). The Immersive Learning Research Network. https://doi.org/10.56198/u6c0wfyqb
• Towards Neuro-Enhanced Education: A Systematic Review of BCI-Assisted Development for Non-academic Skills and Abilities
  
  Pan, Z., & Cristea, A. I. (2024). Towards Neuro-Enhanced Education: A Systematic Review of BCI-Assisted Development for Non-academic Skills and Abilities. In Generative Intelligence and Intelligent Tutoring Systems (pp. 49-66). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-63031-6_5
• Natural Language Processing for a Personalised Educational Experience in Virtual Reality
  
  Alghamdi, N., & Cristea, A. I. (2024). Natural Language Processing for a Personalised Educational Experience in Virtual Reality. In A. M. Olney, I.-A. Chounta, Z. Liu, O. C. Santos, & I. Ibert Bittencourt (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (pp. 355-361). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64312-5_43
• Paraphrase Generation and Identification at Paragraph-Level
  
  Al Saqaabi, A., Stewart, C., Akrida, E., & Cristea, A. I. (2024). Paraphrase Generation and Identification at Paragraph-Level. In Generative Intelligence and Intelligent Tutoring Systems (pp. 278-291). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-63031-6_24
• Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs
  
  Alrajhi, L., Pereira, F. D., Cristea, A. I., & Alamri, A. (2023, September 4 – 2023, September 8). Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs [Conference paper]. Presented at HT ’23: 34th ACM Conference on Hypertext and Social Media, Rome Italy. ACM.
• Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance
  
  Aduragba, T. O., Yu, J., Cristea, A. I., & Long, Y. (2023). Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance. In WWW ’23: Proceedings of the ACM Web Conference 2023 (pp. 3928-3936). ACM. https://doi.org/10.1145/3543507.3583877
• Evaluation of a hybrid AI-human recommender for CS1 instructors in a real educational scenario
  
  Dwan Pereira, F., Oliveira, E., Rodrigues, L., Cabral, L., Oliveira, D., Carvalho, L., Gasevic, D., Cristea, A., Dermeval, D., & 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 (pp. 308-323). Springer. https://doi.org/10.1007/978-3-031-42682-7_21
• PICA-PICA: Exploring a Customisable Smart STEAM Educational Approach via a Smooth Combination of Programming, Engineering and Art
  
  Nagai, T., Klem, S., Kayama, M., Asuke, T., Meccawy, M., Wang, J., Cristea, A. I., Stewart, C. D., & Shi, L. (2023). 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. https://doi.org/10.1109/educon54358.2023.10125184
• A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics
  
  Hodgson, R., Wang, J. W., Cristea, A., Matsuzaki, F., & Kubota, H. (2022). A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics (A. Mitrovic & N. Bosch, Eds.). International Educational Data Mining Society. https://doi.org/10.5281/zenodo.6853001
• Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry
  
  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. Presented at 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), Phuket, Thailand. https://doi.org/10.1109/iiai-aai-winter58034.2022.00018
• Efficient Uncertainty Quantification for Multilabel Text Classification
  
  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. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy. https://doi.org/10.1109/ijcnn55064.2022.9892871
• Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification
  
  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. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy. https://doi.org/10.1109/ijcnn55064.2022.9892257
• INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations
  
  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. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy. https://doi.org/10.1109/ijcnn55064.2022.9892336
• Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention
  
  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. Presented at IEEE Big Data, Osaka, Japan. https://doi.org/10.1109/bigdata55660.2022.10020791
• MOOCSent: a Sentiment Predictor for Massive Open Online Courses
  
  Alsheri, M. A., Alrajhi, L. M., Alamri, A., & Cristea, A. I. (2021, September 1). MOOCSent: a Sentiment Predictor for Massive Open Online Courses. Presented at 29th International Conference on Information systems and Development (ISD2021), Valencia, Spain.
• Forum-based Prediction of Certification in Massive Open Online Courses
  
  Alsheri, M. A., Alamri, A., Cristea, A. I., & Stewart, C. D. (2021, August). Forum-based Prediction of Certification in Massive Open Online Courses. Presented at 29th International Conference on Information systems and Development (ISD2021), Valencia, Spain.
• A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs
  
  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.), Lecture Notes in Computer Science (pp. 28-37). Springer. https://doi.org/10.1007/978-3-030-80421-3_4
• Towards a Human-AI hybrid system for categorising programming problems
  
  Pereira, F. D., Piris, F., Cristo da Fonseca, S., Cristea, A., Oliveira, E. H., Carvalho, L., & Fernandes, D. (2021). Towards a Human-AI hybrid system for categorising programming problems. In SIGCSE ’21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 94-100). ACM. https://doi.org/10.1145/3408877.3432422
• Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums
  
  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 (pp. 78-90). https://doi.org/10.1007/978-3-030-80421-3_10
• A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns
  
  Li, Z., Shi, L., Cristea, A. I., & Zhou, Y. (2021). A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns. Presented at ACM Designing Interactive Systems (DIS), Virtual. https://doi.org/10.1145/3461778.3462135
• Agent-based Simulation of the Classroom Environment to Gauge the Effect of Inattentive or Disruptive Students
  
  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 (pp. 211-223). https://doi.org/10.1007/978-3-030-80421-3_23
• Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours
  
  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 (pp. 48-53). Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_8
• Interpretable AI to Understand Early Effective and Ineffective Programming Behaviours from CS1 Learners
  
  Pereira, F. D., Oliveira, E. H. T. de, Oliveira, D. B. F. de, Carvalho, L. S. G. de, & Cristea, A. I. (2021). Interpretable AI to Understand Early Effective and Ineffective Programming Behaviours from CS1 Learners. Presented at Anais Estendidos do I Simpósio Brasileiro de Educação em Computação (EDUCOMP Estendido 2021), Online. https://doi.org/10.5753/educomp_estendido.2021.14853
• A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data
  
  Sun, Z., Harit, A., Yu, J., Cristea, A., & Al Moubayed, N. (2021). A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data. Presented at IEEE International Joint Conference on Neural Network (IJCNN2021), Virtual. https://doi.org/10.1109/ijcnn52387.2021.9533981
• COVID-19’s Impact on the Telecommunications Companies
  
  Almuqren, L., & Cristea, A. I. (2021). COVID-19’s Impact on the Telecommunications Companies. In Álvaro Rocha, H. Adeli, G. Dzemyda, F. Moreira, & A. M. R. Correia (Eds.), WorldCIST 2021: Trends and Applications in Information Systems and Technologies (pp. 318-327). Springer Verlag. https://doi.org/10.1007/978-3-030-72654-6_31
• Automatic creating variation of CS1 assignments and exams
  
  Pereira, F. D., Júnior, H. B. de F., Oliveira, E. H. T. de, Carvalho, L. S. G. de, Oliveira, D. B. F. de, Benedict, A., Dorodchi, M., & Cristea, A. I. (2021). Automatic creating variation of CS1 assignments and exams. Presented at Anais Estendidos do I Simpósio Brasileiro de Educação em Computação (EDUCOMP Estendido 2021), Online. https://doi.org/10.5753/educomp_estendido.2021.14856
• Wide-Scale Automatic Analysis of 20 Years of ITS Research
  
  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 (pp. 8-21). https://doi.org/10.1007/978-3-030-80421-3_2
• Sequential Recommender via Time-aware Attentive Memory Network
  
  Ji, W., Wang, K., Wang, X., Chen, T., & Cristea, A. I. (2020). Sequential Recommender via Time-aware Attentive Memory Network. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20), October 19–23, 2020, Virtual Event, Ireland. (pp. 565-574). Association for Computing Machinery (ACM). https://doi.org/10.1145/3340531.3411869
• Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course
  
  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) (pp. 182-187). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/iccse49874.2020.9201631
• Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs
  
  Alharbi, K., Alrajhi, L., Cristea, A. I., Bittencourt, I. I., Isotani, S., & James, A. (2020). Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs. In V. Kumar & C. Troussas (Eds.), Intelligent Tutoring Systems (pp. 142-151). Springer Verlag. https://doi.org/10.1007/978-3-030-49663-0_18
• GamiCSM: relating education, culture and gamification - a link between worlds
  
  Toda, A., Klock, A. C. T., Palomino, P. T., Rodrigues, L., Oliveira, W., Stewart, C., Cristea, A. I., Gasparini, I., & Isotani, S. (2020). GamiCSM: relating education, culture and gamification - a link between worlds. Presented at 19th Brazilian Symposium on Human Factors in Computing Systems, Diamantina, Brazil. https://doi.org/10.1145/3424953.3426490
• A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts
  
  Alrajhi, L., Alharbi, K., & Cristea, A. I. (2020). A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts. In C. Troussas & V. Kumar (Eds.), ITS 2020: Intelligent Tutoring Systems (pp. 226-236). Springer Verlag. https://doi.org/10.1007/978-3-030-49663-0_27
• Digital Inclusion in Nothern England: Training Women from Underrepresented Communities in Tech: A Data Analytics Case Study
  
  Aduragba, O. T., Yu, J., Cristea, A. I., Hardey, M., & Black, S. (2020). Digital Inclusion in Nothern England: Training Women from Underrepresented Communities in Tech: A Data Analytics Case Study. In 2020 15th International Conference on Computer Science & Education (ICCSE) (pp. 162-168). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/iccse49874.2020.9201693
• Can We Use Gamification to Predict Students’ Performance? A Case Study Supported by an Online Judge
  
  Pereira, F. D., Toda, A., Oliveira, E. H., Cristea, A. I., Isotani, S., Laranjeira, D., Almeida, A., & Mendonça, J. (2020). Can We Use Gamification to Predict Students’ Performance? A Case Study Supported by an Online Judge. In Lecture Notes in Computer Science (pp. 259-269). Springer Verlag. https://doi.org/10.1007/978-3-030-49663-0_30
• For whom should we gamify? Insights on the users intentions and context towards gamification in education
  
  Toda, A., Pereira, F. D., Klock, A. C. T., Rodrigues, L., Palomino, P., Oliveira, W., Oliveira, E. H. T., Gasparini, I., Cristea, A. I., & Isotani, S. (2020). For whom should we gamify? Insights on the users intentions and context towards gamification in education. Presented at Simpósio Brasileiro de Informática na Educação (SBIE 2020), Porto Alegre, Brazil. https://doi.org/10.5753/cbie.sbie.2020.471
• Automatic Subject-based Contextualisation of Programming Assignment Lists
  
  Fonseca, S. C., Pereira, F. D., Oliveira, E. H., Oliveira, D. B., Carvalho, L. S., & Cristea, A. I. (2020). Automatic Subject-based Contextualisation of Programming Assignment Lists. In A. N. Rafferty, J. Whitehill, C. Romero, & V. Cavalli-Sforza (Eds.), Proceedings of the 13th International Conference on Educational Data Mining. (pp. 81-91). EDM.
• Planning Gamification Strategies based on User Characteristics and DM: A Gender-based Case Study
  
  Toda, A., Oliveira, W., Shi, L., Bittencourt, I. I., Isotani, S., & Cristea, A. I. (2019). Planning Gamification Strategies based on User Characteristics and DM: A Gender-based Case Study. In M. Desmarais, C. F. Lynch, A. Merceron, & R. Nkambou (Eds.), Proceedings of the 12th International Conference on Educational Data Mining. (pp. 438-443). Educational Data Mining 2019.
• A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation
  
  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. https://doi.org/10.1109/icalt.2019.00028
• Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation
  
  Tsakalidis, A., Liakata, M., Damoulas, T., & Cristea, A. I. (2019). Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, M. Berlingerio, & N. Hurley (Eds.), Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III. (pp. 407-423). Springer Verlag. https://doi.org/10.1007/978-3-030-10997-4_25
• Narrative for Gamification in Education: Why Should you Care?
  
  Toledo Palomino, P., Toda, A. M., Oliveira, W., Cristea, A. I., & Isotani, S. (2019). Narrative for Gamification in Education: Why Should you Care?. Presented at 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Maceio, Brazil. https://doi.org/10.1109/icalt.2019.00035
• Research on Prediction of Infectious Diseases, their spread via Social Media and their link to Education
  
  Aduragba, O. T., & Cristea, A. I. (2019). Research on Prediction of Infectious Diseases, their spread via Social Media and their link to Education. In Proceedings of the 2019 4th International Conference on Information and Education Innovations (ICIEI 2019). (pp. 38-42). Association for Computing Machinery (ACM). https://doi.org/10.1145/3345094.3345118
• Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm
  
  Pereira, F. D., Oliveira, E. H., Fernandes, D., & Cristea, A. (2019). Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm. Presented at 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Maceio, Brazil. https://doi.org/10.1109/icalt.2019.00066
• Exploring content game elements to support gamification design in educational systems: narrative and storytelling
  
  Palomino, P., Toda, A., Oliveira, W., Rodrigues, L., Cristea, A. I., & Isotani, S. (2019). Exploring content game elements to support gamification design in educational systems: narrative and storytelling. Presented at Anais do XXX Simpósio Brasileiro de Informática na Educação (SBIE 2019). https://doi.org/10.5753/cbie.sbie.2019.773
• Proceedings of the 3rd Conference on Computing Education Practice
  
  Bradley, S., & Cristea, A. (Eds.). (2019). Proceedings of the 3rd Conference on Computing Education Practice. In Proceedings of the 3rd Conference on Computing Education Practice. ACM.
• Predicting Learners' Demographics Characteristics: Deep Learning Ensemble Architecture for Learners' Characteristics Prediction in MOOCs
  
  Aljohani, T., & Cristea, A. I. (2019). Predicting Learners’ Demographics Characteristics: Deep Learning Ensemble Architecture for Learners’ Characteristics Prediction in MOOCs. In Proceedings of the 2019 4th International Conference on Information and Education Innovations - ICIEI 2019 (pp. 23-27). ACM. https://doi.org/10.1145/3345094.3345119
• Early Dropout Prediction for Programming Courses Supported by Online Judges
  
  Pereira, F. D., Oliveira, E., Cristea, A., Fernandes, D., Silva, L., Aguiar, G., Alamri, A., & Alshehri, M. (2019). Early Dropout Prediction for Programming Courses Supported by Online Judges. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Lecture Notes in Computer Science. Springer Verlag. https://doi.org/10.1007/978-3-030-23207-8_13
• Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses
  
  Shi, L., & Cristea, A. (2018). Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Designing Digitalization (ISD2018 Proceedings). Lund, Sweden: Lund University.. Association for Information Systems.
• How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers
  
  Cristea, A., Alshehri, M., Alamri, A., Kayama, M., Stewart, C., & Shi, L. (2018). How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Proceedings of the 27th International Conference on Information Systems Development (ISD2018), Education Track, Lund, Sweden, August 22-24, 2018.. Association for Information Systems.
• Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses
  
  Cristea, A., Alamri, A., Kayama, M., Stewart, C., Alsheri, M., & Shi, L. (2018). Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Designing Digitalization (ISD2018 Proceedings). Lund, Sweden: Lund University.. Association for Information Systems.
• In-depth Exploration of Engagement Patterns in MOOCs
  
  Lei, S., & Cristea, A. (2018). In-depth Exploration of Engagement Patterns in MOOCs. In H. Hacid, W. Cellary, H. Wang, H.-Y. Paik, & R. Zhou (Eds.), Web information systems engineering - WISE 2018 : 19th International Conference, Dubai, United Arab Emirates, November 12-15, 2018. Proceedings. Part II (pp. 395-409). Springer Verlag. https://doi.org/10.1007/978-3-030-02925-8_28
• Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum
  
  Tsakalidis, A., Aletras, N., Cristea, A., & Liakata, M. (2018). Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. (pp. 367-376). Association for Computing Machinery (ACM). https://doi.org/10.1145/3269206.3271783
• Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses
  
  Cristea, A. I., Alamri, A., Kayama, M., Stewart, C., Alshehri, M., & Shi, L. (2018). Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Designing Digitalization (ISD2018 Proceedings).. Association for Information Systems.
• How is Learning Fluctuating? FutureLearn MOOCs Fine-grained Temporal Analysis and Feedback to Teachers and Designers
  
  Cristea, A. I., Alamri, A., Kayama, M., Stewart, C., Alshehri, M., & Shi, L. (2018). How is Learning Fluctuating? FutureLearn MOOCs Fine-grained Temporal Analysis and Feedback to Teachers and Designers. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Designing Digitalization (ISD2018 Proceedings). Association for Information Systems.
• A large-scale category-based evaluation of a visual language for adaptive hypermedia
  
  Khan, J., Cristea, A., & Alamri, A. (2018). A large-scale category-based evaluation of a visual language for adaptive hypermedia. In Proceedings of the 2018 the 3rd International Conference on Information and Education Innovations (ICIEI’18) : London, United Kingdom, June 30 - July 02, 2018. (pp. 94-98). Association for Computing Machinery (ACM). https://doi.org/10.1145/3234825.3234834
• On the need for fine-grained analysis of Gender versus Commenting Behaviour in MOOCs
  
  Alshehri, M., Foss, J., Cristea, A. I., Kayama, M., Shi, L., Alamri, A., & Tsakalidis, A. (2018). On the need for fine-grained analysis of Gender versus Commenting Behaviour in MOOCs. In Proceedings of the 2018 the 3rd International Conference on Information and Education Innovations (ICIEI’18) : London, United Kingdom, June 30 - July 02, 2018. (pp. 73-77). Association for Computing Machinery (ACM). https://doi.org/10.1145/3234825.3234833
• An intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System
  
  Alamri, A., Rusby, H., Cristea, A. I., Khan, J., Shi, L., & Stewart, C. (2018). An intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System. In Proceedings of the 2018 the 3rd International Conference on Information and Education Innovations (ICIEI’18) : London, United Kingdom, June 30 - July 02, 2018. (pp. 57-61). Association for Computing Machinery (ACM). https://doi.org/10.1145/3234825.3234835
• Connecting Targets to Tweets: Semantic Attention-based Model for Target-Specific stance Detection
  
  Zhou, Y., Cristea, A. I., & Shi, L. (2017). Connecting Targets to Tweets: Semantic Attention-based Model for Target-Specific stance Detection. In A. Bouguettaya, Y. Gao, A. Klimenko, L. Chen, X. Zhang, F. Dzerzhinskiy, W. Jia, S. V. Klimenko, & Q. Li (Eds.), Web Information Systems Engineering -- WISE 2017 (pp. 18-32). Springer Verlag. https://doi.org/10.1007/978-3-319-68783-4_2
• Identifying Objectives for a Learning Space Management System with Value-focused Thinking
  
  Alessandrini, A., Cristea, A., Hartikainen, M., Isomäki, H., & Tuhkala, A. (2017). Identifying Objectives for a Learning Space Management System with Value-focused Thinking. In P. Escudeiro, G. Costagliola, S. Zvacek, J. Uhomoibhi, & B. M. McLaren (Eds.), Proceedings of the 9th International Conference on Computer Supported Education (CSEDU). (pp. 25-34). SciTePress. https://doi.org/10.5220/0006230300250034
• Combining heterogeneous user generated data to sense well-being
  
  Tsakalidis, A., Liakata, M., Damoulas, T., Jellinek, B., Guo, W., & Cristea, A. (2016). Combining heterogeneous user generated data to sense well-being. In Y. Matsumoto & R. Prasad (Eds.), Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics : Technical Papers. (pp. 3007-3018). The COLING 2016 Organizing Committee.
• Real-time timeline summarisation for high-impact events in Twitter
  
  Zhou, Y., Kanhabua, N., & Cristea, A. (2016). Real-time timeline summarisation for high-impact events in Twitter. In G. A. Kaminka, M. Fox, P. Bouquet, E. Hüllermeier, V. Dignum, F. Dignum, & F. van Harmelen (Eds.), Proceedings of the 22nd European Conference on Artificial Intelligence, 29 August–2 September 2016, The Hague, The Netherlands. (pp. 1158-1166). IOS Press. https://doi.org/10.3233/978-1-61499-672-9-1158
• Motivational gamification strategies rooted in self-determination theory for social adaptive E-Learning
  
  Cristea, A., & Shi, L. (2016). Motivational gamification strategies rooted in self-determination theory for social adaptive E-Learning. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Intelligent Tutoring Systems, 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016, Proceedings. (pp. 294-300). Springer Verlag. https://doi.org/10.1007/978-3-319-39583-8_32
• Who likes me more? Analysing entity-centric language-specific bias in multilingual Wikipedia
  
  Zhou, Y., Demidova, E., & Cristea, A. (2016). Who likes me more? Analysing entity-centric language-specific bias in multilingual Wikipedia. In Proceedings of the 2016 ACM Symposium on Applied Computing : Artificial Intelligence and Agents, Distributed Systems, and Information Systems. (pp. 750-757). ACM. https://doi.org/10.1145/2851613.2851858
• Towards detection of influential sentences affecting reputation in Wikipedia
  
  Zhou, Y., & Cristea, A. (2016). Towards detection of influential sentences affecting reputation in Wikipedia. In W. Nejdl (Ed.), WebSci ’16 : Proceedings of the 8th ACM Conference on Web Science (pp. 244-248). ACM. https://doi.org/10.1145/2908131.2908177
• WarwickDCS : from phrase-based to target-specific sentiment recognition
  
  Townsend, R., Tsakalidis, A., Zhou, Y., Wang, B., Liakata, M., Zubiaga, A., Cristea, A., & Procter, R. (2015). WarwickDCS : from phrase-based to target-specific sentiment recognition. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). (pp. 657-663). Association for Computational Linguistics. https://doi.org/10.18653/v1/s15-2110
• What do people affected by cancer talk about online? Text analysis of online cancer community usage in Bosnia and Herzegovina
  
  Hadzidedic Bazdarevic, S., & Cristea, A. I. (2015). What do people affected by cancer talk about online? Text analysis of online cancer community usage in Bosnia and Herzegovina. Presented at Fifth International Conference on Social Medial Technologies, Communication and Informatics (SOTICS), Barcelona, Spain.
• A Taxonomy-Based Evaluation of Personalized E-Advertisement
  
  Al Qudah, D. A., Cristea, A. I., Hadzidedic Bazdarevic, S., Al-Saqqa, S., & Al-Sayyad, R. M. (2015). A Taxonomy-Based Evaluation of Personalized E-Advertisement. Presented at IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK. https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.56
• The Critical Role of Profiles in Social E-Learning Design
  
  Shi, L., Cristea, A. I., & Hadzidedic, S. (2014, October 14). The Critical Role of Profiles in Social E-Learning Design. Presented at 15th Annual Conference on Information Technology Education (SIGITE), Atlanta, Georgia, US. https://doi.org/10.1145/2656450.2656458
• Towards understanding learning behavior patterns in social adaptive personalized e-learning systems
  
  Shi, L., Cristea, A., Awan, M., Stewart, C., & Hendrix, M. (2013). Towards understanding learning behavior patterns in social adaptive personalized e-learning systems. In Hyperconnected World: Anything, Anywhere, Anytime; Proceedings of the 19th Americas Conference on Information Systems (AMCIS 2013). (pp. 1-10). Association for Information Systems.
• Zero-cost labelling with web feeds for weblog data extraction
  
  Gkotsis, G., Stepanyan, K., Cristea, A., & Joy, M. (2013). Zero-cost labelling with web feeds for weblog data extraction. In WWW ’13 Companion : Proceedings of the 22nd international conference on World Wide Web companion (pp. 73-74). International World Wide Web Conferences Steering Committee.

Journal Article

• Explainable artificial intelligence and advanced feature selection methods for predicting gas concentration in longwall mining
  
  Chang, H., Wang, X., Cristea, A. I., Meng, X., Hu, Z., & Pan, Z. (2025). Explainable artificial intelligence and advanced feature selection methods for predicting gas concentration in longwall mining. Information Fusion, 118, Article 102976. https://doi.org/10.1016/j.inffus.2025.102976
• Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost
  
  Al-Qudah, D. A., Al-Zoubi, A. M., Cristea, A. I., Merelo-Guervós, J. J., Castillo, P. A., & Faris, H. (2025). Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost. PeerJ Computer Science, 11, Article e2370. https://doi.org/10.7717/peerj-cs.2370
• Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums
  
  Alrajhi, L., Alamri, A., Pereira, F. D., Cristea, A. I., & Oliveira, E. H. T. (2024). Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums. User Modeling and User-Adapted Interaction, 34(3), 797-852. https://doi.org/10.1007/s11257-023-09381-y
• How Personalization Affects Motivation in Gamified Review Assessments
  
  Rodrigues, L., Palomino, P. T., Toda, A. M., Klock, A. C., Pessoa, M., Pereira, F. D., Oliveira, E. H., Oliveira, D. F., Cristea, A. I., Gasparini, I., & Isotani, S. (2024). How Personalization Affects Motivation in Gamified Review Assessments. International Journal of Artificial Intelligence in Education, 34(2), 147-184. https://doi.org/10.1007/s40593-022-00326-x
• Partially-Supervised Metric Learning via Dimensionality Reduction of Text Embeddings using Transformer Encoders and Attention Mechanisms
  
  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, 12, 77536-77554. https://doi.org/10.1109/access.2024.3403991
• The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation
  
  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
• Editorial: New challenges and future perspectives in cognitive neuroscience
  
  Frantzidis, C. A., Peristeri, E., Andreou, M., & Cristea, A. I. (2024). Editorial: New challenges and future perspectives in cognitive neuroscience. Frontiers in Human Neuroscience, 18, Article 1390788. https://doi.org/10.3389/fnhum.2024.1390788
• Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features
  
  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
• Sim-GAIL: A generative adversarial imitation learning approach of student modelling for intelligent tutoring systems
  
  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
• Using deep learning to analyze the psychological effects of COVID-19
  
  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
• Predicting STC Customers' Satisfaction Using Twitter
  
  Almuqren, L., & Cristea, A. I. (2023). Predicting STC Customers’ Satisfaction Using Twitter. IEEE Transactions on Computational Social Systems, 10(1), 204-210. https://doi.org/10.1109/tcss.2021.3135719
• Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation
  
  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
• Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges
  
  Pereira, F. D., Fonseca, S. C., Wiktor, S., Oliveira, D. B., Cristea, A. I., Benedict, A., Fallahian, M., Dorodchi, M., Carvalho, L. S., Mello, R. F., & 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
• Gamification suffers from the novelty effect but benefits from the familiarization effect: Findings from a longitudinal study
  
  Rodrigues, L., Pereira, F. D., Toda, A. M., Palomino, P. T., Pessoa, M., Carvalho, L. S. G., Fernandes, D., Oliveira, E. H., Cristea, A. I., & Isotani, S. (2022). Gamification suffers from the novelty effect but benefits from the familiarization effect: Findings from a longitudinal study. International Journal of Educational Technology in Higher Education, 19(1). https://doi.org/10.1186/s41239-021-00314-6
• Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs
  
  Alshehri, M., Alamri, A., Cristea, A. I., & Stewart, C. D. (2021). Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs. International Journal of Artificial Intelligence in Education, 31(2), 215-233. https://doi.org/10.1007/s40593-021-00246-2
• AraCust: a Saudi Telecom Tweets corpus for sentiment analysis
  
  Almuqren, L., & Cristea, A. (2021). AraCust: a Saudi Telecom Tweets corpus for sentiment analysis. PeerJ Computer Science, 7, Article e510. https://doi.org/10.7717/peerj-cs.510
• An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
  
  Almuqren, L., Alrayes, F. S., & Cristea, A. I. (2021). An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach. Future Internet, 13(7), Article 175. https://doi.org/10.3390/fi13070175
• Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations
  
  Aljohani, T., & Cristea, A. I. (2021). Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations. Frontiers in Research Metrics and Analytics, 6, Article 673928. https://doi.org/10.3389/frma.2021.673928
• Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model
  
  Pereira, F. D., Fonseca, S. C., Oliveira, E. H., Cristea, A. I., Bellhauser, H., Rodrigues, L., Oliveira, D. B., Isotani, S., & Carvalho, L. S. (2021). Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model. IEEE Access, 9, 117097-117119. https://doi.org/10.1109/access.2021.3105956
• Using learning analytics in the Amazonas: understanding students’ behaviour in introductory programming
  
  Pereira, F. D., Oliveira, E. H., Oliveira, D., Cristea, A. I., Carvalho, L. S., Fonseca, S., Toda, A., & Isotani, S. (2020). Using learning analytics in the Amazonas: understanding students’ behaviour in introductory programming. British Journal of Educational Technology, 51(4), 955-972. https://doi.org/10.1111/bjet.12953
• Investigating users’ experience on social media ads: perceptions of young users
  
  Al Qudah, D. A., Al-Shboul, B., Al-Zoubi, A., Al-Sayyed, R., & Cristea, A. I. (2020). Investigating users’ experience on social media ads: perceptions of young users. Heliyon, 6(7), Article e04378. https://doi.org/10.1016/j.heliyon.2020.e04378
• Deep learning for early performance prediction of introductory programming students: a comparative and explanatory study
  
  Pereira, F. D., Fonseca, S. C., Oliveira, E. H., Oliveira, D. B., Cristea, A. I., & Carvalho, L. S. (2020). Deep learning for early performance prediction of introductory programming students: a comparative and explanatory study. Revista Brasileira De InformaÌ Tica Na educação, 28, 723-749. https://doi.org/10.5753/rbie.2020.28.0.723
• How to Gamify learning Systems? An Experience Report using the Design Sprint Method and a Taxonomy for Gamification Elements in Education
  
  Toda, A. M., Palomino, P. T., Oliveira, W., Rodrigues, L., Klock, A. C., Gasparini, I., Cristea, A. I., & Isotani, S. (2019). How to Gamify learning Systems? An Experience Report using the Design Sprint Method and a Taxonomy for Gamification Elements in Education. Journal of Educational Technology and Society, 22(3), 47-60.
• Analysing Gamification Elements in Educational Environments Using an Existing Gamification Taxonomy
  
  Toda, A. M., Klock, A. C., Oliveira, W., Palomino, P. T., Rodrigues, L., Shi, L., Bittencourt, I., Gasparini, I., Isotani, S., & 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
• Building and evaluating resources for sentiment analysis in the Greek language
  
  Tsakalidis, A., Papadopoulos, S., Voskaki, R., Ioannidou, K., Boididou, C., Cristea, A., Liakata, M., & Kompatsiaris, Y. (2018). Building and evaluating resources for sentiment analysis in the Greek language. Language Resources and Evaluation, 52(4), 1021-1044. https://doi.org/10.1007/s10579-018-9420-4
• Lightweight adaptive E-Advertising Model
  
  Qaffas, A. A., Cristea, A., & Mead, M. A. (2018). Lightweight adaptive E-Advertising Model. Journal of Universal Computer Science, 24(7), 935-974.
• Cognitive agents and machine learning by example : representation with conceptual graphs
  
  Gkiokas, A., & Cristea, A. (2018). Cognitive agents and machine learning by example : representation with conceptual graphs. Computational Intelligence, 34(2), 603-634. https://doi.org/10.1111/coin.12167
• Do personalisation and emotions affect the use of cancer-related websites?
  
  Hadzidedic Bazdarevic, S., & Cristea, A. I. (2017). Do personalisation and emotions affect the use of cancer-related websites?. Online Information Review, 41(1), 102-118. https://doi.org/10.1108/oir-09-2015-0305
• How emotions stimulate people affected by cancer to use personalised health websites
  
  Hadzidedic Bazdarevic, S., & Cristea, A. I. (2015). How emotions stimulate people affected by cancer to use personalised health websites. Knowledge Management & E-Learning., 7(4), 658-676.
• Learners Thrive When Using Multifaceted Open Social Learner Models
  
  Shi, L., & Cristea, A. I. (2015). Learners Thrive When Using Multifaceted Open Social Learner Models. IEEE MultiMedia, 23(1), 36-47. https://doi.org/10.1109/mmul.2015.93
• Predicting elections for multiple countries using Twitter and polls
  
  Tsakalidis, A., Papadopoulos, S., Cristea, A., & Kompatsiaris, Y. (2015). Predicting elections for multiple countries using Twitter and polls. IEEE Intelligent Systems, 30(2), 10-17. https://doi.org/10.1109/mis.2015.17
• The ethical and social implications of personalization technologies for e-learning
  
  Ashman, H., Brailsford, T., Cristea, A., Sheng, Q. Z., Stewart, C., Toms, E. G., & Wade, V. (2014). The ethical and social implications of personalization technologies for e-learning. Information and Management, 51(6), 819-832. https://doi.org/10.1016/j.im.2014.04.003
• Introduction to the special issue of the journal World Wide Web: Social media preservation and applications
  
  Cristea, A., Katsaros, D., & Manolopoulos, Y. (2014). Introduction to the special issue of the journal World Wide Web: Social media preservation and applications. World Wide Web, 17(4), 691-693. https://doi.org/10.1007/s11280-014-0282-4
• Entropy-based automated wrapper generation for weblog data extraction
  
  Gkotsis, G., Stepanyan, K., Cristea, A., & Joy, M. (2013). Entropy-based automated wrapper generation for weblog data extraction. World Wide Web, 17(4), 827-846. https://doi.org/10.1007/s11280-013-0269-6

Working Paper

• Effect of emotions and personalisation on cancer website reuse intentions
  
  Hadzidedic, S., Cristea, A., & Watson, D. (2023). Effect of emotions and personalisation on cancer website reuse intentions. arxiv.org. https://doi.org/10.48550/arXiv.2301.00886