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Dr Craig Stewart's Outputs (73)

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.

Unveiling crisis in globalised higher education: Artificial intelligence insights from doctoral research in EThOS (2024)
Journal Article
Montgomery, C., Stewart, C., Aduragba, O., & Poli, F. (2024). Unveiling crisis in globalised higher education: Artificial intelligence insights from doctoral research in EThOS. Higher Education Quarterly, https://doi.org/10.1111/hequ.12537

This paper seeks to illuminate new perspectives on the concept of crisis in globalised higher education (HE) by focusing on knowledge generated by doctoral research. Doctoral research is a significant part of research and knowledge building in HE, pa... Read More about Unveiling crisis in globalised higher education: Artificial intelligence insights from doctoral research in EThOS.

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.

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., …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.

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). Forum-based Prediction of Certification in Massive Open Online Courses.

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.

Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs (2021)
Journal Article
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

Since their ‘official’ emergence in 2012 (Gardner and Brooks 2018), massive open online courses (MOOCs) have been growing rapidly. They offer low-cost education for both students and content providers; however, currently there is a very low level of... Read More about Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs.

GamiCSM: relating education, culture and gamification - a link between worlds (2020)
Presentation / Conference Contribution
Toda, A., Klock, A. C. T., Palomino, P. T., Rodrigues, L., Oliveira, W., Stewart, C., …Isotani, S. (2020). GamiCSM: relating education, culture and gamification - a link between worlds. . https://doi.org/10.1145/3424953.3426490

The potential of gamification to improve users' motivation and engagement influenced many researchers and professionals to analyse its effects in educational settings. While some studies focus on adapting game elements according to demographic and be... Read More about GamiCSM: relating education, culture and gamification - a link between worlds.

Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities (2019)
Book Chapter
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 (163-173). Springer Verlag. https://doi.org/10.1007/978-3-030-22244-4_20

While Massive Open Online Course (MOOCs) platforms provide knowledge in a new and unique way, the very high number of dropouts is a significant drawback. Several features are considered to contribute towards learner attrition or lack of interest, whi... Read More about Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities.

Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses (2018)
Presentation / Conference Contribution
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

Whilst a high dropout rate is a well-known problem in MOOCs, few studies take a data-driven approach to understand the reasons of such a phenomenon, and to thus be in the position to recommend and design possible adaptive solutions to alleviate it. I... Read More about Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses.

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). 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

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.

Can Learner Characteristics Predict Their Behaviour on MOOCs? (2018)
Presentation / Conference Contribution
Cristea, A. I., Alamri, A., Alshehri, M., Kayama, M., Foss, J., Shi, L., & Stewart, C. D. (2018). Can Learner Characteristics Predict Their Behaviour on MOOCs?. In 10th International Conference on Education Technology and Computers (119-125). https://doi.org/10.1145/3290511.3290568

Stereotyping is the first type of adaptation in education ever proposed. However, the early systems have never dealt with the numbers of learners that current MOOCs provide. Thus, the umbrella question that this work tackles is if learner characteris... Read More about Can Learner Characteristics Predict Their Behaviour on MOOCs?.

Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses (2018)
Presentation / Conference Contribution
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)

Whilst a high dropout rate is a well-known problem in MOOCs, few studies take a data-driven approach to understand the reasons of such a phenomenon, and to thus be in the position to recommend and design possible adaptive solutions to alleviate it. I... Read More about Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses.

How is Learning Fluctuating? FutureLearn MOOCs Fine-grained Temporal Analysis and Feedback to Teachers and Designers (2018)
Presentation / Conference Contribution
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)

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 and Designers.

The ethical and social implications of personalization technologies for e-learning (2014)
Journal Article
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

Personalization in information systems can be considered beneficial but also ethically and socially harmful. Like many other technologies, the uptake of personalization has been rapid, with inadequate consideration given to its effects. Personalizati... Read More about The ethical and social implications of personalization technologies for e-learning.

User interface challenges for the World Wide Web (2007)
Book Chapter
Ashman, H., Brailsford, T., Burnett, G., Goulding, J., Moore, A., Stewart, C., & Truran, M. (2007). User interface challenges for the World Wide Web. In Human-Computer Interaction Handbook (559-572). CRC Press

Authoring of adaptive hypermedia (2006)
Book Chapter
Cristea, A., & Stewart, C. (2006). Authoring of adaptive hypermedia. In Advances in web-based education: Personalized learning environments (225-252). IGI Global

Goal oriented personalisation with SCORM (2005)
Presentation / Conference Contribution
Power, G., Davis, H. C., Cristea, A. I., Stewart, C., & Ashman, H. (2005). Goal oriented personalisation with SCORM.

Authoring for adaptive presentation (2003)
Presentation / Conference Contribution
Moore, A., Brailsford, T., Campus, J., Nottingham, N., Stewart, C., & Davies, P. (2003). Authoring for adaptive presentation.

HCI and the Web
Journal Article
Ashman, H., Brailsford, T., Burnett, G., Goulding, J., Moore, A., Stewart, G., & Truran, M. (online). HCI and the Web

My online teacher 2.0
Journal Article
Ghali, F., Cristea, A. I., & Stewart, C. (online). My online teacher 2.0