Outputs (44)

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., …Palomino, P. (2019). A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation. . https://doi.org/10.1109/icalt.2019.00028

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.

Planning Gamification Strategies based on User Characteristics and DM: A Gender-based Case Study (2019)
Presentation / Conference Contribution
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 (438-443)

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.

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.

On the need for fine-grained analysis of Gender versus Commenting Behaviour in MOOCs (2018)
Presentation / Conference Contribution
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 (73-77). https://doi.org/10.1145/3234825.3234833

Stereotyping is the first type of adaptation ever proposed. However, the early systems have never dealt with the numbers of learners that current Massive Open Online Courses (MOOCs) provide. Thus, the umbrella question that this work tackles is if le... Read More about On the need for fine-grained analysis of Gender versus Commenting Behaviour in MOOCs.

An intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System (2018)
Presentation / Conference Contribution
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 (57-61). https://doi.org/10.1145/3234825.3234835

Adaptive Educational Hypermedia (AEH) offers more advanced personalisation and customisation features to the field of e-learning compared to the outdated static systems (where every learner is given the same set of learning materials). AEH can improv... Read More about An intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System.

Connecting Targets to Tweets: Semantic Attention-based Model for Target-Specific stance Detection (2017)
Presentation / Conference Contribution
Zhou, Y., Cristea, A. I., & Shi, L. (2017, October). Connecting Targets to Tweets: Semantic Attention-based Model for Target-Specific stance Detection. Presented at Web Information Systems Engineering – WISE 2017, 18th International Conference, Moscow

Understanding what people say and really mean in tweets is still a wide open research question. In particular, understanding the stance of a tweet, which is determined not only by its content, but also by the given target, is a very recent research a... Read More about Connecting Targets to Tweets: Semantic Attention-based Model for Target-Specific stance Detection.