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Outputs (11)

MOOCSent: a Sentiment Predictor for Massive Open Online Courses (2021)
Conference Proceeding
Alsheri, M. A., Alrajhi, L. M., Alamri, A., & Cristea, A. I. (2021). MOOCSent: a Sentiment Predictor for Massive Open Online Courses.

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

Forum-based Prediction of Certification in Massive Open Online Courses (2021)
Conference Proceeding
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.

Early Dropout Prediction for Programming Courses Supported by Online Judges (2019)
Conference Proceeding
Pereira, F. D., Oliveira, E., Cristea, A., Fernandes, D., Silva, L., Aguiar, G., …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.), . https://doi.org/10.1007/978-3-030-23207-8_13

Many educational institutions have been using online judges in programming classes, amongst others, to provide faster feedback for students and to reduce the teacher’s workload. There is some evidence that online judges also help in reducing dropout.... Read More about Early Dropout Prediction for Programming Courses Supported by Online Judges.

Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses (2018)
Conference Proceeding
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)
Conference Proceeding
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)
Conference Proceeding
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)
Conference Proceeding
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)
Conference Proceeding
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.

A large-scale category-based evaluation of a visual language for adaptive hypermedia (2018)
Conference Proceeding
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 (94-98). https://doi.org/10.1145/3234825.3234834

Adaptive Hypermedia (AH) provides a personalised and customised approach, enhancing the usability of hypermedia, by building a model of various qualities of a user and applying this information to adapt the content and the navigation to their require... Read More about A large-scale category-based evaluation of a visual language for adaptive hypermedia.

On the need for fine-grained analysis of Gender versus Commenting Behaviour in MOOCs (2018)
Conference Proceeding
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.