Outputs (13)

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, October). Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses. Presented at 27th International Conference on Information Systems Development (ISD2018)., Lund, Sweden

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.

Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses (2018)
Presentation / Conference Contribution
Shi, L., & Cristea, A. (2018, October). Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses. Presented at 27th International Conference on Information Systems Development (ISD2018)., Lund

Big data and analytics for educational information systems, despite having gained researchers’ attention, are still in their infancy and will take years to mature. Massive open online courses (MOOCs), which record learner-computer interactions, bring... Read More about Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics 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, 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.

In-depth Exploration of Engagement Patterns in MOOCs (2018)
Presentation / Conference Contribution
Lei, S., & Cristea, A. (2018, December). In-depth Exploration of Engagement Patterns in MOOCs. Presented at Web Information Systems Engineering (WISE 2018), Dubai

With the advent of ‘big data’, various new methods have been proposed, to explore data in several domains. In the domain of learning (and e-learning, in particular), the outcomes lag somewhat behind. This is not unexpected, as e-learning has the addi... Read More about In-depth Exploration of Engagement Patterns in MOOCs.

Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum (2018)
Presentation / Conference Contribution
Tsakalidis, A., Aletras, N., Cristea, A., & Liakata, M. (2018, December). Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum. Presented at 2018 ACM Conference on Information and Knowledge Management (CIKM’18), Torino

Modelling user voting intention in social media is an important research area, with applications in analysing electorate behaviour, online political campaigning and advertising. Previous approaches mainly focus on predicting national general election... Read More about Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum.

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, August). Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses. Presented at 27th International Conference on Information Systems Development (ISD2018)., Lund, Sweden

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, December). How is Learning Fluctuating? FutureLearn MOOCs Fine-grained Temporal Analysis and Feedback to Teachers and Designers. Presented at 27th International Conference on Information Systems Development (ISD2018), Lund, Sweden

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.

Lightweight adaptive E-Advertising Model (2018)
Journal Article
Qaffas, A. A., Cristea, A., & Mead, M. A. (2018). Lightweight adaptive E-Advertising Model. Journal of Universal Computer Science, 24(7), 935-974

Adaptive online advertising is a rapidly expanding marketing tool that delivers personalised messages and adverts to Internet users. At a time when the Internet is burgeoning, many websites use an adaptation process to tailor their advertisements, ho... Read More about Lightweight adaptive E-Advertising Model.

Building and evaluating resources for sentiment analysis in the Greek language (2018)
Journal Article
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

Sentiment lexicons and word embeddings constitute well-established sources of information for sentiment analysis in online social media. Although their effectiveness has been demonstrated in state-of-the-art sentiment analysis and related tasks in th... Read More about Building and evaluating resources for sentiment analysis in the Greek language.

A large-scale category-based evaluation of a visual language for adaptive hypermedia (2018)
Presentation / Conference Contribution
Khan, J., Cristea, A., & Alamri, A. (2018, June). A large-scale category-based evaluation of a visual language for adaptive hypermedia. Presented at 3rd International Conference on Information and Education Innovations (ICIEI'18), London

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)
Presentation / Conference Contribution
Alshehri, M., Foss, J., Cristea, A. I., Kayama, M., Shi, L., Alamri, A., & Tsakalidis, A. (2018, June). On the need for fine-grained analysis of Gender versus Commenting Behaviour in MOOCs. Presented at 3rd International Conference on Information and Education Innovations (ICIEI'18), London

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, June). An intuitive Authoring System for a Personalised, Social, Gamified, Visualisation-supporting e-learning System. Presented at 3rd International Conference on Information and Education Innovations (ICIEI'18), London

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.

Cognitive agents and machine learning by example : representation with conceptual graphs (2018)
Journal Article
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

As machine learning (ML) and artificial intelligence progress, more complex tasks can be addressed, quite often by cascading or combining existing models and technologies, known as the bottom‐up design. Some of those tasks are addressed by agents, wh... Read More about Cognitive agents and machine learning by example : representation with conceptual graphs.