Skip to main content

Research Repository

Advanced Search

All Outputs (44)

Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study (2021)
Book Chapter
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 (139-144). Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_25

This study aims to explore and improve ways of handling a continuous variable dataset, in order to predict student dropout in MOOCs, by implementing various models, including the ones most successful across various domains, such as recurrent neural n... Read More about Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study.

Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours (2021)
Presentation / Conference Contribution
Alharbi, K., Cristea, A. I., Shi, L., Tymms, P., & Brown, C. (2021, June). Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours. Presented at Artificial Intelligence in Education, Utrecht

Schoolchildren's academic progress is known to be affected by the classroom environment. It is important for teachers and administrators to under-stand their pupils' status and how various factors in the classroom may affect them, as it can help them... Read More about Agent-based Classroom Environment Simulation: the Effect of Disruptive Schoolchildren’s Behaviour versus Teacher Control over Neighbours.

Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs (2021)
Journal Article
Xing, B., Si, H., Chen, J., Ye, M., & Shi, L. (2021). Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs. CCF Transactions on Pervasive Computing and Interaction, 3, 147-169. https://doi.org/10.1007/s42486-021-00064-4

Visual aesthetics is vital in determining the usability of the graphical user interface (GUI). It can strengthen the competitiveness of interactive online applications. Human aesthetic preferences for GUI are implicit and linked to various aspects of... Read More about Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs.

Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course (2020)
Presentation / Conference Contribution
Yu, J., Aduragba, O. T., Sun, Z., Black, S., Stewart, C., Shi, L., & Cristea, A. (2020, December). Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course. Presented at 2020 15th International Conference on Computer Science & Education (ICCSE), Delft, Netherlands

Social media is ubiquitous, a continuous part of our daily lives; it offers new ways of communication. This is especially crucial in education, where various online systems make use of (perceived) public or private communication, as a means to suppor... Read More about Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course.

Social Interactions Clustering MOOC Students: An Exploratory Study (2020)
Presentation / Conference Contribution
Shi, L., Cristea, A. I., Toda, A. M., Oliveira, W., Ahmad, A., Chang, M., Sampson, D. G., Huang, R., Hooshyar, D., Chen, N.-S., Kinshuk, & Pedaste, M. (2020, December). Social Interactions Clustering MOOC Students: An Exploratory Study. Presented at The 20th International Conference on Advanced Learning Technologies (ICALT), Tartu, Estonia

An exploratory study on social interactions of MOOC students in FutureLearn was conducted, to answer "how can we cluster students based on their social interactions?" Comments were categorized based on how students interacted with them, e.g., how a s... Read More about Social Interactions Clustering MOOC Students: An Exploratory Study.

Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes (2020)
Book Chapter
Shi, L., Yang, B., & Toda, A. (2020). Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes. In A. Siarheyeva, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Advances in information systems development (146-163). Springer Verlag. https://doi.org/10.1007/978-3-030-49644-9_9

This chapter demonstrates a temporal analysis in Massive Open Online Courses (MOOCs), towards identifying at-risk students through analyzing their demographical changes. At-risk students are those who tend to drop out from the MOOCs. Previous studies... Read More about Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes.

Exploring Navigation Styles in a FutureLearn MOOC (2020)
Book Chapter
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 (45-55). Springer Verlag. https://doi.org/10.1007/978-3-030-49663-0_7

This paper presents for the first time a detailed analysis of fine-grained navigation style identification in MOOCs backed by a large number of active learners. The result shows 1) whilst the sequential style is clearly in evidence, the global style... Read More about Exploring Navigation Styles in a FutureLearn MOOC.

Analysing Gamification Elements in Educational Environments Using an Existing Gamification Taxonomy (2019)
Journal Article
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

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 and analysis of gamified str... Read More about Analysing Gamification Elements in Educational Environments Using an Existing Gamification Taxonomy.

Demographical Changes of Student Subgroups in MOOCs: Towards Predicting At-Risk Students (2019)
Presentation / Conference Contribution
Yang, B., Shi, L., Toda, A., Siarheyeva, A., Barry, C., Lang, M., Linger, H., & Schneider, C. (2019, August). Demographical Changes of Student Subgroups in MOOCs: Towards Predicting At-Risk Students. Presented at 28th International Conference on Information Systems Development (ISD2019), Toulon, France

Past studies have shown that student engagement in Massive Open Online Courses (MOOCs) could be used to identify at-risk students (students with drop-out tendency). Some studies have further considered student diversity by looking into subgroup behav... Read More about Demographical Changes of Student Subgroups in MOOCs: Towards Predicting At-Risk Students.

Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students (2019)
Presentation / Conference Contribution
Shi, L., Cristea, A. I., Toda, A., Oliveira, W., Siarheyeva, A., Barry, C., Lang, M., Linger, H., & Schneide, C. (2019, August). Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students. Presented at 28th International Conference on Information Systems Development (ISD2019), Toulon, France

Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students. The advent of complex “big data” from MOOC platforms is a challenging yet rewarding opportunity to deeply understand how students are engaged in MOOCs. Past research,... Read More about Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students.

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., 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

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.

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, 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.

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, December). Can Learner Characteristics Predict Their Behaviour on MOOCs?. Presented at 10th International Conference on Education Technology and Computers - ICETC '18, Tokyo

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, 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.

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.

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.