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Outputs (43)

Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation (2023)
Journal Article
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2023). Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation. AI open, 4, 19-32. https://doi.org/10.1016/j.aiopen.2023.05.001

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence... Read More about Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation.

Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews (2022)
Book Chapter
Xiao, C., Shi, L., Cristea, A., Li, Z., & Pan, Z. (2022). Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews. In M. Rodrigo, N. Matsuda, A. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education (294-306). Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_24

Online course reviews have been an essential way in which course providers could get insights into students’ perceptions about the course quality, especially in the context of massive open online courses (MOOCs), where it is hard for both parties to... Read More about Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews.

Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data (2022)
Book Chapter
Drousiotis, E., Pentaliotis, P., Shi, L., & Cristea, A. I. (2022). Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data. In Artificial Intelligence in Education (256-268). Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_21

Along with the exponential increase of students enrolling in MOOCs [26] arises the problem of a high student dropout rate. Researchers worldwide are interested in predicting whether students will drop out of MOOCs to prevent it. This study explores a... Read More about Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data.

SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour (2022)
Book Chapter
Li, Z., Shi, L., Cristea, A., Zhou, Y., Xiao, C., & Pan, Z. (2022). SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour. In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (348-351). Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_67

Lacking behavioural data between students and an Intelligent Tutoring System (ITS) has been an obstacle for improving its personalisation capability. One feasible solution is to train “sim students”, who simulate real students’ behaviour in the ITS.... Read More about SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour.

Tailored gamification in education: A literature review and future agenda (2022)
Journal Article
Oliveira, W., Hamari, J., Shi, L., Toda, A. M., Rodrigues, L., Palomino, P. T., & Isotani, S. (2023). Tailored gamification in education: A literature review and future agenda. Education and Information Technologies, 28(1), 373-406. https://doi.org/10.1007/s10639-022-11122-4

Gamification has been widely used to design better educational systems aiming to increase students’ concentration, motivation, engagement, flow experience, and others positive experiences. With advances in research on gamification in education, over... Read More about Tailored gamification in education: A literature review and future agenda.

Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods (2022)
Book Chapter
Drousiotis, E., Shi, L., Spirakis, P. G., & Maskell, S. (2022). Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods. In L. Iliadis, C. Jayne, A. Tefas, & E. Pimenidis (Eds.), Engineering Applications of Neural Networks: 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings (90-102). Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_8

Decision Forests have attracted the academic community’s interest mainly due to their simplicity and transparency. This paper proposes two novel decision forest building techniques, called Maximal Information Coefficient Forest (MICF) and Pearson’s C... Read More about Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods.

AI‐driven user aesthetics preference prediction for UI layouts via deep convolutional neural networks (2022)
Journal Article
Xing, B., Cao, H., Shi, L., Si, H., & Zhao, L. (2022). AI‐driven user aesthetics preference prediction for UI layouts via deep convolutional neural networks. Cognitive Computation and Systems, 4(3), 250-264. https://doi.org/10.1049/ccs2.12055

Leveraging the power of computational methods, AI can perform effective strategies in intelligent design. Researchers are pushing the boundaries of AI, developing computational systems to solve complex questions. The authors investigate the associati... Read More about AI‐driven user aesthetics preference prediction for UI layouts via deep convolutional neural networks.

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.

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 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., …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.

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.

Learners Thrive When Using Multifaceted Open Social Learner Models (2015)
Journal Article
Shi, L., & Cristea, A. I. (2015). Learners Thrive When Using Multifaceted Open Social Learner Models. IEEE MultiMedia, 23(1), 36-47. https://doi.org/10.1109/mmul.2015.93

This article explores open social learner modeling (OSLM)-a social extension of open learner modeling (OLM). A specific implementation of this approach is presented by which learners' self-direction and self-determination in a social e-learning conte... Read More about Learners Thrive When Using Multifaceted Open Social Learner Models.

How is Learning Fluctuating? FutureLearn MOOCs Fine-grained Temporal Analysis and Feedback to Teachers and Designers
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.

Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses
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.

A Taxonomy of Game Elements for Gamification in Educational Contexts: Proposal and Evaluation
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.

Social Interactions Clustering MOOC Students: An Exploratory Study
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.

Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students
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.