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Outputs (127)

Early Dropout Prediction for Programming Courses Supported by Online Judges (2019)
Presentation / Conference Contribution
Pereira, F. D., Oliveira, E., Cristea, A., Fernandes, D., Silva, L., Aguiar, G., Alamri, A., & Alshehri, M. (2019, June). Early Dropout Prediction for Programming Courses Supported by Online Judges. Presented at AIED 2019, Chicago, IL

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.

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.

Narrative for Gamification in Education: Why Should you Care? (2019)
Presentation / Conference Contribution
Toledo Palomino, P., Toda, A. M., Oliveira, W., Cristea, A. I., & Isotani, S. (2019, July). Narrative for Gamification in Education: Why Should you Care?. Presented at 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Maceio, Brazil

Gamification applied to education studies are focusing to encourage students to perform specific tasks, however many of these studies are still inconclusive about how much gamification can influence engagement. Also, the frameworks used to apply gami... Read More about Narrative for Gamification in Education: Why Should you Care?.

Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm (2019)
Presentation / Conference Contribution
Pereira, F. D., Oliveira, E. H., Fernandes, D., & Cristea, A. (2019, July). Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm. Presented at 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Maceio, Brazil

Many researchers have started extracting student behaviour by cleaning data collected from web environments and using it as features in machine learning (ML) models. Using log data collected from an online judge, we have compiled a set of successful... Read More about Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm.

How to Gamify learning Systems? An Experience Report using the Design Sprint Method and a Taxonomy for Gamification Elements in Education (2019)
Journal Article
Toda, A. M., Palomino, P. T., Oliveira, W., Rodrigues, L., Klock, A. C., Gasparini, I., Cristea, A. I., & Isotani, S. (2019). How to Gamify learning Systems? An Experience Report using the Design Sprint Method and a Taxonomy for Gamification Elements in Education. Journal of Educational Technology & Society, 22(3), 47-60

One of the main goals of gamification in educational settings is to increase student motivation and engagement. To facilitate the design of gamified educational systems, in recent years, studies have proposed various approaches (e.g., methodologies,... Read More about How to Gamify learning Systems? An Experience Report using the Design Sprint Method and a Taxonomy for Gamification Elements in Education.

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 Learners' Demographics Characteristics: Deep Learning Ensemble Architecture for Learners' Characteristics Prediction in MOOCs (2019)
Presentation / Conference Contribution
Aljohani, T., & Cristea, A. I. (2019, December). Predicting Learners' Demographics Characteristics: Deep Learning Ensemble Architecture for Learners' Characteristics Prediction in MOOCs. Presented at ICIEI 2019: 2019 The 4th International Conference on Information and Education Innovations

Author Profiling (AP), which aims to predict an author's demographics characteristics automatically by using texts written by the author, is an important mechanism for many applications, as well as highly challenging. In this research, we analyse var... Read More about Predicting Learners' Demographics Characteristics: Deep Learning Ensemble Architecture for Learners' Characteristics Prediction in MOOCs.

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.

Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation (2019)
Presentation / Conference Contribution
Tsakalidis, A., Liakata, M., Damoulas, T., & Cristea, A. I. (2018, September). Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation. Presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018 Applied Data Science Track), Dublin

Predicting mental health from smartphone and social media data on a longitudinal basis has recently attracted great interest, with very promising results being reported across many studies. Such approaches have the potential to revolutionise mental h... Read More about Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation.