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O presente trabalho explora a hipótese de predição da nota ENEM em ciências humanas a partir de dados pedagógicos oriundos de notas de avaliações escolares de estudantes de ensino médio de um colégio particular em São Paulo. A partir da análise, foi possível a predição da nota ENEM de ciências humanas no decurso do 3º ano do ensino médio com uma correlação (r de Pearson) de 60,2% e RMSE de 39,7 pontos na escala ENEM. O modelo apontou, ainda, conteúdos relevantes fortemente correlacionados com o desempenho ENEM já desde o 1º ano do ensino médio.

Protocol for an umbrella review of systematic reviews at CHI.

Gamification has become a significant direction in designing technologies, services, products, organizational structures, and any human activities towards being more game-like and consequently being more engaging and motivating. Albeit its success, research indicates that personal differences exist with regards to susceptibility to gamification at large as well as to different types of gamification designs. As a response, models and measurement instruments of user types when it comes to gamification have been developed. One of the most discussed related instruments is the Hexad user types scale. However, there has been paucity of research related to the validity and reliability of the Hexad instrument in general but also of its different formulations and language versions. To face this gap, our study focused on analyzing the psychometric properties of the Hexad scale in Brazilian Portuguese by conducting two confirmatory factor analyses and two multi-group confirmatory factor analyses. The survey was answered by 421 Brazilian respondents (52% self-reported women, 47% self-reported men, 0.5% preferred not to provide their gender, and 0.5% checked the option “other”), from the five Brazilian regions (23 different states and the Federal District), and aged between 10 and 60 years old. Findings support the structural validity of the scale as an oblique model and indicate opportunities for small improvements. Further research, both at academy and practice, may use this study as the source of measurement of user types related to gamification (in Brazilian Portuguese), as well as, as a theoretical and practical source for further studies discussing personalized gamification.

This paper presents a systematic literature review (SLR) that investigates group formation, as a first step towards automated group formation for collaborative learning. Out of 105 papers selected for review, after using specific selection and a quality assessment method, a final list of 21 relevant studies was selected for analysis. The review revealed the current state of the art in group formation.

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. Nevertheless, there is still a high level of dropout noticeable in introductory programming classes. In this sense, the objective of this work is to develop and validate a method for predicting student dropout using data from the first two weeks of study, to allow for early intervention. Instead of the classical questionnaire-based method, we opted for a non-subjective, data-driven approach. However, such approaches are known to suffer from a potential overload of factors, which may not all be relevant to the prediction task. As a result, we reached a very promising 80% of accuracy, and performed explicit extraction of the main factors leading to student dropout.

© Springer Nature Switzerland AG 2019. Reflective writing is widely acknowledged to be one of the most effective learning activities for promoting students’ self-reflection and critical thinking. However, manually assessing and giving feedback on reflective writing is time consuming, and known to be challenging for educators. There is little work investigating the potential of automated analysis of reflective writing, and even less on machine learning approaches which offer potential advantages over rule-based approaches. This study reports progress in developing a machine learning approach for the binary classification of pharmacy students’ reflective statements about their work placements. Four common statistical classifiers were trained on a corpus of 301 statements, using emotional, cognitive and linguistic features from the Linguistic Inquiry and Word Count (LIWC) analysis, in combination with affective and rhetorical features from the Academic Writing Analytics (AWA) platform. The results showed that the Random-forest algorithm performed well (F-score = 0.799) and that AWA features, such as emotional and reflective rhetorical moves, improved performance.