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Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34
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Item Help To Programming: uma Ferramenta para o Ensino e Aprendizagem da Programação(Grupo GRIAL, 2021-02-11) Figueiredo, J.; García-Peñalvo, F. J.Existe a ideia generalizada de que o ensino e aprendizagem da pro-gramação é difícil. Desde que surgiram as linguagens de programação que este tema é estudado e investigado por todos os que se dedicam a esta área das ciên-cias da computação. Os conceitos básicos da programação fazem parte de mui-tos cursos de ensino superior nas mais diversas áreas do conhecimento. As difi-culdades no ensino e aprendizagem da programação refletem-se não só nas altas taxas de reprovação, mas também, e talvez a mais preocupante, nas altas per-centagens de abandono, na falta de motivação e de interesse dos alunos. Neste trabalho apresentamos algumas das razões nas dificuldades do ensino e na aprendizagem da programação. Descrevemos um conjunto de estratégias de en-sino e aprendizagem de introdução à programação de modo a reduzir este pro-blema. Este conjunto de estratégias é auxiliado por uma aplicação HTPro-gramming que nos permitirá acompanhar em pormenor o desenvolvimento de cada aluno, nas diferentes fases do processo de aprendizagem. À medida que o aluno constrói o seu perfil de aprendizagem será possível aplicar um modelo preditivo de sucesso ou insucesso. É possível ao aluno melhorar aspetos especí-ficos do seu perfil de aprendizagem e ao professor ter um conhecimento preciso do nível de conhecimento de cada aluno, e intervir rapidamente se necessário. Os resultados obtidos são encorajadores. Os alunos estão mais interessados, motivados e envolvidos no processo de ensino e aprendizagem e sentem-se mais confiantes com a possibilidade de aprender e praticar ao seu próprio ritmo, sem o receio de errar.Item Teaching and learning strategies of programming for university courses(ACM, 2019-10-16) Figueiredo, J.; García-Peñalvo, F. J.It is consensual to consider teaching and learning programming difficult. A lot of work, dedication, and motivation are required for teachers and students. Since the first programming languages have emerged, the problem of teaching and learning programming is studied and investigated. The theme is very serious, not only for the important concepts underlying the course but also for the lack of motivation, failure, and abandonment that such frustration may imply in the student. Immediate response and constant monitoring of students' activities and problems are important. With this work, it is our goal to improve student achievement in courses where programming is essential. We want each student to be able to improve and deepen their programming skills, performing a set of exercises appropriate and worked for each student and situation. We intend to build a dynamic learning model of constant evaluation, build the profile of the student. The student profile will be analyzed by our predictive model, which in case of prediction of failure, the student will have more careful attention. Predict the student's failure with anticipation and act with specific activities, giving the student the possibility of training and practicing the activities with difficulties. With this model, we try to improve the skills of each student in programming.Item Predicting Student Failure in an Introductory Programming Course with Multiple Back-Propagation(ACM, 2019-10-16) Figueiredo, J.; Lopes, N.; García-Peñalvo, F. J.One of the most challenging tasks in computer science and similar courses consists of both teaching and learning computer programming. Usually this requires a great deal of work, dedication, and motivation from both teachers and students. Accordingly, ever since the first programming languages emerged, the problems inherent to programming teaching and learning have been studied and investigated. The theme is very serious, not only for the important concepts underlying computer science courses but also for reducing the lack of motivation, failure, and abandonment that result from students frustration. Therefore, early identification of potential problems and immediate response is a fundamental aspect to avoid student’s failure and reduce dropout rates. In this paper, we propose a machine-learning (neural network) predictive model of student failure based on the student profile, which is built throughout programming classes by continuously monitoring and evaluating student activities. The resulting model allows teachers to early identify students that are more likely to fail, allowing them to devote more time to those students and try novel strategies to improve their programming skills.