A Tool for Assisted Correction of Programming Exercises in Java Based in Computational Reflection

INTRODUCTION: This work reports on the creation and use of a tool to verify compliance in java programming exercises. The solution is based on the hypothesis that computational reflection can provide a way to automatically assess the programing competences of students. The work reflects the concern to make students learning a programming language have practical activities in parallel to what they learn in theoretical classes. OBJECTIVE: Attesting the effectiveness of using computational reflection to automatically correct programming exercises. Provide the teacher with a tool to support the follow-up of practical activities. Provide students with immediate feedback on their learning, so as to encourage them to behave more autonomously. METHOD: A case study was carried out with two classes of a computer sciencecourse. They answered five practical programming exercices and their responses for each activity were collected in source code format, which were used as the basis of solutions, totaling 100 responses.A comparative analysis was made between the notes obtained through CodeTeacher and the notes assigned by a group of  teachers. RESULTS: Comparing the expected notes and the actual notes, the automatic correction obtained an accuracy of 90.17%. CONCLUSION: The use of computational reflection techniques for assisted correction in programming classes can bring beneficial result. Teachers can optimize their work and have performance reports of their students. Students can also be benefited by having an immediate feedback, so they can perceive themselves capable of achieving the learning objectives defined by the teacher

Predicting and Improving Performance on Introductory Programming Courses (CS1)

This thesis describes a longitudinal study on factors which predict academic success in introductory programming at undergraduate level, including the development of these factors into a fully automated web based system (which predicts students who are at risk of not succeeding early in the introductory programming module) and interventions to address attrition rates on introductory programming courses (CS1). Numerous studies have developed models for predicting success in CS1, however there is little evidence on their ability to generalise or on their use beyond early investigations. In addition, they are seldom followed up with interventions, after struggling students have been identified. The approach overcomes this by providing a web-based real time system, with a prediction model at its core that has been longitudinally developed and revalidated, with recommendations for interventions which educators could implement to support struggling students that have been identified. This thesis makes five fundamental contributions. The first is a revalidation of a prediction model named PreSS. The second contribution is the development of a web-based, real time implementation of the PreSS model, named PreSS#. The third contribution is a large longitudinal, multi-variate, multi-institutional study identifying predictors of performance and analysing machine learning techniques (including deep learning and convolutional neural networks) to further develop the PreSS model. This resulted in a prediction model with approximately 71% accuracy, and over 80% sensitivity, using data from 11 institutions with a sample size of 692 students. The fourth contribution is a study on insights on gender differences in CS1; identifying psychological, background, and performance differences between male and female students to better inform the prediction model and the interventions. The final, fifth contribution, is the development of two interventions that can be implemented early in CS1, once identified by PreSS# to potentially improve student outcomes. The work described in this thesis builds substantially on earlier work, providing valid and reliable insights on gender differences, potential interventions to improve performance and an unsurpassed, generalizable prediction model, developed into a real time web-based system