Teaching and Learning Strategies for Introductory Programming in University Courses

Student success in introductory programming courses is a widespread concern. Success does not depend only on the individual characteristics of each student, but there is a set of factors that can interfere with academic performance. Especially in computer science courses, failure has a strong negative influence, since in many cases students are demotivated and, in many cases, they even drop out of the course. In this work, we present a collection of strategies, included in an application, with the objective of helping teacher and student in the teaching and initial learning process of programming. Early identification of potential problems and immediate response is a fundamental aspect to avoid student failure and reduce dropout rates. This paper also describes 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. Finally, we present the results obtained in the use of our set of strategies applied to our study group with particular characteristics in introductory programming course (Introduction to Programming), lectured to the first year, first semester, students of the CS course at the Polytechnic of Guarda (IPG), Portugal.