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Learning to program is a difficult process for novice students and challenging for teaching methods. In this paper we present an automatic evaluation platform and a methodology to support the teaching/learning process in computer programming introductory courses for engineering students at the Universidad de Concepción. This platform uses static/dynamic techniques and applies comprehension /analysis evaluation enabling personalized feedback to students. This work includes the criteria used to create didactic problems sequences and effective ways to apply them through automatic evaluation. The results obtained during six years of application show that automatic evaluation along with the proposed methodology improves student's motivation, performance and self-efficacy. The methodology used to study the effectiveness of the platform includes qualitative and quantitative analysis. The qualitative aspects are extracted by observing student behavior during the learning process, whereas the quantitative analysis is based on student data registered by the platform. In order to gather quantitative information, we applied different experiments based on control and experimental student groups. [ABSTRACT FROM AUTHOR]

Learning to program is a difficult process for novice students and challenging for teaching methods. In this paper we present an automatic evaluation platform and a methodology to support the teaching/learning process in computer programming introductory courses for engineering students at the Universidad de Concepción. This platform uses static/dynamic techniques and applies comprehension /analysis evaluation enabling personalized feedback to students. This work includes the criteria used to create didactic problems sequences and effective ways to apply them through automatic evaluation. The results obtained during six years of application show that automatic evaluation along with the proposed methodology improves student's motivation, performance and self-efficacy. The methodology used to study the effectiveness of the platform includes qualitative and quantitative analysis. The qualitative aspects are extracted by observing student behavior during the learning process, whereas the quantitative analysis is based on student data registered by the platform. In order to gather quantitative information, we applied different experiments based on control and experimental student groups. [ABSTRACT FROM AUTHOR]