ne-Course for Learning Programming

Difficulties in learning programming are a constant concern in engineering courses. In many research studies involving the learning programming must of the solutions presented, from the beginning of the first programming languages, was to apply different type of problems analysis. Literature relating to the understanding of nature of learning programming skills has been focused explicitly on the teaching methodology and few of them focus on abilities, characteristics and knowledge acquired over the life cycle of learning programming in each student. Most of the students enrolled in engineering courses, where programming is a crucial competence, never had the opportunity to develop skills of computational thinking. In this paper, we focus our work on the learning programming developing and applying a set of exercises where students with more difficulties can express and develop their skills in computational thinking. In order to understand some programming students difficulties we have create a set of exercises, and apply it to a pre-programming course, that allows teachers to understand how students analyse and comprehend aspects such as visualization, spatial interpretation and physical manipulation. This paper also reports on results obtained from a class experiment where Memory Transfer Language was used by students to learn programming. All the exercises must be resolved without any type of technology, designed as a ne-course (no electronic course) for learning programming

ne-Course for Learning Programming

Difficulties in learning programming are a constant concern in engineering courses. In many research studies involving the learning programming must of the solutions presented, from the beginning of the first programming languages, was to apply different type of problems analysis. Literature relating to the understanding of nature of learning programming skills has been focused explicitly on the teaching methodology and few of them focus on abilities, characteristics and knowledge acquired over the life cycle of learning programming in each student. Most of the students enrolled in engineering courses, where programming is a crucial competence, never had the opportunity to develop skills of computational thinking. In this paper, we focus our work on the learning programming developing and applying a set of exercises where students with more difficulties can express and develop their skills in computational thinking. In order to understand some programming students difficulties we have create a set of exercises, and apply it to a pre-programming course, that allows teachers to understand how students analyse and comprehend aspects such as visualization, spatial interpretation and physical manipulation. This paper also reports on results obtained from a class experiment where Memory Transfer Language was used by students to learn programming. All the exercises must be resolved without any type of technology, designed as a ne-course (no electronic course) for learning programming

Teaching and Learning Tools for Introductory Programming in University Courses

Difficulties in teaching and learning introductory programming have been studied over the years. The students' difficulties lead to failure, lack of motivation, and abandonment of courses. The problem is more significant in computer courses, where learning programming is essential. Programming is difficult and requires a lot of work from teachers and students. Programming is a process of transforming a mental plan into a computer program. The main goal of teaching programming is for students to develop their skills to create computer programs that solve real problems. There are several factors that can be at the origin of the problem, such as the abstract concepts that programming implies; the skills needed to solve problems; the mental skills needed to decompose problems; many of the students never had the opportunity to practice computational thinking or programming; students must know the syntax, semantics, and structure of a new unnatural language in a short period of time. In this work, we present a set of strategies, included in an application, with the objective of helping teachers and students. Early identification of potential problems and prompt response is critical to preventing student failure and reducing dropout rates. This work also describes a predictive machine learning (neural network) model of student failure based on the student profile, which is built over the course of programming lessons by continuously monitoring and evaluating student activities