Identifying Informatively Easy and Informatively Hard Concepts

In this article, we leverage ideas from the theory of coevolutionary computation to analyze interactions of students with problems. We introduce the idea of informatively easy or hard concepts. Our approach is different from more traditional analyses of problem difficulty such as item analysis in the sense that we consider Pareto dominance relationships within the multidimensional structure of student–problem performance data rather than average performance measures. This method allows us to uncover not just the problems on which students are struggling but also the variety of difficulties different students face. Our approach is to apply methods from the Dimension Extraction Coevolutionary Algorithm to analyze problem-solving logs of students generated when they use an online software tutoring suite for introductory computer programming called problets . The results of our analysis not only have implications for how to scale up and improve adaptive tutoring software but also have the promise of contributing to the identification of common misconceptions held by students and thus, eventually, to the construction of a concept inventory for introductory programming.

Introduction to Computer Programming Through a System of Tasks

Computer programming is a fundamental discipline in many academic programs, especially in the fields of informatics, applied mathematics, physics, and engineering. Despite its popularity, computer programming courses does not possess a widely-accepted methodology for its structure, and because of this reason, even introductory courses highly differ in their curriculum, approach, complexity, and even technical background. In this paper we propose a methodology for introductory computer programming course structure definition that is based on the concept of notion formation through a system of tasks. The approach is intended to be applied in the context of academic education, but it is also applicable in the last years of high-school courses.

Flipped Classrooms for Introductory Computer Programming Courses

Teaching and learning how to program are not easy tasks. Disapproval and dropout rates are a concern for everyone concerned with the topic. Therefore, it is necessary to look for strategies that improve the motivation of students who start a programming course, also improving success rates and decreasing dropout rates. The inverted class model, or flipped classroom, has been used in several experiments, showing very good results. The objectives of this teaching-learning technique is to change the traditional order: students have contact with new subjects before the classroom, using videos, texts or other material, as well as small online tests to check their knowledge. In this way, the face-to-face classes are reserved for discussion, doubts and application of previously acquired knowledge. The purpose of this paper is to analyze the scientific production on Flipped classrooms in introductory programming courses indexed in Elsevier’s Scopus. The sample is composed by 45 articles in total. The results obtained by bibliometric analysis showed when and where those documents are published, who are the authors and what is the focus of said articles. We also analyzed the most cited documents. We made a summary of the articles, namely in what refers to the sample size of the experiences, which programming language is used, in which universities the articles are made, which technology is used, as well as which methods are used in order to create inverted classes and which are the objectives and results of these experiences reported on the articles.. We managed to get a global view of the theme, getting a strong analysis for those who want to use flipped classrooms for teaching programming.