Study of causal relationships among indicators of academic performance in the freshman year of Computer Science by using modal implicative analysis

Abstract

The Statistical Implicative Analysis (SIA) is a method of non-symmetrical analysis of data whose main objective is the structuring of data, interrelating individuals and variables, the extraction of inductive rules among the variables and from their contingency, the explanation and in consequence a certain prediction in different knowledge branches. The SIA holds two techniques of analysis of data, the cohesive analysis and the implicative analysis, along with the classificatory or similarity analysis. The objective of the present research is to reveal possible similarity, propensity and cohesion relationships among the academic results of students coming from high schools that enter to Computer Science career and the results that they show in undergraduate courses related to Mathematics and Programming, which they receive in the first year of the mentioned career. The gathered data were processed using the software SIASI for modal data.