PurposeThe paper aims to present a new two stage local causal learning algorithm – HEISA. In the first stage, the algorithm discoveries the subset of features that better explains a target variable. During the second stage, computes the causal effect, using partial correlation, of each feature of the selected subset. Using this new algorithm, the study aims to identify the actions that lead a student succeed or failure in a course.Design/methodology/approachThe paper presents a brief review of main concepts used in this study: Causal Learning and Causal effects. The paper also discusses the results of applying the algorithm in education data set. Data used in this study was extracted from the log of actions of a Learning Management System, Moodle. These actions represent the behavior of 229 engineering students that take Algorithm and Data Structure course offered in a blended model.FindingsThe algorithm proposed in the paper identifies that features with weak relevance to a target may become relevant when computing the direct effect.Research limitations/implicationsThe algorithm needs to be improved to automatically discard attributes that are under a specific threshold of direct effect. Researchers are also encouraged to test the proposed propositions further.Practical implicationsThe algorithm presented in this paper can be used to identify the mostly relevant features given a classification task.Originality/valueThis paper computes the direct effect of a selected subset of features in a target variable to evaluate if a variable in this subset is really a cause of the target or if it is a spurious correlation.
Understanding the reasons that leads students to succeed during their course is a challenge for every Institution of Education, independently of the modality of teaching and learning adopted. In this paper we use the theory of Causal Inference for analyzing the main factors that causes the success, or failure, of an engineering student enrolled in an online course of Algorithm. We used data extracted from the Learning Management System Moodle and, after preprocessing the dataset, analyzed the actions performed by the students during the six months (20 weeks) that the online course lasted. We concluded that before submitting an evaluation activity to be assessed, it is important that students analyze the problem thoroughly. Students that took a little bit longer to submit their work got more chances to be approved.
Feature selection is a process of the data preprocessing task in business intelligence (BI), analytics, and data mining that urges for new methods that can handle with high dimensionality. One alternative that have been researched to deal with the curse of dimensionality is causal feature selection. Causal feature selection is not based on correlation, but the causality relationship among variables. The main goal of this chapter is to present, based on the issues identified on other methods, a new strategy that considers attributes beyond those that compounds the Markov blanket of a node and calculate the causal effect to ensure the causality relationship.
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