Teaching students to use Decision trees (Dt) for unstructured data

Abstract

The research aims to analyze the importance of teaching to use unstructured data methods that students generate from the learning activities and examine the relative efficiency of the decision trees within load conditions and self-efficacy of each learner. The present research collected the data using a questionnaire to analyze self-efficacy and cognitive load among students. The sample included 150 students divided into two groups. The research revealed no significant differences in self-efficacy between the two groups participants (F = 0.01, p> 0.05). According to the results, no differences were identified between the students who worked with unstructured data using decision trees and those students who analyzed the unstructured data using association rules. The research uses an independent t-test for the analysis of cognitive load within the academic environment. No significant differences were detected concerning cognitive load between the two groups of participants. Keywords: unstructured data, decision trees, association rules, self-efficacy, cognitive load, SDGs.