Classification and Regression of Learner’s Scores in Logic Environment

Abstract

This paper presents the possibility of classifying and regressing learner's scores according to different cognitive tasks which are grouped with difficulty level, type and category. This environment is namely, Logic environment. It is mainly divided into three main categories: memory, concentration and reasoning. To classify and regress learner's scores according to the category and the type of cognitive task acquired, we trained and tested different machine learning algorithms such as linear regression, support vector machines, random forests and gradient boosting. Primary results shows that a random forest algorithm is the most suitable model for classifying and regressing the learners' scores in cognitive tasks, where the features most important for the model are, in descending order: the task difficulty and the task category in the case of regression, the task difficulty, the time taken by the participant before completing it, and his electroencephalogram mental metrics in the case of classification.In the same vein and in order to have an overview of a learner's performance before accomplishing a cognitive task, we have developed Logic environment which contains seven types of cognitive tasks. These tasks are grouped into three

The Relationship Between Design Outcomes and Mental States During Ideation

“…These advantages led to superior performance of SVM in several EEG studies, such as synchronous brain-computer interface, emotion recognition, and eye events, compared to other machine learning methods [66][67][68]. Therefore, SVM classifiers are extensively used in EEG-based research to facilitate predictions based on brain wave features, including ABM metrics [69][70][71][72].…”