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.
Highlights
In Intelligent Tutoring Systems (ITS) and Massive Open Online Courses (MOOC), recognition of user affective states, cognitive status and performance evolution during a task remain of great importance (Berka et al 2004, Pope et al 1995, Prinzel & Freeman 2000, Ramesh et al 2014)
Bloom (Krathwohl 2002) has created a hierarchical taxonomy that classifies cognitive tasks into six main categories: (1) remembering which consists of recall or retrieve previous information, (2) understanding which deals with comprehending the meaning, translate and interpolate objects, (3) applying which consists of using a concept in a new situation by computing or predicting information, (4) analyzing which requires analyzing, comparing, inferring and selecting material, (5) evaluating which consists of making judgments about the existing material by using a self-report questionnaire and (6) creating which deals with creating a new model from the previous material
Four main machine-learning algorithms are reported : support vector machine, random forest, gradient boosting with decision trees, and linear regression
Summary
In Intelligent Tutoring Systems (ITS) and Massive Open Online Courses (MOOC), recognition of user affective states, cognitive status and performance evolution during a task remain of great importance (Berka et al 2004, Pope et al 1995, Prinzel & Freeman 2000, Ramesh et al 2014). To detect and assess users’ alertness several studies have been undertaken in the field of artificial intelligence, human computer interaction, cognition and neuroscience (Prinzel & Freeman 2000, Wilson 2004) These works focus on using electroencephalogram (EEG) to extract more important features and bands. These measures intervene when a learner is involved in a task They can reflect the degree of a learner’s concentration during a task that is necessarily depending on different types of factors such as a learner’s situation during the task (if he is relaxed or not), his familiarity with the presented task (the level of the learner), the type of task presented, the difficulty of the task, his motivation and emotions. We will present a new approach, which is based mainly on gathering different types of data (EEG, task duration, task difficulty, emotion, task category, etc.) from a cognitive environment (Logic environment) and using them to train different machine learning algorithms. We present the results obtained from these algorithms and give some suggestions for future use according to different features used in this study (EEG mental metrics, task information and self-reported emotions, gender and age)
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