THE IMPACT OF LEARNING THROUGH COGNITIVE LOAD ASSESSMENT AND EMOTIONAL STATE EVALUATION
THE IMPACT OF LEARNING THROUGH COGNITIVE LOAD ASSESSMENT AND EMOTIONAL STATE EVALUATION

Summary/Abstract: The cognitive load (CL) and the emotion that encompasses each learning process, despite its type or level, represent very challenging aspects of a learning process, their assessment being difficult mainly due to the subjective appreciation. On the other hand, nowadays, students learn by spending most of their time on using digital devices. Therefore, analyzing the emotional state during learning, evaluating the visual effort, and assessing the cognitive load level, all induced using software applications or electronic devices, becomes a necessity. Emotions are defined as mental and physiological sensorial states that influence human perception, thinking, experience, learning, behavior, and decision making. They are apprehended as a short and conscious experience characterized by an intense mental activity classified between negative or unpleasant and positive or pleasant feelings. Some peripheral physiological signals such as heart rate, arterial pressure, electrodermal activity, or body temperature reflect the excitement level, but they are unable to discern the positivity and the negativity relative to a reference state. A student's concentration effort or its emotional state could be evaluated as well by using precise measurements of the pupil dimension of the blinking frequency or the eye movement. Instruction should aim to reduce the extrinsic CL and to optimize the intrinsic CL enhancing the learning relevant CL. Nevertheless, only EEG signals offer precise information concerning the emotional state and the CL level through sensors placed on the scalp according to the functioning of the cerebral lobes and their reactions to certain short-term or long-term stimuli. Recording and processing physiological signals or cerebral waves are some of the most explored solutions of the last decade that are leading to encouraging results. This paper mainly studies and illustrates some approaches to evaluating the cognitive load and emotional state of students during a learning process. Essentially, this paper focuses on elaborating experimental sessions, choosing the proper stimulus and equipment, exploiting adequate recording and preprocessing methods for the involved physiological information, and engaging artificial intelligence techniques for feature selection and data classification to achieve the best calibration, appreciation, and monitoring of a learning process.

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