A Review of an Invasive and Non-invasive Automatic Confusion Detection Techniques. Issue 1 (June 2021) - Ibrahim, F; Mutashar, S; Hamed, B
Abstract: Human mind confusion was found one of the primary causes of minimal execution in any type of everyday assignment that requires reasoning or during any learning process. Detecting confusion is important, and it plays a vital role in student e-learning environment. Detecting confusion by computerized machinery is challenging since it requires artificial intelligence methodology, and it has many advantages, which are highlighted in this work. The computerized confusion detection techniques are classified into two categories, sensor based and extraction of facial visual cues, in the paper, with elaborating on their details. The different confusion detection techniques that have been used in some previous research works with their classification technique, number of participants, accuracy and feature type were listed and compared to investigate the better technique, and recommendation was stated. This review would absolutely rapid researchers to supplement their efforts towards the expansion of automatic confusion detection systems.