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Neurophysiological Measurements in Higher Education: A Systematic Literature Review

IN: International Journal of Artificial Intelligence in Education


The use of neurophysiological measurements to advance the design, development, use, acceptance, influence and adaptivity of information systems is receiving increasing attention. Within the field of education, neurophysiological measurements have commonly been used to capture a learner’s psychological constructs such as cognitive load, attention and emotion, which play an important role in student learning. This paper systematically examines the literature on the use of neurophysiological measurements in higher education. In particular, using a well-established Systematic Literature Review (SLR) method, we identified 83 papers reporting empirical evidence about the outcome of employing neurophysiological measurements within educational technologies in higher education. The findings of the SLR are divided into three main themes discussing the employed measurements, experimental settings and constructs and outcomes. Our findings identify that (1) electroencephalography and facial expression recognition are the dominantly employed types of measurement, (2) the majority of the experiments used a pre-experimental design, (3) attention and emotion are the two foremost cognitive and non-cognitive constructs under investigation, while less emphasis is paid to meta-cognitive constructs and (4) the reported results mostly focus on monitoring learners’ states, which are not always the same as the intended purpose, such as developing an adaptive system. On a broader term, the review of the literature provides evidence of the effective use of neurophysiological measurements by educational technologies to enhance learning; however, a number of challenges and concerns related to the accuracy and validity of the captured construct, the intrusiveness of the employed instruments as well as ethical and privacy considerations have surfaced, that need to be addressed before such technologies can be employed and adopted at scale.

Ali Darvishi, Hassan Khosravi, Shazia Sadiq, Barbara Weber

20 Jun 2021

Item Type
Journal paper
Journal Title
International Journal of Artificial Intelligence in Education
Subject Areas
computer science