Abstract:

Engagement plays a crucial role in learning. Many studies outline that engagement is important and assert its impact on student’s performance, satisfaction and retention.

In the scientific literature engagement is defined according to its multidimensional nature: behavioral, cognitive and affective. Self-reporting instruments are predominantly used to report cognitive or affective engagement, but these instruments are also known for their limitations: people are often biased when they report on their own experiences and talk aloud approach are known to interfere with the authenticity of the learning experience.

Recently, technological developments in the field of neuroscience have created new possibilities for research in education. New approaches developed in the field such as neuroergonomics allow the gathering of data with high temporal resolution on cognitive and affective dimensions of engagement, and it is even possible to do measure it in real-time.

In that context, this paper aims to explore the potential of collecting neurophysiological data to assess the quality of e-learning media. The – quality of learning media – is understood in terms of engagement generated to the learners who use it. Thus, in a neurophysiological methodological approach, multidimensional engagement data are collected with various sensors:
i) subject behaviors are recorded through interactions within the e-learning environment and through eye-tracking,
ii) cognitive engagement is characterized with electroencephalographic signal (EEG) and
iii) affective engagement is inferred with galvanic skin conductance and automatic facial emotion recognition.

As an example, data from an ongoing research project will be presented and discussed. The paper will show how affective or cognitive engagement varies overtime in 2 conditions oriented on the same content but where the media richness is different (a classical video of a professor with powerpoint vs. an infographic animation of content). It will be argued that the gathering continuous psychophysiological data gives way to e-learning curriculum developers for more informed decision making on which e-learning pedagogical material are more engaging, and consequently of a better quality.

Keywords:

e-learning, neuroscience, engagement, quality.