The Human Learning Attention



Journal Title

Journal ISSN

Volume Title



Background: Typical methods of assessing learning attention or engagement involve subjective responses, classroom interaction, and observations. As for online learning, learning analytic and pedagogical tools are currently used to motivate and track engagement through the Learning Management System (LMS), but can this accurately measure students' level of attention and engagement? Additionally, there is a gap in how learning attention is studied and objectively measured in Optometry. Purpose: The study examined the effects of active learning strategies on engagement, course satisfaction, and student outcomes during a simulated online neuroanatomy lecture. The engagement was measured subjectively using surveys and objectively monitored using wireless, wearable devices and biometrics such as Electroencephalography (EEG), Galvanic Skin Response (GSR), and an eye tracker application while the subjects participated in the lecture. Methods: In this pilot experimental research study, 32 incoming first-year Optometry students who had not taken neuroanatomy were recruited for a two-hour laboratory visit. The subjects were randomly assigned to one of the four learning modules (Active 1, Active 2, Passive 1, or Passive 2); afterward, a cross-over module was provided for the second part of the experiment (i.e., if P1 first, then A2 second). The signals from the Shimmer GSR device, Emotiv EPOC X EEG headband, and the subject's pupil size and gaze were simultaneously integrated and measured using EventIDE software (Okazolab). Data from a course test and subjective assessments were collected for each learning module, including a course evaluation, an Active Learning in Health Professions Scale (ALPHS), and an Engagement Learning Index (ELI). Results: The results suggested that for this small homogeneous sample size: 1) Didactic strategies perception was significantly increased when active learning was present (t(31) = 5.11, p < .00); 2) Students’ engagement was shown to be statistically improved with active learning for these objective measurements, EEG for engagement (t(31)= 4.31, p<.001), EEG for interest (t(31)=3.96, p<.001), EEG- Excitement (t(31)=3.28, p<.01), GSR Peaks per minute (t(31)=2.98, p<.01), and fixation rate (t(31)=4.12, p<.001); and for subjective measurement, ALHPS didactic strategies (t(31) = 5.11, p<.001) were significantly higher when active learning was employed, and that other surveys were not sensitive; 3) There was a significant positive correlation between a) course evaluation and ALPHS didactic strategies (r(30) = .35, p<.05), b) pupil size and fixation number (r(30) = .65, p<.01), and c) EEG focus and EEG excitement (r(30) = .35, p<.05); 4) There was no significant positive correlation between subjective and objective measurements; 5) Although the course performance was 4% better and the students were more satisfied with active instruction, these improvements were not significantly correlated with students' engagement; and 6) Breaking it down by college grades, 58.8% of students who did not earn mostly A’s gained ten or more points in course performance compared to 13.3 % of the mostly A’s students. Conclusions: This study found better reliability for wireless mobile devices in tracking engagements than surveys; however, due to its positive correlation, a course evaluation survey can be utilized to gauge learners' perceptions of the use of didactic strategies. A larger-scale study in the classroom, with faculty-student interaction and meaningful active learning intervention, is required to investigate further the impact of active learning on learning outcomes in Optometry and the correlations among other biometric and objective measurements.



Active learning, Engaged learning, Measuring engagement objectively, Optometric education, Emotiv EEG, GSR, Pupil Tracker