The MET Lab
Micro-Facial Expression (MET) Lab is involved in research on students’ learning-related emotions, motivation, and persistence: using a micro-facial tracking system, studies monitor the changes, intensity, and frequency of students’ emotions for analysis.
Current Research Direction
Lifelong learning has garnered increasing interest given its relevance to people as well as to machine learning. The 2019 World Bank’s Annual Report highlighted the importance of lifelong learning amid an era of information explosion, technological transformation, and ongoing evolution. Researchers have long advocated for the promotion of lifelong learning skills in school and family settings. A lifelong learner should be persistent, autonomous, and self-motivated. Given the complexity of the cognitive mechanism underlying lifelong learning, few studies have systematically deciphered the interplay between persistence, autonomy, and motivation (the three basic elements of lifelong learning). Further, little is known about the role of emotion in lifelong learning.
Research Questions
a) How does the cognitive mechanism of lifelong learning work?
b) How does emotion wellbeing influence lifelong learning?
Method
Survey + Interview + Experiment
Measuring Motivation
Internalization of Learning Motivation (ILM) scale developed by Dr. Yurou Wang & Dr. Stepanie Wind
Experiment Measuring Autonomy & Persistence
Two sets of computer-based tasks were conducted for two topics (numerical and literacy reasoning). Each group responded to 25 questions from Genius Tests’ Culture Fair Intelligence Test.
Persistence Calculation
The test-level persistence index was calculated with the following formula:
Equiptment
The Intelligent Micro-facial Expression Recognition System integrates computer vision processing technology and micro-expression detection. Its judgment system can model and learn large numbers of cases through offline training of terabyte data. The system obtains micro-expression models corresponding to different cognitive and psychological states, especially micro-expression data that convey emotion-based changes
Data Visulization
Current Findings
Results of the regression model indicated that supporting of autonomy and joy were each significantly and positively related to the persistence score (β1 = 4.159, p = .012), partially supporting our hypothesis. A larger sample size is needed to confirm this finding.
Future Directions
A future project is planned that explores the development of lifelong learning competence between high school students (adolescence) and college students (early adulthood) in addition to including students from different countries (e.g., China, Korea, and Turkey). The same procedure will be used as in the aforementioned study with the potential inclusion of MRI data. Adolescents’ prefrontal cortex is not fully developed, and the emotional control circuit (i.e., top-down control of the prefrontal cortex for the limbic system) in the brain is not fully formed. Thus, adolescents may exhibit different patterns of lifelong learning competence compared with early adults. Relevant insight could help educators explore ideal ways to promote lifelong learning competence while the brain is developing.
Credits:
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