Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding
The relationship between the cognitive and affective dimensions of understanding has remained unexplored due to the lack of reliable methods for measuring emotions and feelings during learning. Focusing on five phases of understanding—nascent understanding, misunderstanding, confusion, emergent unde...
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MDPI AG
2025-01-01
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| author | Milan Lazic Earl Woodruff Jenny Jun |
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| description | The relationship between the cognitive and affective dimensions of understanding has remained unexplored due to the lack of reliable methods for measuring emotions and feelings during learning. Focusing on five phases of understanding—nascent understanding, misunderstanding, confusion, emergent understanding, and deep understanding—this study introduces an AI-driven solution to measure subjective understanding by analyzing physiological activity manifested in facial expressions. To investigate these phases, 103 participants remotely worked on 15 riddles while their facial expressions were video recorded. Action units (AUs) for each phase instance were measured using AFFDEX software. AU patterns associated with each phase were then identified through the application of six supervised machine learning algorithms. Distinct AU patterns were found for all five phases, with gradient boosting machine and random forest models achieving the highest predictive accuracy. These findings suggest that physiological activity can be leveraged to reliably measure understanding. Further, they advance a novel approach for measuring and fostering understanding in educational settings, as well as developing adaptive learning technologies and personalized educational interventions. Future studies should explore how physiological signatures of understanding phases both reflect and influence their associated cognitive processes, as well as the generalizability of this study’s findings across diverse populations and learning contexts (A suite of AI tools was employed in the development of this paper: (1) ChatGPT4o (for writing clarity and reference checking), (2) Grammarly (for grammar and editorial corrections), and (3) ResearchRabbit (reference management)). |
| format | Article |
| id | doaj-art-bfeb46ce49574d86a0f4a70993d48212 |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-01-01 |
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| series | AI |
| spelling | doaj-art-bfeb46ce49574d86a0f4a70993d482122025-01-24T13:17:24ZengMDPI AGAI2673-26882025-01-01611810.3390/ai6010018Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of UnderstandingMilan Lazic0Earl Woodruff1Jenny Jun2Department of Applied Psychology and Human Development, Ontario Institute for Studies in Education, University of Toronto, Toronto, ON M5S 1V6, CanadaDepartment of Applied Psychology and Human Development, Ontario Institute for Studies in Education, University of Toronto, Toronto, ON M5S 1V6, CanadaDepartment of Applied Psychology and Human Development, Ontario Institute for Studies in Education, University of Toronto, Toronto, ON M5S 1V6, CanadaThe relationship between the cognitive and affective dimensions of understanding has remained unexplored due to the lack of reliable methods for measuring emotions and feelings during learning. Focusing on five phases of understanding—nascent understanding, misunderstanding, confusion, emergent understanding, and deep understanding—this study introduces an AI-driven solution to measure subjective understanding by analyzing physiological activity manifested in facial expressions. To investigate these phases, 103 participants remotely worked on 15 riddles while their facial expressions were video recorded. Action units (AUs) for each phase instance were measured using AFFDEX software. AU patterns associated with each phase were then identified through the application of six supervised machine learning algorithms. Distinct AU patterns were found for all five phases, with gradient boosting machine and random forest models achieving the highest predictive accuracy. These findings suggest that physiological activity can be leveraged to reliably measure understanding. Further, they advance a novel approach for measuring and fostering understanding in educational settings, as well as developing adaptive learning technologies and personalized educational interventions. Future studies should explore how physiological signatures of understanding phases both reflect and influence their associated cognitive processes, as well as the generalizability of this study’s findings across diverse populations and learning contexts (A suite of AI tools was employed in the development of this paper: (1) ChatGPT4o (for writing clarity and reference checking), (2) Grammarly (for grammar and editorial corrections), and (3) ResearchRabbit (reference management)).https://www.mdpi.com/2673-2688/6/1/18machine learningsubjective understandingphysiological activityfacial expressionsaction units (AUs) |
| spellingShingle | Milan Lazic Earl Woodruff Jenny Jun Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding AI machine learning subjective understanding physiological activity facial expressions action units (AUs) |
| title | Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding |
| title_full | Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding |
| title_fullStr | Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding |
| title_full_unstemmed | Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding |
| title_short | Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding |
| title_sort | decoding subjective understanding using biometric signals to classify phases of understanding |
| topic | machine learning subjective understanding physiological activity facial expressions action units (AUs) |
| url | https://www.mdpi.com/2673-2688/6/1/18 |
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