A method related to personality traits that detects learning challenges through physiological signals.
Saved in:
| Title: | A method related to personality traits that detects learning challenges through physiological signals. |
|---|---|
| Authors: | Lin, Jai-Rou1 (AUTHOR), Tseng, Chun-Hsiung1 (AUTHOR) lendle.tseng.archive@gmail.com, Lin, Hao-Chiang Koong2 (AUTHOR), Huang, Andrew Chih-Wei3 (AUTHOR) |
| Source: | Soft Computing - A Fusion of Foundations, Methodologies & Applications. Jul2026, Vol. 30 Issue 7, p4945-4955. 11p. |
| Subjects: | Learning problems, Personality, Heart beat, Electrocardiography, Real-time computing, Neurophysiology, Galvanic skin response, Random forest algorithms |
| Abstract: | Assessing students' comprehension and identifying learning challenges in traditional physical courses is a challenging task, as it often lacks the data-driven techniques used in online learning environments. This study introduces a novel approach to address this issue by leveraging physiological signals and accounting for individual differences in learning-related personality traits. Previous research has shown that emotional responses are linked to learning challenges. Physiological signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and heart rate (HR), offer a potential avenue to detect these emotional responses. The study involved two phases. In the first phase, participants' physiological signals were recorded while they answered a quiz, and their perceived difficulty levels were self-assessed. To account for individual differences in learning-related personality traits, participants were categorized into four groups based on their conscientiousness and openness to experience, both of which have been identified in prior literature as significant predictors of academic performance. Random Forest models were then trained for each group, using physiological signal features as input and perceived difficulty levels as output. This approach significantly improved accuracy compared to a single, generalized model. In the second phase, the developed models were tested in a "Discrete Mathematics" course with 48 enrolled students, and the results provided a preliminary indication of the system's feasibility in a real classroom setting, with students reporting that the system's difficulty assessments were broadly plausible. The proposed approach is adaptable to both physical and online courses, automated, and capable of real-time assessment. Future work may focus on refining and expanding the model to encompass a broader range of personality traits and learning contexts. [ABSTRACT FROM AUTHOR] |
| Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 196062591 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A method related to personality traits that detects learning challenges through physiological signals. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lin%2C+Jai-Rou%22">Lin, Jai-Rou</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tseng%2C+Chun-Hsiung%22">Tseng, Chun-Hsiung</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lendle.tseng.archive@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Hao-Chiang+Koong%22">Lin, Hao-Chiang Koong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Andrew+Chih-Wei%22">Huang, Andrew Chih-Wei</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Soft+Computing+-+A+Fusion+of+Foundations%2C+Methodologies+%26+Applications%22">Soft Computing - A Fusion of Foundations, Methodologies & Applications</searchLink>. Jul2026, Vol. 30 Issue 7, p4945-4955. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Learning+problems%22">Learning problems</searchLink><br /><searchLink fieldCode="DE" term="%22Personality%22">Personality</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+beat%22">Heart beat</searchLink><br /><searchLink fieldCode="DE" term="%22Electrocardiography%22">Electrocardiography</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Neurophysiology%22">Neurophysiology</searchLink><br /><searchLink fieldCode="DE" term="%22Galvanic+skin+response%22">Galvanic skin response</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Assessing students' comprehension and identifying learning challenges in traditional physical courses is a challenging task, as it often lacks the data-driven techniques used in online learning environments. This study introduces a novel approach to address this issue by leveraging physiological signals and accounting for individual differences in learning-related personality traits. Previous research has shown that emotional responses are linked to learning challenges. Physiological signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and heart rate (HR), offer a potential avenue to detect these emotional responses. The study involved two phases. In the first phase, participants' physiological signals were recorded while they answered a quiz, and their perceived difficulty levels were self-assessed. To account for individual differences in learning-related personality traits, participants were categorized into four groups based on their conscientiousness and openness to experience, both of which have been identified in prior literature as significant predictors of academic performance. Random Forest models were then trained for each group, using physiological signal features as input and perceived difficulty levels as output. This approach significantly improved accuracy compared to a single, generalized model. In the second phase, the developed models were tested in a "Discrete Mathematics" course with 48 enrolled students, and the results provided a preliminary indication of the system's feasibility in a real classroom setting, with students reporting that the system's difficulty assessments were broadly plausible. The proposed approach is adaptable to both physical and online courses, automated, and capable of real-time assessment. Future work may focus on refining and expanding the model to encompass a broader range of personality traits and learning contexts. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=196062591 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00500-026-11404-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 4945 Subjects: – SubjectFull: Learning problems Type: general – SubjectFull: Personality Type: general – SubjectFull: Heart beat Type: general – SubjectFull: Electrocardiography Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Neurophysiology Type: general – SubjectFull: Galvanic skin response Type: general – SubjectFull: Random forest algorithms Type: general Titles: – TitleFull: A method related to personality traits that detects learning challenges through physiological signals. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lin, Jai-Rou – PersonEntity: Name: NameFull: Tseng, Chun-Hsiung – PersonEntity: Name: NameFull: Lin, Hao-Chiang Koong – PersonEntity: Name: NameFull: Huang, Andrew Chih-Wei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14327643 Numbering: – Type: volume Value: 30 – Type: issue Value: 7 Titles: – TitleFull: Soft Computing - A Fusion of Foundations, Methodologies & Applications Type: main |
| ResultId | 1 |
