Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables
The ability to concentrate well is an important determinant of students’ learning outcomes but remains poorly understood. In this work we investigated whether there exists a mapping between students’ biosignals and perceived concentration levels. If we succeed in this mapping,...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10077565/ |
| _version_ | 1797855360263913472 |
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| author | Caj Sodergard Timo Laakko |
| author_facet | Caj Sodergard Timo Laakko |
| author_sort | Caj Sodergard |
| collection | DOAJ |
| description | The ability to concentrate well is an important determinant of students’ learning outcomes but remains poorly understood. In this work we investigated whether there exists a mapping between students’ biosignals and perceived concentration levels. If we succeed in this mapping, a wearable can function as a Concentration Tracker, a novel feature that is missing from current wearables. For this, a wearable wristband was used to record students’ heart rate, heart rate variability, skin temperature, skin conductivity and acceleration from body changes. Additionally, students self-assessed their concentration levels using a smartphone application. We improved the accuracy by utilizing a big amount of unlabelled biodata from outside the study sessions. Our best boosted regression tree model predicted students’ concentration level with only 1.7% NMAE error. The predictions for a user not in the training set were much weaker; the best model, a convolutional neural network, achieved a prediction NMAE error of 30.7%. This implies that the users generated biosignals highly individually. Thus, models are not well transferable from one user to another without rooting them in user-specific data. Contrary to stress research, our results showed that skin conductivity had mostly a negative correlation with students’ concentration levels. Also diverging from stress reactions, skin temperature had mainly a positive correlation. Conductivity and temperature were the two dominant predictors. Further, the results suggest that an element of deep, effortless concentration was present in the learning experience of the subjects. Altogether, our work demonstrates that a concentration tracking wearable for improving learning is technically achievable. |
| first_indexed | 2024-04-09T20:22:29Z |
| format | Article |
| id | doaj.art-c6c17340977b409ca9dc67cf3163ba51 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T20:22:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c6c17340977b409ca9dc67cf3163ba512023-03-30T23:01:36ZengIEEEIEEE Access2169-35362023-01-0111303083032310.1109/ACCESS.2023.326006110077565Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From WearablesCaj Sodergard0https://orcid.org/0000-0002-7379-8610Timo Laakko1https://orcid.org/0000-0001-7893-9083NextAI, Espoo, FinlandVTT Technical Research Centre of Finland Ltd., Espoo, FinlandThe ability to concentrate well is an important determinant of students’ learning outcomes but remains poorly understood. In this work we investigated whether there exists a mapping between students’ biosignals and perceived concentration levels. If we succeed in this mapping, a wearable can function as a Concentration Tracker, a novel feature that is missing from current wearables. For this, a wearable wristband was used to record students’ heart rate, heart rate variability, skin temperature, skin conductivity and acceleration from body changes. Additionally, students self-assessed their concentration levels using a smartphone application. We improved the accuracy by utilizing a big amount of unlabelled biodata from outside the study sessions. Our best boosted regression tree model predicted students’ concentration level with only 1.7% NMAE error. The predictions for a user not in the training set were much weaker; the best model, a convolutional neural network, achieved a prediction NMAE error of 30.7%. This implies that the users generated biosignals highly individually. Thus, models are not well transferable from one user to another without rooting them in user-specific data. Contrary to stress research, our results showed that skin conductivity had mostly a negative correlation with students’ concentration levels. Also diverging from stress reactions, skin temperature had mainly a positive correlation. Conductivity and temperature were the two dominant predictors. Further, the results suggest that an element of deep, effortless concentration was present in the learning experience of the subjects. Altogether, our work demonstrates that a concentration tracking wearable for improving learning is technically achievable.https://ieeexplore.ieee.org/document/10077565/Affective computingaffective learningartificial intelligencebiosignalsconvolutional neural networkseducational technology |
| spellingShingle | Caj Sodergard Timo Laakko Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables IEEE Access Affective computing affective learning artificial intelligence biosignals convolutional neural networks educational technology |
| title | Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables |
| title_full | Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables |
| title_fullStr | Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables |
| title_full_unstemmed | Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables |
| title_short | Inferring Students’ Self-Assessed Concentration Levels in Daily Life Using Biosignal Data From Wearables |
| title_sort | inferring students x2019 self assessed concentration levels in daily life using biosignal data from wearables |
| topic | Affective computing affective learning artificial intelligence biosignals convolutional neural networks educational technology |
| url | https://ieeexplore.ieee.org/document/10077565/ |
| work_keys_str_mv | AT cajsodergard inferringstudentsx2019selfassessedconcentrationlevelsindailylifeusingbiosignaldatafromwearables AT timolaakko inferringstudentsx2019selfassessedconcentrationlevelsindailylifeusingbiosignaldatafromwearables |