Publication
ISense: An assessment instrument that predicts self-regulated learning using Mobile sensing
| Summary: | Self-regulated learning significantly influences students’ academic behavior and performance. Traditional SRL assessment heavily relies on subjective and self-evaluation methods, which are susceptible to personal biases and cognitive limitations. In this study, we leverage mobile devices with GPS and sensors to collect self-reported and passive mobile sensing data from 211 college students over a year. Our aim is to conduct a passive assessment of self-regulated learning. To achieve this, we apply four deep learning models to analyze behavioral features associated with self-regulated learning using students’ life record data. Our findings demonstrate a precise assessment of student self-regulated learning, encompassing the following subscales. Environment structuring (MAE = 2.88, r = 0.54) represents participation in the organization and construction of the learning environment. Time management (MAE = 2.82, r = 0.57) represents planning study time and balancing study with other activities; Help seeking (MAE = 4.86, r = 0.53) represents the tendency and frequency of students to seek help during the learning process. Our study helps inspire new forms of education to assess self-regulated learning and paves the way for individualized interventions in future studies. |
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| Main Authors: | Zhao, Tongyu |
| Other Authors: | Gao, Jiaying; Feng, Yu; Zu, Yatong; Tavares, Adriano; Gomes, Tiago Manuel Ribeiro; Pinto, Sandro; Xu, Hao |
| Subject: | Evaluation methodologies Human-computer interface Improving classroom teaching Lifelong learning Mobile learning |
| Year: | 2026 |
| Country: | Portugal |
| Document type: | article |
| Access type: | open access |
| Associated institution: | Universidade do Minho |
| Language: | English |
| Origin: | RepositóriUM - Universidade do Minho |
| Summary: | Self-regulated learning significantly influences students’ academic behavior and performance. Traditional SRL assessment heavily relies on subjective and self-evaluation methods, which are susceptible to personal biases and cognitive limitations. In this study, we leverage mobile devices with GPS and sensors to collect self-reported and passive mobile sensing data from 211 college students over a year. Our aim is to conduct a passive assessment of self-regulated learning. To achieve this, we apply four deep learning models to analyze behavioral features associated with self-regulated learning using students’ life record data. Our findings demonstrate a precise assessment of student self-regulated learning, encompassing the following subscales. Environment structuring (MAE = 2.88, r = 0.54) represents participation in the organization and construction of the learning environment. Time management (MAE = 2.82, r = 0.57) represents planning study time and balancing study with other activities; Help seeking (MAE = 4.86, r = 0.53) represents the tendency and frequency of students to seek help during the learning process. Our study helps inspire new forms of education to assess self-regulated learning and paves the way for individualized interventions in future studies. |
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Funded activities
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