دورية أكاديمية
An Engagement-Aware Predictive Model to Evaluate Problem-Solving Performance from the Study of Adult Skills' (PIAAC 2012) Process Data
| العنوان: | An Engagement-Aware Predictive Model to Evaluate Problem-Solving Performance from the Study of Adult Skills' (PIAAC 2012) Process Data |
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| اللغة: | English |
| المؤلفون: | Jinnie Shin (ORCID |
| المصدر: | Large-scale Assessments in Education. 2024 12. |
| الإتاحة: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 22 |
| تاريخ النشر: | 2024 |
| نوع الوثيقة: | Journal Articles Reports - Research |
| Descriptors: | Prediction, Models, Problem Solving, Performance, Short Term Memory, Attention, Task Analysis, Sequential Learning, Learning Analytics, Foreign Countries, Learner Engagement, Adults, Competence |
| مصطلحات جغرافية: | United States, South Korea, United Kingdom |
| Assessment and Survey Identifiers: | Program for the International Assessment of Adult Competencies (PIAAC) |
| DOI: | 10.1186/s40536-024-00194-y |
| تدمد: | 2196-0739 |
| مستخلص: | The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-scale assessments and learning analytics. This study introduces a deep-learning-based approach to predictive modeling of the examinee's performance in sequential, interactive problem-solving tasks from a large-scale assessment of adults' educational competencies. The current methods disambiguate problem-solving behaviors using network analysis to inform the examinee's performance in a series of problem-solving tasks. The unique contribution of this framework lies in the introduction of an "effort-aware" system. The system considers the information regarding the examinee's task-engagement level to accurately predict their task performance. The study demonstrates the potential to introduce a high-performing deep learning model to learning analytics and examinee performance modeling in a large-scale problem-solving task environment collected from the OECD Programme for the International Assessment of Adult Competencies (PIAAC 2012) test in multiple countries, including the United States, South Korea, and the United Kingdom. Our findings indicated a close relationship between the examinee's engagement level and their problem-solving skills as well as the importance of modeling them together to have a better measure of students' problem-solving performance. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| رقم الأكسشن: | EJ1415232 |
| قاعدة البيانات: | ERIC |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 2196-0739 |
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| DOI: | 10.1186/s40536-024-00194-y |