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| Autori principali: | , , , , , , , |
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| Natura: | Recurso educativo Open Access |
| Lingua: | en |
| Pubblicazione: |
2025
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| Accesso online: | https://eric.ed.gov/?id=EJ1465625 |
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| _version_ | 1867181861952815104 |
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| author | Joel Weijia Lai Wei Qiu Maung Thway Lei Zhang Nurabidah Binti Jamil Chit Lin Su Samuel S. H. Ng Fun Siong Lim |
| author_facet | Joel Weijia Lai Wei Qiu Maung Thway Lei Zhang Nurabidah Binti Jamil Chit Lin Su Samuel S. H. Ng Fun Siong Lim Joel Weijia Lai Wei Qiu Maung Thway Lei Zhang Nurabidah Binti Jamil Chit Lin Su Samuel S. H. Ng Fun Siong Lim |
| collection | Education Resources Information Center |
| contents | Leveraging Process-Action Epistemic Network Analysis to Illuminate Student Self-Regulated Learning with a Socratic Chatbot Joel Weijia Lai Wei Qiu Maung Thway Lei Zhang Nurabidah Binti Jamil Chit Lin Su Samuel S. H. Ng Fun Siong Lim Artificial Intelligence Computer Software Learning Analytics Introductory Courses Statistics Education Undergraduate Students Network Analysis Learning Activities Metacognition Learning Strategies Information Seeking Learning Processes Interaction Problem Solving Concept Formation Learning Experience Questioning Techniques Scores Pretests Posttests Achievement Gains The growing use of generative AI (GenAI) has sparked discussions regarding integrating these tools into educational settings to enrich the learning experience of teachers and students. Self-regulated learning (SRL) research is pivotal in addressing this inquiry. One prevalent manifestation of GenAI is the large-language model (LLM) chatbot, enabling users to seek information and assistance. This paper aims to showcase how data on student interaction with a chatbot can be used in learning analytics to gain insights into SRL. This is achieved by adapting existing SRL frameworks to comprehend 34 students' interaction with an educational Socratic chatbot for a statistics class at the introductory undergraduate level. Chatbot conversations from students are categorized into learning actions and processes using the framework's process-action library. Thereafter, we analyze this data through ordered epistemic network analysis, furnishing valuable insights into how different students interact with the chatbot. Our findings reveal that higher-scoring students engage more frequently in reflective and evaluative activities, while lower-scoring students focus on searching for answers. Furthermore, students should shift from structured problem-solving, such as solving classroom questions, to questioning fundamental concepts with the chatbot and soliciting more examples to improve their learning gains. |
| format | Recurso educativo Open Access |
| id | eric_EJ1465625 |
| institution | ERIC Institute of Education Sciences |
| language | en |
| publishDate | 2025 |
| record_format | eric |
| spellingShingle | Leveraging Process-Action Epistemic Network Analysis to Illuminate Student Self-Regulated Learning with a Socratic Chatbot Joel Weijia Lai Wei Qiu Maung Thway Lei Zhang Nurabidah Binti Jamil Chit Lin Su Samuel S. H. Ng Fun Siong Lim Artificial Intelligence Computer Software Learning Analytics Introductory Courses Statistics Education Undergraduate Students Network Analysis Learning Activities Metacognition Learning Strategies Information Seeking Learning Processes Interaction Problem Solving Concept Formation Learning Experience Questioning Techniques Scores Pretests Posttests Achievement Gains Leveraging Process-Action Epistemic Network Analysis to Illuminate Student Self-Regulated Learning with a Socratic Chatbot Joel Weijia Lai Wei Qiu Maung Thway Lei Zhang Nurabidah Binti Jamil Chit Lin Su Samuel S. H. Ng Fun Siong Lim Artificial Intelligence Computer Software Learning Analytics Introductory Courses Statistics Education Undergraduate Students Network Analysis Learning Activities Metacognition Learning Strategies Information Seeking Learning Processes Interaction Problem Solving Concept Formation Learning Experience Questioning Techniques Scores Pretests Posttests Achievement Gains The growing use of generative AI (GenAI) has sparked discussions regarding integrating these tools into educational settings to enrich the learning experience of teachers and students. Self-regulated learning (SRL) research is pivotal in addressing this inquiry. One prevalent manifestation of GenAI is the large-language model (LLM) chatbot, enabling users to seek information and assistance. This paper aims to showcase how data on student interaction with a chatbot can be used in learning analytics to gain insights into SRL. This is achieved by adapting existing SRL frameworks to comprehend 34 students' interaction with an educational Socratic chatbot for a statistics class at the introductory undergraduate level. Chatbot conversations from students are categorized into learning actions and processes using the framework's process-action library. Thereafter, we analyze this data through ordered epistemic network analysis, furnishing valuable insights into how different students interact with the chatbot. Our findings reveal that higher-scoring students engage more frequently in reflective and evaluative activities, while lower-scoring students focus on searching for answers. Furthermore, students should shift from structured problem-solving, such as solving classroom questions, to questioning fundamental concepts with the chatbot and soliciting more examples to improve their learning gains. |
| title | Leveraging Process-Action Epistemic Network Analysis to Illuminate Student Self-Regulated Learning with a Socratic Chatbot |
| topic | Artificial Intelligence Computer Software Learning Analytics Introductory Courses Statistics Education Undergraduate Students Network Analysis Learning Activities Metacognition Learning Strategies Information Seeking Learning Processes Interaction Problem Solving Concept Formation Learning Experience Questioning Techniques Scores Pretests Posttests Achievement Gains |
| url | https://eric.ed.gov/?id=EJ1465625 |