Enhancing Self-Directed Learning and Python Mastery through Integration of a Large Language Model and Learning Analytics Dashboard
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| Title: | Enhancing Self-Directed Learning and Python Mastery through Integration of a Large Language Model and Learning Analytics Dashboard |
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| Language: | English |
| Authors: | Ming Liu (ORCID |
| Source: | British Journal of Educational Technology. 2026 57(4):1009-1035. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 27 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Independent Study, Mastery Learning, Programming Languages, Natural Language Processing, Learning Analytics, Educational Technology, Artificial Intelligence, Online Courses, Computer Science Education, Self Evaluation (Individuals), Interpersonal Competence, Electronic Learning |
| DOI: | 10.1111/bjet.70005 |
| ISSN: | 0007-1013 1467-8535 |
| Abstract: | Self-directed learning (SDL) is a critical skill in the 21st century, particularly in online Python learning environments. Learning analytics (LA) can track and analyse learning processes, which can be leveraged to prompt students to reflect on their learning strategies and progress through learning analytics dashboards (LADs). However, LADs lack pedagogical domain knowledge and fail to provide effective personalised feedback and guidance. This study designs and presents a Generative AI-powered SDL tool, SDLChat. It integrates a large language model (ERNIE-3.5) with retrieval-augmented generation (RAG) technology to generate contextualised, actionable feedback for learners across the entire SDL cycle: planning, self-monitoring and self-reflection. To evaluate the impact of SDLChat on learners' SDL skills and Python knowledge, a randomised experimental study was conducted over a six-week Python online course. The study compared the changes in SDL skills and Python knowledge of students using both SDLChat and LAD group (n = 39) and LAD-only group (n = 35). The results indicate that: (1) students using SDLChat and LAD significantly outperformed those using LAD alone in Python knowledge mastery, self-monitoring and interpersonal skills and (2) the LAD-only group showed significant improvement only in Python knowledge mastery; however, (3) no significant differences were found in posttask motivation between these two groups. This study highlights the potential of integrating LLM with learning analytics to enhance SDL skills and learning performance in online learning contexts. It also establishes a theory-informed operational framework for understanding the LLM-empowered SDL process. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1508528 |
| Database: | ERIC |
| Abstract: | Self-directed learning (SDL) is a critical skill in the 21st century, particularly in online Python learning environments. Learning analytics (LA) can track and analyse learning processes, which can be leveraged to prompt students to reflect on their learning strategies and progress through learning analytics dashboards (LADs). However, LADs lack pedagogical domain knowledge and fail to provide effective personalised feedback and guidance. This study designs and presents a Generative AI-powered SDL tool, SDLChat. It integrates a large language model (ERNIE-3.5) with retrieval-augmented generation (RAG) technology to generate contextualised, actionable feedback for learners across the entire SDL cycle: planning, self-monitoring and self-reflection. To evaluate the impact of SDLChat on learners' SDL skills and Python knowledge, a randomised experimental study was conducted over a six-week Python online course. The study compared the changes in SDL skills and Python knowledge of students using both SDLChat and LAD group (n = 39) and LAD-only group (n = 35). The results indicate that: (1) students using SDLChat and LAD significantly outperformed those using LAD alone in Python knowledge mastery, self-monitoring and interpersonal skills and (2) the LAD-only group showed significant improvement only in Python knowledge mastery; however, (3) no significant differences were found in posttask motivation between these two groups. This study highlights the potential of integrating LLM with learning analytics to enhance SDL skills and learning performance in online learning contexts. It also establishes a theory-informed operational framework for understanding the LLM-empowered SDL process. |
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| ISSN: | 0007-1013 1467-8535 |
| DOI: | 10.1111/bjet.70005 |
