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When students reflect on their learning from a textbook via think aloud, network representations can be used to capture their concepts and relations. What can we learn from these network representations about students’ learning processes, knowledge acquisition, and learning outcomes? This study brings methods from entity and relation extraction using classic and LLM-based methods to the application domain of educational psychology. We built a ground-truth baseline of relational data that represent relevant (to educational science), textbook-based information as a semantic network. We identified SPN4RE and LUKE as the most accurate method to extracting semantic networks capturing the same types of information from transcriptions of verbal student data. Correlating the students’ semantic networks with learning outcomes showed that students’ verbalizations varied in structure, reflecting different learning processes. Denser and more interconnected semantic networks indicated more elaborated knowledge acquisition. Structural features such as the number of edges and surface overlap with textbook networks significantly correlated with students’ posttest performance.
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Leveraging learning theory and analytics to produce grounded, innovative, data-driven, equitable improvements to teaching and learning.
Research in educational psychology involves empirical investigation into the learning process with an aim to refine psychological theories of learning and their application to real-world settings where they can be used to benefit learners. Emergent methodological processes involved in learning analytics include the study of event-based data produced by individuals in learning environments where they use technology. Paradigms for substantive-methodological synergy can be used to align the strengths of educational psychology and learning analytics research. The Journal of Educational Psychology invites such collaborations. This issue illustrates the advancements to educational theory and practice that can be attained when learning analytics practices are aligned to reflect the assumptions within psychological theories of learning and learning analytics methods including feature engineering and multimodal modeling are leveraged. Exemplars demonstrate learning analytics’ potential contribution to the refinement and application of theories of learning and motivation. Educational Impact and Implications Statement Theories about learning describe complex processes and how the ways individuals undertake them affect the understanding they obtain and performances they achieve. Many of these learning processes are difficult to observe in the naturalistic settings where people learn. When data individuals produce during learning with technologies are collected and modeled in alignment with learning theories and using learning analytics methods, they can make learning processes observable. Incorporating learning analytics into the study of learning and the development of instruction can help refine learning theories and the design of technologies that individuals use to learn.
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- Award ID(s):
- 1920756
- PAR ID:
- 10654995
- Publisher / Repository:
- APA
- Date Published:
- Journal Name:
- Journal of Educational Psychology
- Volume:
- 117
- Issue:
- 1
- ISSN:
- 0022-0663
- Page Range / eLocation ID:
- 1 to 11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1037/edu0000933
