Topic Maps as Indexing Tools in the Educational Sphere: Theoretical Foundations, Review of Empirical Research and Future Challenges
Bibliographic record
Abstract
Topic Maps (International Organization of Standardization [ISO 13250], 1999; Topic maps are malleable -the concept and relationship creation process is dynamic and user-driven. In addition, topic maps are scalable and can hence be conjoined and merged. Perhaps, most impressively, topic maps provide a distinct separation between resources and concepts, thereby facilitating migration of the data models therein Topic map technologies are extensively employed to navigate databases of information in the fields of medicine, military, and corporations. Many of these proprietary topic maps are machine-generated through the use of context-specific algorithms which read a corpus of text, and automatically produce a set of topics along with the relationships among them. However, there has been little, if any, research on how to use cognitive notions of mental models, knowledge representation and decision-making processes employed in problemsolving situations as a basis for the design of ontologies for topic maps. This chapter will first outline the theoretical foundations in educational psychology and cognitive information retrieval that should underlie the development of ontologies that describe topic maps. The conjectural analyses presented will reveal how various modes of online interaction between key stakeholders (e.g., instructors, learners, content and graphical user interfaces), as well as the classic information processing model, mental models and related research on problem representation must be integrated into our current understanding of how the design of topic maps can better reflect the relationships between concepts in any given domain. Next, the chapter outlines a selective review of empirical research conducted on the use of topic maps in educational contexts, with a focus on learner perceptions and cognitions. Finally, the chapter provides comments on what the future holds for researchers who are committed to the development, implementation, and evaluation of topic map indexes in educational contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.027 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".