ENWIC: VISUALIZING WIKI SEMANTICS AS TOPIC MAPS - An Automated Topic Discovery and Visualization Tool
Bibliographic record
Abstract
knowledge management systems, Intelligent visualization tools Abstract: In this paper, we present ENWiC (EduNuggets Wiki Crawler), a framework for intelligent visualization of Wikis. In recent years, e-learning has emerged as an appealing alternative to traditional teaching. The effectiveness of e-Learning is depended upon the sharing of information on the web, which makes the web a vast library of information that students and instructors can utilize for educational purposes. Wiki’s collaborative authoring nature makes it a very attractive tool to use for e-Learning purposes; however, its text-based navigational structure becomes insufficient as the Wiki grows in size, and this backlash can hinder students from taking full advantage of the information available. ENWiC’s goal is to provide student with an intelligent interface for navigating Wikis and other similar large-scale websites. ENWiC make use of graphic organizers to visualize the relationships between content pages so that students can gain a cognitional understanding of the content as they navigating through the Wiki pages. We describe ENWiC’s automated visualization process, and its user interfaces for students to view and navigate the Wiki in a meaningful manner, and for instructors to further enhance the visualization. We also discuss our usability study for evaluating ENWiC’s effectiveness as a Wiki Interface. 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".