An Agent for Web-based Structured Hypermedia Algorithm Explanation System
Bibliographic record
Abstract
Abstract: Studying and understanding algorithms is important for all computer scientists. Over two decades of research has been devoted to improving algorithm visualization and algorithm explanation techniques. Knowledge gained from these practices allows us to design and implement logically correct programs with considerations to runtime and memory constraints. For many students, learning algorithms in a traditional manner (i.e. using text-books) is challenging. We have developed an alternative approach to teaching algorithms called the Structured Hypermedia Algorithm Explanation (SHALEX) system, which uses hypermedia and represents algorithms as an abstract tree structure. Although SHALEX is a fully functioning teaching tool, currently it does not provide a way of receiving feedback on student’s progress. To address this problem, this paper extends SHALEX with intelligent agent to monitor student progress, to provide the student with hints where necessary and to record the results of student interaction, all of which provide a means of quantifying the level of understanding the student has achieved. The system is implemented as a web-based application using the client-server architecture. This allows students to learn algorithms through both distance education and in the classroom setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".