NotesKB: Web-Based Semantic Annotation Tool for Online Multimedia Learning Content
Bibliographic record
Abstract
Due to the rapid growth of e-Learning contents, especially videos, published online, the usability of tools that help students keep track of such contents along with their thought traces is an issue.We addressed the problem by designing and building a web-based semantic annotation tool called NotesKB.Through technical and theoretical lenses, we aim to discover and improve upon the usability of such tools.The usability of NotesKB is evaluated and compared with two other predominant note-taking methods, namely Pen and Paper (PnP) and Microsoft OneNote.According to the SUS test scores, PnP is the most usable while NotesKB was the most preferred when students were interviewed.We have reaffirmed that cognitive load is key to design e-Learning systems and that note-taking tools with semantic capabilities need to help users learn how to use the greater power available.viii Dr. Biddle, I cannot thank you enough for accepting to co-supervise me half-way of my program and devoting an enormous amount of your valuable time -including weekends and holidays -just so that I finish writing my thesis.You have provided me with a first-hand experience of what a rigorous research is.I am also thankful to the members of my committee, Sonia Chiasson, Ali Arya, and the defense chair Anil Somayaji for their guidance, for their expertise, and for offering different perspectives, all of which have helped me shape this dissertation.I would like to give special thanks to Ms. Erenia Oliver for
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".