Transcending the display size: the case for speech interaction in educational applications
Bibliographic record
Abstract
Speech is the most natural form of communication that humans employ, and is one of the main modalities through which we acquire and share knowledge. Moreover, speech is used not only to deliver knowledge, but as a modality that supports learning, such as student-teacher interactions around printed materials. During the past decade, we have witnessed significant advances mainly in preserving spoken educational materials, from informal how-to videos to full academic lectures being stored and available through a variety of online channels. Unfortunately, there is proportionately less research on enabling access to such multimedia knowledge repositories (e.g. searching, indexing) or on facilitating spoken, natural interaction between learners and digital interactive media (such as automated tutors or interactive learning resources). By enabling speech as a modality, learners become less constrained by the physical properties of the educational materials and can interact more naturally with the educational software, be it in the form of a mobile language assistant, a desktop-based online lecture browsing system, or a mixed-reality serious gaming system. In this paper I present examples of such recent research on improving the way we interact with educational resources through speech and natural language, and make the case for the need to conduct further research in this area.
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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".