Towards an Integrated Hardware And SOftware Book (HASOB)
Bibliographic record
Abstract
This paper describes a new concept of an integrated Hardware And SOftware Book (iHASOB).The proposed iHASOB platform aims at increasing subconscious or habit learning to supplement declarative learning 2,3 through the addition of hardware capabilities to electronic books that run on Tablet PCs.The iHASOB would integrate traditional text book information, hardware capabilities to collect external data relevant to the educational topic and software capabilities to provide simulations and analysis of the collected data.The proposed platform utilizes a tablet with enhanced capabilities through external hardware including data acquisition and sensors.iHASOB could be thought of as a new format for educational books, especially scientific or engineering books that may require additional modalities to explain abstract concepts such as filters for example.Such concepts may be better taught through simulations and display and manipulations of data from an accompanying measuring hardware.The paper discusses relevant pedagogical models and issues related to the development of an early prototype of the iHASOB concept with a focus on teaching programming of C language.The embodiment of the idea utilizes an iPad tablet interfaced to an Arduino microcontroller acting as data acquisition system interface to external sensors.Although the idea has not been utilized in an actual classroom at this point, the paper intends to share the concept of iHASOB and practical issues associated with the creation of the early prototype.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".