TangiBooks: Design and Creation of Paper-Based Tangibles with Embedded Electronics for Teaching Programming Concepts
Bibliographic record
Abstract
The growing need for programming and computational skills has led to a demand for teaching approaches that can appeal to a broader audience. We explore the design and creation of tangible objects and interactions to make introductory programming concepts more engaging and less tedious. We introduce TangiBooks, a platform that uses paper-based tangibles with embedded electronics and augments paper with sensory interactions like visuals, sounds, haptics, and kinesthetics to reinforce learning. The platform is implemented as proof-of-concept and offers four self-contained lessons on key concepts such as algorithms, variables, conditionals, and loops. Results from our observational study (12 adult learners, 6 instructors) showed that participants found TangiBooks to be playful, appealing to their senses, and valuable for sparking curiosity, with potential pedagogical benefits for promoting reflection among learners. TangiBooks has implications for HCI researchers in furthering the design space of paper-based tangibles in the learning domain and empowering instructors to innovate and personalize lessons.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".