WIP: Assessing 21st Century Skills of High School Students in a Machine Learning Workshop with TinyML
Bibliographic record
Abstract
This research WIP paper describes a case study on the introduction of machine learning concepts in K-12 Education, providing students with more direct contact with new artificial intelligence technologies and potentially promoting an approach to foster the so-called 21st century skills, which enable students to deal with the technological world available in today's society. Working on skills such as Learning and Teamwork (LTE), Communication (COM), Citizenship and Social Responsibility (CSR), and proficiency in Information and Communication Technologies (ICT) may enable K-12 students to deal with work requirements that will be increasingly needed within the marketplace. In this study, we evaluated the perception of first-year high school students, between 15 and 17 years old, from a Brazilian public school regarding their own mastering of 21st century skills. The results show us how much such perceptions can be influenced by contact with knowledge of new technologies, even at an introductory level. In our case study, we used the TinyML framework, promoting an initiation to machine learning with the use of small hardware devices and minimizing the use of programming to simplify understanding and increase student motivation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 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".