Praxistemology: Early childhood education, engineering education in a university, and universal concepts for people of all ages and abilities
Bibliographic record
Abstract
Existential tinkering as a form of inquiry must be brought into the engineering curriculum at the university level, as well as into the education curricula in general, including early childhood education. This paper presents a methodology of education for people of all ages and abilities, including engineering education, through unstructured play, personal involvement (authenticity), expression, and exploration - playful tinkering - as forms of inquiry. Current methods of engineering education have too much emphasis on structure, creating rigidity that destroys the capacity for creativity and radical innovation and invention. We introduce “existinquiry/praxistemology” (existential tinkering as inquiry) as a learning methodology consisting of three parts: learning by thinking, learning by doing, and “learning by being” (existential education). The goal of this learning methodology is to create lateral thinkers who integrate ideas and methodologies normally associated with play, the arts, and the sciences, into the the creative thinking process of engineering and design. Our hope is that (1) existinquiry in engineeing education will create more competitive and versatile thinkers capable of solving more sophisticated problems; and (2) that combining concepts of engineering education with concepts of unstructured play that are normally associated with early childhood education, will result in more groundbreaking inventions. We playfully explore topics of Veillance (surveillance, sousveillance, reciprocal transparency, equiveillance/omniveillance, uberveillance, and dataveillance) and Natural User Interfaces with the fundamental Elements (earth, water, air, etc.). The methodologies are applicable to teaching engineering to children or adults of any age or ability.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".