“These two worlds are antithetical”: epistemic tensions in integrating computational thinking in K12 humanities and arts
Bibliographic record
Abstract
Background While advocates for integrating Computational Thinking (CT) into existing K12 classrooms have acknowledged and aimed to address various barriers to implementation, we contend that a more foundational issue – tensions between the epistemology of computing and those of existing disciplines – has largely been overlooked. Studies of contact between heterogeneous disciplinary perspectives in both pedagogical and real world professional settings point to other risks, and harms, that educators may need to consider as they attempt to integrate CT into their teaching. As such, designing for integrated CT pedagogies does not simply require addressing functional problems such as teacher professional learning and limited classroom time, but rather implicates complex epistemological navigations.Objective This manuscript explores epistemic tensions between Computational Thinking (CT) and K12 humanities and arts disciplines and possibilities for their resolution.Method Based on a Delphi study with 43 experts from three disciplines – language arts, social studies, and arts – as they engaged in 20 hours of focus group conversations exploring potential approaches to integrating CT these disciplines, analysis focused on identifying perceived epistemic tensions that can arise in the context of instruction and directions for their resolution.Findings We found 5 epistemic tensions that are explored in detail: contextual reductionism, procedural reductionism, epistemic chauvinism, threats to epistemic identities, and epistemic convergence, as well as a number of potential directions for navigating them.Implications The study’s findings provide insights that bear on both scholarship and pedagogical design aimed at promoting substantive interdisciplinary learning with CT, and, critically, navigating potential tensions that can arise within it.
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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 | 0.053 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.071 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.005 | 0.009 |
| 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".