The Future Nexus of Computational Thinking Education: A Preliminary Systematic Review of Reviews
Bibliographic record
Abstract
Recent years have seen a high volume of computational thinking (CT) review studies. However, there have been no existing studies that map these reviews with the goal of achieving comprehensive understanding of the field of CT. This paper utilizes Tikva & Tambouris’ (2021) K-12 CT research domain conceptual model as the basis for identifying and defining CT reviews, then maps the identified 38 CT reviews onto the identified domains. We pinpoint eight potential future review topics, including "communities" of tools, "modeling simulations," "problem-solving" and "scaffolding" of learning strategies, "demographic attributes" of factors, "practices" and "perspectives" of the knowledge-based areas, and the "teacher training" of capacity building. We also examine the topical keywords of the reviews and identify that the scope of the term "unplugged" is vaguely defined among the existing research, suggesting a need to refine the definition of this frequently discussed topic so as to be able to more effectively conduct supplementary reviews. Our results help to better understand the CT review field and formulate future directions.
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 | 0.024 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".