Work in Progress Pilot Study: Virtual Reality for Computational Thinking Foundations and STEM Enrichment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the pilot study of a web-based desktop virtual reality (VR) instructional framework used to teach computational thinking (CT) concepts to secondary students.Classroom CT instructional practices are vastly underexplored in research on adolescent beginning programmers.Training in computational thinking, requires a firm grasp of various components ranging from fundamental aspects.The study's objective was to create a VR platform consisting of four VR learning modules to teach data types, conditionals, loops, and operators.Each module developed one CT topic with engaging interactive activities, animated models, and games with built-in self-assessment.This paper details the modules' development, deployment, and outcomes related to the use of the VR modules within a science and math enrichment camp focused on learning engineering design and coding.The study assessed student use of the four CT topics in their final design project-a coded personal reflection.A lack of the fundamental understanding of CT concepts is a critical factor in STEM attrition rates as CT skills are highly interconnected to various branches of engineering and technology.So, we employ a CT perspective to deliver essential skills related to STEM concepts to facilitate skills transfer including problem solving and critical thinking.Students' final projects were analyzed including a block-coded animation or app in code.org and a written summary of the project, as well as an "artist statement" that was required to relate the CT topics to the project's program.Data analysis is still underway.Early conclusions indicate that explicit development of each CT topic was useful for project success if the coding platform also scaffolded coding using identical language as the modules (for loops to for loops, for example.)Potential impacts of this study include recommendations for introducing CT topics to high school beginning coders.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it