STEAM-GAAR Field Learning Model to Enhance Grit
Bibliographic record
Abstract
The STEAM-GAAR Field Learning Model to Enhance Grit is a learning model using STEAM education (Science, Technology, Engineering, Art and Mathematics) integrated with gamification (G), animation (A), augmented reality (AR) and space utilization (Field) to promote the factors that enhance a learner’s grit. The purpose of this research is to: (1) synthesize a STEAM-GAAR Field Learning Model to Enhance Grit; (2) evaluate the STEAM-GAAR Field Learning Model to Enhance Grit. The sample is made up of ten specialists–two instructional model design specialists, two STEAM education specialists, two gamification specialists, two animation specialists, and two augmented reality (AR) specialists–selected by purposive sampling. The instrument used in the study was an evaluation form with regard to the Model. The statistics used in the study were arithmetic mean and standard deviation. The results show that: (1) a STEAM-GAAR Field Learning Model to Enhance Grit contains four elements. The first element relates to input factors including 1) Expected Learning Outcomes (ELO), 2) Learning Objectives, 3) Teacher Analysis, 4) Student Analysis, 5) Content Analysis, 6) Environment Analysis, and 7) The Learning Management Plan. The second element relates to the STEAM-GAAR Field Learning Process, including 1) Investigate by Game, 2) Discover by AR-Game, 3) Connect by Animation and Game, 4) Create by Game Animation and AR and 5) Reflect by Knowledge Exchange Field. The third element relates to evaluating learning achievement and grit, conducted via teachers’ observation and an evaluation form. And the final element relates to an analysis of feedback; (2) All ten specialists agreed that the STEAM-GAAR Field Learning Model to Enhance Grit developed through this study demonstrates the highest level of appropriateness ( x= 4.65, S.D. = 0.57).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".