Bibliographic record
Abstract
Two pathways for the future of educational design in computer simulation technology are proposed in this chapter. Design in this context refers to both the design of the simulation and its interface and the design of pedagogic activities that integrate this technology. In this chapter, the author will: 1) present a classification scheme for digital technologies in education and determine where computer simulations fall within this scheme, 2) offer comparisons of different types of computer simulations in chemistry education and their external representations, 3) put forward new design directions on the use of non-standard external representations, such as analogic representations and, 4) draw upon three empirical studies to outline how such analogic computer simulations can be used productively in the chemistry classroom with a non-traditional pedagogic design. The first two studies investigate a simulatation with a dynamic analogy, and a third study investigates how a technology-enhanced approach to instruction contributes to student understanding of chemistry in the classroom. Research on this simulation and the instructional approach might be of interest to educators and developers who are exploring the future of design for computer simulations within classroom environments.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".