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Record W2281060867

Using Story-based Virtual Experiment to Help Students Building Their Science Process Skills

2010· article· en· W2281060867 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEdMedia: World Conference on Educational Media and Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsAthabasca University
Fundersnot available
KeywordsProcess (computing)Computer scienceFocus (optics)Mathematics educationVirtual labVirtual LaboratoryMultimediaPsychology
DOInot available

Abstract

fetched live from OpenAlex

Science Process Skills are what students use to solve scientific problems. This research designs a virtual experiment environment (V.E.E.) to help students building their science process skills by doing virtual experiments. To motivate students practicing their science process skills, this research designs virtual experiment which integrates story elements into the virtual experiment environment and asks students solving the problem described in the story. Students solve the problem by following the Seven Problem Solving Stages. Each stage helps students practicing different science process skills, e.g. observing, formulating hypothesis, and classifying. To reduce teachers working loads, teachers only need to focus on designing the story backgrounds and establishing the connections between story elements and Physics experiment elements. The virtual experiment environment can automatically generate the virtual experiments in which the seven problem solving stages are embedded. A practical system with a complete example is demonstrated at the end of this paper.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.307
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it