MétaCan
Menu
Back to cohort
Record W1993032993 · doi:10.2190/ax3r-a8t1-h5a3-810h

Assessing Technology Enhanced Instruction: A Case Study in Secondary Science

2000· article· en· W1993032993 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

VenueJournal of Educational Computing Research · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsInstitute for Christian StudiesUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)Mathematics educationComputer scienceSituatedTechnology integrationQualitative propertyQualitative researchScalabilityPedagogyMedical educationPsychologyEducational technologySociologyMedicine

Abstract

fetched live from OpenAlex

The paper describes an evaluation program designed to assess the effectiveness of Technology Enhanced Instruction (TEI). The study is situated within the context of the Technology Enhanced Secondary Science Instruction (TESSI) project, a seven-year, field-based research program of technology integration into secondary science (grades 9–12). Evaluation procedures include analyses of student enrollment and achievement, teacher-researcher reports, an independent ethnographic assessment, the project's scalability, and interviews with graduates from the program. Taken together, these evaluations of TESSI support claims that TESSI is a scaleable and reproducible model of successful TEI implementation, which encourages greater student enrollment and retention in senior science electives (i.e. greater success for more students), and prepares students for post-secondary education and the realities of an information-based workplace. The effectiveness of the project's implementation of technology is supported by both quantitative and qualitative data.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.528
Teacher spread0.442 · 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