MétaCan
Menu
Back to cohort
Record W2128015884 · doi:10.5539/jel.v1n2p268

Results of Using the Take-away Technique on Students’ Achievements and Attitudes in High School Physics and Physical Science Courses

2012· article· en· W2128015884 on OpenAlexvenueaboutno aff
James Carifio, Michael Doherty

Bibliographic record

VenueJournal of Education and Learning · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationClass (philosophy)Quarter (Canadian coin)PsychologyGroup (periodic table)Control (management)Physical sciencePhysicsComputer scienceGeography

Abstract

fetched live from OpenAlex

The Take-away Technique was used in High School Physics and Physical Science courses for the unit on Newtonian mechanics in a teacher (6) by grade level (4) partially crossed design (N=272). All classes received the same IE instructional treatment. The experimental group (classrooms) did a short Takeaway after each class summarizing the key concepts and points covered in the class, whereas the control group (classrooms) wrote a short evaluation of what they liked and disliked about the class. The experimental group performed better than the control group on the standardized Force Concepts Inventory achievement measure (Hestenes et al., 1992) using Hake normalized gain scores by a quarter to two-thirds of a standard deviation. Neither group showed the typical decline in attitudes towards Physics that occurs in IE approaches. The experimental group students gave the same positive benefits for the Takeaway technique as given in a previous study done with college undergraduates in a psychology course with those students in the control group citing much lower rates of these benefits and primarily emotional expression benefits. Given its design, the increased achievement observed in this study could be attributed directly to the Take-away technique as opposed to other rival hypotheses.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.075
GPT teacher head0.469
Teacher spread0.394 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Education and LearningSame topicScience Education and PedagogyFrench-language works237,207