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Record W3000366293 · doi:10.15460/eder.3.1.1388

Implementing an Intervention into a Grade Six Learning Environment: A Design-Based Research Framework

2020· article· en· W3000366293 on OpenAlexaffabout
Deborah Lambert, Michele Jacobsen

Bibliographic record

VenueEDeR Educational Design Research · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCurriculumPsychological interventionContext (archaeology)Intervention (counseling)Instructional designPedagogyDesign-based researchPsychologyMathematics educationMedical educationMedicine

Abstract

fetched live from OpenAlex

Educators in some Canadian schools are especially challenged in developing innovative pedagogical approaches that can intellectually engage their students in deep learning of core curriculum content, while equipping them with 21st century competencies. In this paper, we discuss some key ideas on how an intervention, the design and building of digital video games, was implemented and explored, to address this challenge, in four grade 6 social studies classes at a Calgary charter school, utilizing a design-based research framework. Findings revealed that: (i) to effectively implement this intervention in the classroom context, teachers needed to shift/modify their design of instructional activities compared to how they would normally design them in their social studies classes to teach the same chosen content; and (ii) the intervention, as implemented, seem to have the potential to be an effective innovative pedagogy for deep learning and one that promotes the intellectual engagement of students and their development and application of 21st century competencies. Some implications of these findings for the implementation of interventions in school, in terms of transforming the classroom environment, assessing the type of theoretical support needed, using design-based research as an effective framework to study how interventions and developing policy for the implementation of interventions are listed for K-12 educators, school jurisdictions and Alberta Education.

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)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0160.003

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 head0.442
GPT teacher head0.536
Teacher spread0.094 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations8
Published2020
Admission routes2
Has abstractyes

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