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Record W1554184790 · doi:10.22329/celt.v5i0.3442

29. Discovery, Integration, Communication, and Engagement: Learning Through Scaffolds in a Field-Based Course

2012· article· en· W1554184790 on OpenAlexaffvenue
Thomas Yates

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2012
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMathematics educationField (mathematics)Critical thinkingStudent engagementTeaching methodCourse (navigation)Active learning (machine learning)Discovery learningPsychologyComputer sciencePedagogyEngineering

Abstract

fetched live from OpenAlex

A field-based course in an applied science program can have numerous learning outcomes. These are typically addressed through demonstration, active participation by the students, communication between students and instructor and amongst students, and independent work by students individually or in small groups. Such courses are also opportunities for students to develop their critical thinking. The author’s experience is that teaching techniques used to deliver field courses are generally inherent and based on the experience of the instructor and the teaching culture within the academic unit. These techniques are typically not drawn from the pedagogical literature, although they do have similarities to such established concepts such as scaffolds. Recognition of teaching concepts drawn from the pedagogical literature and their application to the design and teaching of field-based courses may improve the delivery of course material and provide a better student experience. Thinking and teaching in terms of the support that scaffolds represent may also smooth the transition from classroom to outdoors back to classroom. Supported learning based on established teaching methods will improve a student’s opportunity for Discovery, Integration, Communication and Engagement.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.034
GPT teacher head0.376
Teacher spread0.342 · 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.

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

Citations1
Published2012
Admission routes2
Has abstractyes

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