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Record W2927046832

Development of Scaffolding Indicators from Archival Problem-Based Learning Online Discussions of Distance and Blended Undergraduate Science Students

2018· article· en· W2927046832 on OpenAlexaff
Michael-Anne Noble

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsDistance educationBlended learningContext (archaeology)Mathematics educationCoding (social sciences)ScaffoldMultidisciplinary approachComputer scienceAsynchronous communicationContent analysisOnline discussionEducational technologyMultimediaPsychologyWorld Wide WebMathematicsSociology
DOInot available

Abstract

fetched live from OpenAlex

This study used an open coding approach to content analysis of online problem-based learning (PBL) asynchronous online discussion posts from an undergraduate ecotoxicology course to evaluate the following research questions: What types of scaffolds are used most by students? Are there differences in use across students from distance and blended courses? Is there evidence in the discussion posts that the scaffolds used by students support their critical thinking? The course is taught in the context of a multidisciplinary B.Sc. in Environmental Science Program. The unit of analysis for the content analysis will be the words/phrases in the discussion posts. A coding scheme was developed to capture the use of two types of learning scaffolds - instructor and group organization scaffolds, and citations learning scaffolds (source materials). This study highlights differences in distance and blended student use of learning scaffolds during their PBL case problem discussions, as well as how differences in the primary communication medium between distance and blended teams appear to influence their use.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.341
Teacher spread0.314 · 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 designQualitative
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

Citations0
Published2018
Admission routes1
Has abstractyes

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