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Record W2280844005

Using a concept mapping software as a knowledge construction tool in a graduate online course

2003· preprint· en· W2280844005 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

VenueR-libre (Université Téluq) · 2003
Typepreprint
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversité TÉLUQ
Fundersnot available
KeywordsComputer scienceCourse (navigation)Software engineeringSoftwareSoftware analyticsSoftware developmentData scienceSoftware constructionHuman–computer interactionKnowledge managementProgramming languageEngineering
DOInot available

Abstract

fetched live from OpenAlex

Stemming from a twenty-month pedagogical experience using a concept mapping software for higher education students in an online course, this paper reports findings from what became an exploratory study. The objectives were to support the students' knowledge construction process and to stimulate metacognitive reflection. After having read some instructional texts, students used an object-oriented modeling tool (called MOT) to graphically represent a network of at least fifteen knowledge units of their choice. They also had to "explain" their concept map in a narrative format. Based on questionnaire data, comments expressed spontaneously by students in the online forums, and the analysis of their concept maps, the following themes are discussed: (1) students' attitudes toward concept mapping, (2) how they executed the concept mapping task, and (3) characteristics of the maps produced. In conclusion, some research issues are outlined.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.150
GPT teacher head0.393
Teacher spread0.243 · 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