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Collaborative Learning in Problem Solving: A Case Study in Metacognitive Learning

2015· article· en· W2191751886 on OpenAlexaffvenue
Shelly L. Wismath, Doug Orr

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2015
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPsychologyCollaborative learningMetacognitionCollaborative writingPreferenceCooperative learningMathematics educationModalitiesGroup workPedagogyTeaching methodSociologyCognitionMathematics

Abstract

fetched live from OpenAlex

Problem solving and collaborative communication are among the key 21st century skills educators want students to develop. This paper presents results from a study of the collaborative work patterns of 133 participants from a university level course designed to develop transferable problem-solving skills. Most of the class time in this course was spent on actually solving puzzles, with minimal direct instruction; students were allowed to work either independently or in small groups of two or more, as they preferred, and to move back and forth between these two modalities as they wished. A distinctive student-driven pattern blending collaborative and independent endeavour was observed, consistently over four course offerings in four years. We discuss a number of factors which appear to be related to this variable pattern of independent and collaborative enterprise, including the thinking and learning styles of the individuals, the preference of the individuals, the types of problems being worked on, and the stage in a given problem at which students were working. We also consider implications of these factors for the teaching of problem solving, arguing that the development of collaborative problem solving abilities is an important metacognitive skill. La résolution des problèmes et la communication collaborative sont parmi les compétences clés que les éducateurs du XXIe siècle veulent que leurs étudiants acquièrent. Cet article présente les résultats d’une étude menée sur les modèles de travail collaboratif de 133 participants d’un cours universitaire conçu pour développer des compétences en matière de résolution des problèmes. La plupart des activités de classe de ce cours ont été consacrées à résoudre réellement des casse-tête avec un minimum de directives; les étudiants avaient la permission de travailler soit indépendamment soit en petits groupes de deux ou plus, selon leur préférence, et de passer de l’une à l’autre de ces modalités, comme ils voulaient. On a observé un modèle distinct dirigé par les étudiants eux-mêmes qui était un mélange de travail collaboratif et de travail indépendant, et ce dans quatre cours séparés offerts en quatre ans. Nous discutons un certain nombre de facteurs qui semblent être liés à ce modèle variable d’entreprise indépendante et collaborative, y compris les styles de réflexion et d’apprentissage des individus, les préférences des individus, les types de problèmes sur lesquels les étudiants ont travaillé et l’étape, lors de la résolution d’un problème donné, où les étudiants travaillaient. Nous prenons également en considération les implications de ces facteurs pour l’enseignement de la résolution de problèmes et nous discutons le fait que le développement de compétences pour la résolution de problèmes en collaboration est une compétence métacognitive importante.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0100.007
Scholarly communication0.0050.004
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.107
GPT teacher head0.411
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), 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

Citations57
Published2015
Admission routes2
Has abstractyes

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