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Enregistrement W2765477943 · doi:10.18260/1-2--19844

Investigating the Impact of Model Eliciting Activities on Development of Critical Thinking

2020· article· en· W2765477943 sur OpenAlexaff
Jake Kaupp, Brian Frank, Ann Shih-yi Chen

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation and Critical Thinking Development
Établissements canadiensQueen's University
Organismes subventionnairesnon disponible
Mots-clésComputer science

Résumé

récupéré en direct d'OpenAlex

Abstract Investigating the Impact of Model Eliciting Activities on Development of Critical ThinkingModel eliciting activities (MEAs) are realistic problems used in the classroom that requirelearners to document not only their solution to the problems, but also their processes for solvingthem. MEAs have been developed and used in a variety of subject areas, including mathematics,economics, and environmental engineering. Studies have shown MEAs to be valuable in helpingstudents to develop conceptual understanding, knowledge transfer, and generalizable problem-solving skills.MEAs have been integrated into a first-year undergraduate engineering course at a medium-sizedCanadian university. Students in this course are asked to work collaboratively on three differentMEAs, each introduced in a three- to four-week cycle. While each MEA requires students toemploy different areas of subject knowledge, students are taught to approach all three MEAsusing critical thinking skills. For example, students are guided to draw concept maps, questionthe credibility of information sources, incorporate a range of factors into their decision-making,and consider the implications of their conclusions. These skills are what Paul (2006) calls“elements” of critical thinking—invaluable thinking processes involved in any complexproblem-solving activity.A research team has been formed at the university to investigate the impact of the MEA-integrated course on students’ development of critical thinking skills. Ultimately, the team aimsto determine whether the MEA-integrated course facilitates students’ critical thinking. Presently,the team has developed two mini-MEAs to be used as pretest and posttest instruments. They aresimilar to the three MEAs introduced in the course, in that they are set in realistic contexts, butthey are simpler, in that students do not need to do any modeling in labs and are given readingmaterial that is shortened and directly relevant to issues embedded in these MEAs. The paperpresented discusses a pilot study the team conducted on one of the mini-MEAs, which we callmini-MEA A.The purpose of this pilot study was twofold: first, to explore thinking processes involved insolving mini-MEA A; and second, to develop a standard procedure for identifying and evaluatingthinking processes involved. The method employed for eliciting students’ thinking processes wascalled the think-aloud, in which involve participants think aloud while solving a problem. Afterthis the researcher analyzes their verbal and written products, which are known as “think aloudprotocols.” Three upper-year engineering students who had no exposure to the MEA-integratedcourse were randomly selected for the study. Prior to signing consent forms, all three participantswere briefed about the purpose and procedure of the study. The entire think-aloud session lastedfor about one hour, was video-taped, and transcribed and annotated.The research team divided students’ think aloud protocols into five segments. Each segmentconsisted of a particular issue with which the group tackled. Drawing on Paul’s theoreticalframework for critical thinking, the research team found that while students did display criticalthinking in each segment, the quality of this thinking could be greatly improved. For example,the group was able to make reasonable safety recommendations, but the students made severalrecommendations without critically examining their own assumptions or those of the informationsources provided to them. In this presentation, the research team will show several examplesdrawn from the think aloud protocols and discuss how Paul’s theoretical framework can be usedto evaluate students’ thinking processes. In addition, the research team will discuss theadvantages and disadvantages of using mini-MEAs as pretest and posttest tools for investigatingthe impact of MEAs on students’ critical thinking skills.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,024
score de la tête « metaresearch » (Gemma)0,213
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,129

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0240,213
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0020,004
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,125
Tête enseignante GPT0,409
Écart entre enseignants0,284 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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