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Enregistrement W7047361045

Examining factors that can impact conceptual learning in first-year cegep chemistry

2018· other· en· W7047361045 sur OpenAlexaboutno aff

Notice bibliographique

RevueKnowledge UdeS (Institutional Deposit of the University of Sherbrooke) · 2018
Typeother
Langueen
DomainePhysics and Astronomy
ThématiqueMagnetic confinement fusion research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMeaning (existential)Process (computing)Point (geometry)Set (abstract data type)Exploratory researchMisinformation
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Knowledge consists of interrelated concepts that encompass truths, information, and principles that enable a person to construct meaning about the world in a unique way. Learning is a complex, cumulative process by which students add new pieces of information by interpreting them from the vantage point of their preexisting ideas and beliefs. Addressing students’ misconceptions in science courses at all levels of instruction has become a major concern for science educators since incomplete, faulty concepts are major hurdles for attaining future effective learning. This exploratory study has multiple goals: It aims to identify misconceptions in chemistry held by first-semester CEGEP students, to investigate how instruction can influence their conceptual learning, and to analyze if gender and language of instruction in high school are significant predictors of conceptual gains. To identify misconceptions in chemistry and investigate how instruction can influence conceptual learning, 332 first-semester CEGEP Science students in an Anglophone college in Montreal (male:female ratio = 0.83) who were divided in 11 cohorts took the Chemistry Concept Inventory (CCI), a well-researched instrument that contains 22 multiple-choice conceptual questions that test students’ knowledge about basic high-school level material. The CCI was administered twice: as a pretest (before receiving instruction) to detect misconceptions brought from high school and as a posttest (after instruction) to provide information about the changes resulting from instruction. The case-study design involved a treatment group and 10 control groups, which were taught the same material by different instructors. The independent variable was the learning activity, which is either a series of computer simulations that provided visualization of chemical phenomena to students (the treatment), or traditional lecture format for the delivery of the material (the control). The treatment involved three computer simulations that were carried out by groups of students in class. These simulations are available on the website of the Phet Interactive Simulations from the University of Colorado at Boulder. Hake normalized learning gain, based on the differences between pre- and posttest scores, was calculated for each student and for each cohort. The mean of this gain for the whole sample was 6.1%, and the treatment group had the highest gain, 12.1%. The pretest score is a significant predictor (R2adjusted = 0.562, F(7,324) = 61.73, p < 0.01) of posttest score, which indicates that students who already knew chemistry concepts at the beginning of the course did better than those holding multiple misconceptions. No statistically significant difference was observed between test scores and the language of instruction in high school. Although neither the cohort nor the treatment was a significant predictor of posttest scores, the results indicate a gender gap in which the treatment is significant for males (R2adjusted = 0.497, F(5,145) = 30.65, p = 0.00685) but not significant for females. This means that males benefit from the treatment significantly more than females do. One-way ANOVA showed gender as a significant predictor of scores in most of the items in both pretest (14 of 22 questions, F(1, 330) = 5.19, MSE = 0.25, p < 0.024) and posttest (19 of 22 questions, F(1, 330) = 8.42, MSE = 0.25, p < 0.004) with males outperforming females. The combined data indicate the existence of a gender gap in introductory college chemistry, a feature that was not reported in previous studies with the CCI. The comparison between the misconceptions held by first-year CEGEP students with those reported for American first-year undergraduates enabled the identification of the most challenging concepts for which measured learning gains were either negligible or not observed. This study indicates that concepts dealing with the microscopic scale and size of atoms, the distinction between the physical and chemical properties of aggregate matter compared to the properties of its molecular constituents, as well as the energy changes in the formation and breaking of chemical bonds are among the most challenging concepts detected with the CCI. The results emphasize the difficulties faced by learners related to the triplet representation in chemistry whose understanding requires the distinction between the particulate model used to describe matter, the understanding of chemical and physical properties displayed in laboratory experiments, and the symbolic representations used to describe phenomena. The trends reported extensively for American undergraduate students regarding the types of identified misconceptions and the magnitude of the normalized learning gains align with those found in this study, which indicates the validity of the CCI as a tool to analyze conceptual learning under the specific characteristics of Quebec’s CEGEP system. The crafting of lesson plans that engage learners in the transfer of key concepts and ideas to new settings is paramount to helping them learn chemistry more effectively by selecting an appropriate model in each specific context. This study sheds light on critical issues related to curriculum development by attempting to map the conceptual landscape of first-year CEGEP students and by analyzing the effect of instruction in learning gains. The indication that classroom practices based on computer simulations might be beneficial for enhancing conceptual learning deserves further investigation. The findings of this study can be used to guide fruitful pedagogical discussions among chemistry teachers who are interested in aligning students’ pre-knowledge, instruction, curriculum, and assessment.

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)0,002
score de la tête « metaresearch » (Gemma)0,016
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,975
Score d'incertitude au seuil0,049

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,016
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,019
Tête enseignante GPT0,221
Écart entre enseignants0,202 · 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

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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