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Record W7047361045

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

2018· other· en· W7047361045 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge UdeS (Institutional Deposit of the University of Sherbrooke) · 2018
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)Process (computing)Point (geometry)Set (abstract data type)Exploratory researchMisinformation
DOInot available

Abstract

fetched live from 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.

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)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.221
Teacher spread0.202 · 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 designObservational
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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