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Record W2923389761 · doi:10.18260/1-2--31253

WIP: Student and Faculty Experience with Blended Learning in a First-Year Chemistry for Engineers Course

2020· article· en· W2923389761 on OpenAlexafffund
Eline Boghaert, Jason Grove

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsBlended learningExperiential learningMathematics educationActive learning (machine learning)Computer scienceClass (philosophy)PaceEducational technologyMultimediaChemistryPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract WIP: Student and Faculty Experience with Blended Learning in a First-Year Chemistry for Engineers Course Chemistry for Engineers, is an introductory chemistry course taken by most engineering students at our university during their first term of study. Online content was developed to facilitate the implementation of a blended learning format, rather than traditional lecture format, for this course. The motivation for a blended approach was i) to create time for more valuable instructor–student interactions, allowing the instructor to reinforce challenging concepts, lead problem-solving workshops and lead experiential learning activities, and, ii) to allow students to explore content at their own pace, thereby accommodating the diversity of students’ high-school chemistry preparation. During Fall 2016, a blended learning format was piloted with three of the twelve sections of the course for half of the course content. Data from surveys administered throughout the term were combined with course grade data in an effort to compare and contrast student experience, satisfaction and performance between a blended learning and traditional lecture model of instruction. While the results from the Fall 2016 study are inconclusive due to challenges with survey administration and implementing the blended learning model, lessons were learned with respect to the readiness of the students for self-directed learning and the integration of the online and in-class components. During Fall 2017, five sections of the course are using a blended learning model for the entire course, implementing the lessons learned from the Fall 2016 study. In January, data from surveys administered throughout the term will be combined with course grade data, again in an effort to compare and contrast student experience, satisfaction and performance between a blended learning and traditional lecture model of instruction. This presentation will i) describe instructor experience in developing and deploying online content to support the blended learning, and, ii) compare and contrast student experience, satisfaction and performance between the two learning models.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.395
Teacher spread0.345 · 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 teacher head, 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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