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Record W7133026283

An Instructional Structure to Enhance Learning in Undergraduate Laboratories

2020· dissertation· W7133026283 on OpenAlexafffund
Kimia Moozeh

Bibliographic record

VenueTSpace · 2020
Typedissertation
Language
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsExperiential learningInstructional designEngineering educationKnowledge retentionEducational technologyValue (mathematics)Learning sciencesIntervention (counseling)
DOInot available

Abstract

fetched live from OpenAlex

Undergraduate laboratories are an important part of engineering and science curricula, as they have the potential to help students develop and improve many skills. Concerns, however, have been raised regarding the learning achieved in laboratories. This thesis provides a means of enhancing student knowledge in traditional undergraduate laboratories through the development and evaluation of an instructional structure. The instructional structure was developed based on modified Kolb’s experiential learning cycle, the expectancy-value theory of achievement motivation and multimedia design principles. Based on the instructional structure, web-based multimedia prelaboratory and postlaboratory exercises were developed which explain related theories, justify experimental procedures, and enable students to apply gained knowledge in new contexts. In addition, the exercises provided and demonstrated utility value of experiment-acquired knowledge. The instructional structure was developed and evaluated for two experiments in a second-year chemical engineering undergraduate laboratory course using an intervention cross-over methodology. Post-test case-control study design and surveys were used to assess the efficacy of the structure on student knowledge and motivation. Findings indicated that the prelaboratory exercises enhanced student knowledge through explanation of theories and rationale for experimental procedures. Further, this phase of conceptual understanding increased student motivation and encouraged them to be actively involved in learning while performing the experiment. Though results do not indicate a statistically significant increase in knowledge due to the postlaboratory exercises, students’ responses indicated that postlaboratories provided an opportunity to apply experiment-acquired knowledge to a new situation and integrate concepts learned in lecture with concepts learned in the lab course. There was also evidence that the instructional structure enhanced student breadth of knowledge. The instructional structure thus affords instructors a way to design prelaboratory and postlaboratory exercises to enhance student knowledge and motivation in undergraduate laboratories.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.343
Teacher spread0.335 · 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.

Study designSimulation or modeling
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

Citations0
Published2020
Admission routes2
Has abstractyes

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