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Record W4391963531 · doi:10.18260/1-2--38139

Work in Progress: Determining a Mathematical Model to Study the Relationship Between Pedagogical Strategies and the Attainment of Student-learning Outcomes

2024· article· en· W4391963531 on OpenAlex
Kuldeep Rawat, Chandra Asthana

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsConcordia UniversityLockheed Martin (Canada)
Fundersnot available
KeywordsProcess (computing)Computer scienceClass (philosophy)Mathematics educationWork (physics)Linear modelController (irrigation)Control (management)Artificial intelligencePsychologyEngineeringMachine learning

Abstract

fetched live from OpenAlex

In this paper, a linear dynamic model for the Student Learning Outcomes is developed that is derived from the performance of students in weekly tests in a college course.The motivation behind developing such a model is to be able to study the dynamic behavior of the teaching and learning process and develop pedagogical strategies analogous to a controller used in a physical system control design.The linear model is based on the average performance of the class.It is shown that the same method can be used to obtain the model for an individual student if the instructor wishes to use a particular pedagogical strategy to improve the student's performance.The method of computing the linear model and its features are described.The desired design goals of a controller representing the pedagogical strategies employed by the instructor are also discussed.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.957

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.171
GPT teacher head0.383
Teacher spread0.212 · 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