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Record W3036235929 · doi:10.24908/pceea.vi0.14206

Student-centered Teaching Assistance Method for Engineering Students

2020· article· en· W3036235929 on OpenAlexaffvenue
Maryam Miriestahbanat, Sareh Majidi Ivari, Yousef R. Shayan

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsConcordia University
Fundersnot available
KeywordsMathematics educationClass (philosophy)Variety (cybernetics)Constructivist teaching methodsComputer scienceTeaching methodConstructivism (international relations)PsychologyPedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

• The student-center method is designed to address the noticeable learning demands, interests, desires, or cultural backgrounds of individual students and groups of students.• This method includes a wide variety of educational programs, learning experience, educational techniques, and academic support procedures.• In this method, teachers, schools, guidance counselors, and other educational specialists apply different educational techniques such as modifying the educational strategies in the classroom and assignments.• One example of the educational strategy in the classroom is redesigning the way in which students are grouped and taught in a class.• The student-centered method can improve independent problem-solving skills.• In this method, which is based on the constructivist learning theory, the students are participated in choosing what they will learn and how their learning will be evaluated.• Applying this technique, especially in classes with a low number of students, provides an opportunity for the students to deeply analyze the conceptual and mathematical problems through mutual communications.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score1.000
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.006

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 head0.016
GPT teacher head0.311
Teacher spread0.296 · 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 designNot applicable
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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Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicProblem and Project Based LearningFrench-language works237,207