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Record W2886987417 · doi:10.24908/pceea.v0i0.10401

REFLECTIONS FROM TEACHING ASSISTANTS IN COMBINED LEARNING ASSISTANT AND COURSE GRADER ROLES

2018· article· en· W2886987417 on OpenAlexafffundvenue
Natasha Lanziner, Hannah Smith, David R. Waller

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's UniversityStrong
KeywordsTeaching assistantCourse (navigation)Perspective (graphical)Reflection (computer programming)Medical educationPsychologyComputer sciencePedagogyMedicineEngineering

Abstract

fetched live from OpenAlex

Abstract – Integrating the roles of learning assistants and course graders into a single teaching assistant position can be beneficial both to students and to teaching assistants. A critical reflection of teaching assistant experience in undergraduate design courses was undertaken. Benefits to students include meaningful, individualized guidance in assessment, the communication of expectations and concepts from a unique perspective, and increased approachability. The teaching assistants also benefit from practical experience in teaching and mentoring, gaining skills in leadership and communications. However, integrating learning assistant and course grader roles creates a more challenging position, requiring deep understanding of course content and communication strategies to be successful. This insight may be used to improve or develop design courses using the combined model and to help prepare teaching assistants for this demanding but rewarding role.

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)0.011
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0070.003
Open science0.0020.005
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0050.003

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 head0.015
GPT teacher head0.334
Teacher spread0.319 · 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 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".

Quick stats

Citations1
Published2018
Admission routes3
Has abstractyes

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