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Record W2138772265 · doi:10.24908/pceea.v0i0.4825

Use of a reflection journal in a third year engineering project course

2013· article· en· W2138772265 on OpenAlexaffvenue
A. L. Steele

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsCarleton University
Fundersnot available
KeywordsRubricCapstoneCapstone courseReflection (computer programming)Mathematics educationProcess (computing)Course (navigation)Computer scienceWork (physics)PsychologyPedagogyMedical educationEngineeringMedicineMechanical engineering

Abstract

fetched live from OpenAlex

As part of a new third year project course for the Electrical Engineering program, a reflection journal was introduced as part of the work to be undertaken by students. The aim of the one term course is to provide a project experience that will provide design experience in teams, will draw together material from the previous years of academic study as well as further prepare students for their capstone project. The reflection journal has been introduced to provide a regular opportunity for the student to consciously reflect on their progress, challenges encountered, as well as a way to develop their writing skills. This is an attempt to encourage students to look at the process of learning in a project environment and to develop some degree of metacognition1. By undertaking this type of reflection Cowan [1] suggests that this assists students from looking at solving a particular challenge to generalizing the problem solving process, fitting with the objectives of aproject course. The entries for the journal are weekly and are assessed each week by an instructor and contributed to 15% of the final mark. Because this form of assessment would be new to most of the students instructions were provided including a rubric. These instructions as well asthe instructor’s experiences and opinion of the success of the journal will be presented.

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)0.007
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 score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.022
GPT teacher head0.308
Teacher spread0.286 · 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 designObservational
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

Citations2
Published2013
Admission routes2
Has abstractyes

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