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Record W2782636258 · doi:10.1109/tale.2017.8252344

Impact of outcome-based education on software engineering teaching: A case study

2017· article· en· W2782636258 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsDouglas College
Fundersnot available
KeywordsOutcome-based educationCurriculumOutcome (game theory)Computer scienceSoftwareEngineering educationMathematics educationQuality (philosophy)Software engineeringEngineering managementEngineeringPsychologyPedagogyProgramming language

Abstract

fetched live from OpenAlex

This paper investigates the impact of outcome- based education (OBE) on students' learning achievement from a software engineering (SE) program. It is not easy to transform an SE curriculum from traditional knowledge-based education (KBE) method to OBE method since it requires us to identify the outcomes clearly and map the outcomes with the expected capabilities of students. We first give a briefing on our SE program and outline the curriculum, then investigate the impact of OBE in two selected courses in SE program, with the completion of one course being the prequisite for admission into the other one. Experimental results show that OBE can greatly improve the learning effectiveness of students and teaching quality.

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)0.007
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 score0.072
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.076
GPT teacher head0.499
Teacher spread0.424 · 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

Quick stats

Citations17
Published2017
Admission routes1
Has abstractyes

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