MétaCan
Menu
Back to cohort
Record W1899195099 · doi:10.24908/pceea.v0i0.3751

EASYDSP: PROBLEM-BASED LEARNING IN DIGITAL SIGNAL PROCESSING

2011· article· en· W1899195099 on OpenAlexafffundvenueabout
Kaveh Malakuti, Alexandra Branzan Albu

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsComputer scienceDigital signal processingSIGNAL (programming language)Signal processingComponent (thermodynamics)Problem-based learningMultimediaComputer hardwareMathematics educationPsychologyProgramming language

Abstract

fetched live from OpenAlex

This paper presents a pilot pedagogical project that combined for the first time problem-based learning (PBL) with a lecture-based instructional style in Digital Signal Processing I. This new course format was offered during the Spring 2008 term at the University of Victoria, Canada. The PBL component was implemented via EasyDSP, a custom-designed framework for system design and signal analysis.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.177
Teacher spread0.171 · 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 designSimulation or modeling
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
Published2011
Admission routes4
Has abstractyes

Explore more

Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicExperimental Learning in EngineeringFrench-language works237,207