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An Ontology-Based Approach for Curriculum Mapping in Higher Education

2021· article· en· W3146292709 on OpenAlex
Muthana Zouri, Alexander Ferworn

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
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOntologyComputer scienceCurriculumKnowledge managementAccreditationUpper ontologyOntology-based data integrationProcess ontologyKnowledge representation and reasoningSemantic WebDomain knowledgeData scienceWorld Wide WebArtificial intelligencePedagogySociology

Abstract

fetched live from OpenAlex

Programs offered by academic institutions in higher education need to meet specific standards that are established by the appropriate accreditation bodies. Curriculum mapping is an important part of the curriculum management process that is used to document the expected learning outcomes, ensure quality, and align programs and courses with industry standards. Semantic web languages can be used to express and share common agreement about the vocabularies used in the domain under study. In this paper, we present an approach based on ontology for curriculum mapping in higher education. Our proposed approach is focused on the creation of a core curriculum ontology that can support effective knowledge representation and knowledge discovery. The research work presents the case of ontology reuse through the extension of the curriculum ontology to support the creation of micro-credentials. We also present a conceptual framework for knowledge discovery to support various business use case scenarios based on ontology inferencing and querying operations.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.055
GPT teacher head0.297
Teacher spread0.242 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

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