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Record W1822986329 · doi:10.19173/irrodl.v1i2.22

Empire State College: The Development of Online Learning

2001· article· en· W1822986329 on OpenAlexvenueno aff
Patricia J. Lefor, Meg Benke, Evelyn Ting

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPlan (archaeology)Distance educationState (computer science)Online learningInstitutionMedical educationCenter (category theory)Professional developmentQuality (philosophy)Engineering managementPedagogyMathematics educationPublic relationsComputer sciencePsychologyPolitical scienceEngineeringMultimediaMedicine

Abstract

fetched live from OpenAlex

Empire State College was founded in 1971 to meet the needs of adult and other nontraditional student populations in the state of New York. Its initial delivery model was individualized instruction with a student working with a full-time faculty member to develop a unique plan of study and learning contracts to support that plan. By 1979, the College established the Center for Distance Learning, which developed and still offers structured, print-based courses to students with no requirement for on-site meetings. It began to experiment with computer-supported learning activities in the late 1980s, employing professional staff to support the exploration of technology and to provide assistance to faculty in instructional design. However, it was not until 1994, with the formal creation of the Center for Learning and Technology, that the development of online courses and programs was systematically pursued. This article outlines the development of online programs since that time, emphasizing the issues and challenges faced by the institution in seeking to provide quality, cost-effective distance education.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.004

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 head0.112
GPT teacher head0.488
Teacher spread0.376 · 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 designNot applicable
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

Citations11
Published2001
Admission routes1
Has abstractyes

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