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Managing Innovation in Teaching in ODDE

2022· book-chapter· en· W4285065311 on OpenAlex
Tony Bates

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

VenueHandbook of Open, Distance and Digital Education · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCompetition (biology)Open innovationBusinessFace (sociological concept)Higher educationKnowledge managementPublic relationsMarketingPolitical scienceEconomic growthSociologyEconomicsComputer scienceSocial science

Abstract

fetched live from OpenAlex

Abstract Innovation is the lifeblood of open, distance and digital education (ODDE), but it has often proved difficult for ODDE institutions to continue to innovate in response to a changing world outside. Innovation though is not “magic” or serendipitous. There are well-established methods by which innovation can be nurtured and managed in ODDE. Following a literature review of innovation in ODDE, the chapter discusses common myths regarding innovation, several barriers to change in ODDE institutions, then offers five strategies to support innovation. A case study is provided that illustrates a number of factors that support sustained innovation in ODDE, and the chapter ends by suggesting that innovation is not an end in itself but is best managed by focusing on the major, long-term goals of ODDE, and the main challenges that ODDE institutions face. In particular, ODDE institutions need to remain innovative to meet increasing competition from the conventional higher education institutions and above all from large, digital technology companies.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.879
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.372
Teacher spread0.318 · 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