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Record W2096189331 · doi:10.19173/irrodl.v7i2.294

Adaptation for a Changing Environment: Developing learning and teaching with information and communication technologies

2006· article· en· W2096189331 on OpenAlexvenueno aff
Adrian Kirkwood, Linda Price

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyAdaptation (eye)Context (archaeology)ScholarshipKnowledge managementEducational technologyProfessional developmentDistance educationInformation technologyHigher educationPedagogyLearning environmentSociologyEngineering ethicsPublic relationsPsychologyComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

This article examines the relationship between the use of information and communication technologies (ICT) and learning and teaching, particularly in distance education contexts. We argue that environmental changes (societal, educational and technological) make it necessary for Higher Education in general, and distance education in particular, to adapt those systems and practices that are not appropriate for the changing environment. However, the need to adapt is often perceived by academic and support staff to be primarily technology-led and with a requirement for them to develop their technical skills in using ICT. We provide a critique of continuing professional development (CPD) in respect of using ICT for teaching and learning that does not address these wider environmental changes. In particular, we oppose CPD that concentrates on the individual teacher and their use of ICT. Instead, we contend that professional development should focus upon the scholarship of teaching and learning and a concern for understanding the characteristics and needs of learners. Further, CPD should reflect the organisational context within which ICT is managed and produced and so it must involve institutional managers as well as individual academics and associated staff members.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.052
GPT teacher head0.412
Teacher spread0.361 · 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 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

Citations87
Published2006
Admission routes1
Has abstractyes

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