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Record W4239764877 · doi:10.4018/9781591401087.ch010

Values-Based Design of Learning Portals as New Academic Spaces

2011· book-chapter· en· W4239764877 on OpenAlexaffabout
Katy Campbell, Robert Aucoin

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLearning designComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

Many guidelines for portal design tend to focus on the technical aspects of a portal or a network. However, as we continue to define portals as gateways for learning, we need to consider issues related to the social and cultural context in which portals are used. In this chapter we examine learning portals from both the instructors’ and the learners’ perspectives by synthesizing existing research and proposing a framework for quality guidelines. The Collaborative of Online Higher Education Research (COHERE), consisting of eight large research-intensive universities in Canada involved in Internet-based learning, was created to enhance learning and teaching through technology and to move toward a stronger culture of professional collaboration and scholarship in our educational practices (Carey, 2000). Based on our experience with COHERE, we have developed tools for the formative and summative evaluations of learning portals generally. These tools include usability studies, questionnaires and focus groups.Request access from your librarian to read this chapter's full text.

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)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.008
Scholarly communication0.0170.012
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.057
GPT teacher head0.329
Teacher spread0.272 · 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 designTheoretical or conceptual
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 routes2
Has abstractyes

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