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Record W2902684084 · doi:10.1080/1475939x.2018.1544587

Teacher facilitation support in ubiquitous learning environments

2018· article· en· W2902684084 on OpenAlexaff
Alex Mottus, Kinshuk Kinshuk, Nian‐Shing Chen, Sabine Graf, Uthman Alturki, Ahmed Aldraiweesh

Bibliographic record

VenueTechnology Pedagogy and Education · 2018
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsAthabasca University
FundersDeanship of Scientific Research, King Saud UniversityUniversity of North Texas
KeywordsUsabilityDashboardComputer scienceHuman–computer interactionSystem usability scaleMultimediaProcess (computing)FacilitationInterface (matter)Scale (ratio)Usability engineeringData sciencePsychology

Abstract

fetched live from OpenAlex

Teacher support for students in ubiquitous learning environments is challenging owing to physical distance and a lack of reliable real-time multimedia-rich communication. This is further complicated because the learning process is dynamic and problems need quick resolution. Given these challenges, this study proposes an architecture for a teacher facilitation support system. The system was implemented in the form of an interactive teacher dashboard. The interface also generates possible solutions to learning challenges while leaving the ultimate decision up to the teacher. The system feasibility was tested by work-through scenarios designed and validated by two experts. The system usability was evaluated using the System Usability Scale by 40 potential users. The findings revealed that the dashboard has good feasibility and usability for providing teachers relevant information about their students’ learning progress in ubiquitous learning environments. It also enabled opportunity for pedagogical intervention when needed.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.285

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 head0.011
GPT teacher head0.319
Teacher spread0.309 · 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 designOther design
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

Citations8
Published2018
Admission routes1
Has abstractyes

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