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Record W2018500069 · doi:10.4018/jdet.2010040101

Technological Supports for Onsite and Distance Education and Students' Perceptions of Acquisition of Thinking and Team-Building Skills

2010· article· en· W2018500069 on OpenAlexaff
Jennifer Thomas, Danielle Morin

Bibliographic record

VenueInternational Journal of Distance Education Technologies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsConcordia University
Fundersnot available
KeywordsBlackboard (design pattern)Higher-order thinkingPerceptionKnowledge managementDistance educationSkills managementComputer sciencePsychologyMathematics educationTeaching methodPedagogy

Abstract

fetched live from OpenAlex

This paper compares students’ perceptions of support provided in the acquisition of various thinking and team-building skills, resulting from the various activities, resources and technologies (ART) integrated into an upper level Distributed Computing (DC) course. The findings indicate that students perceived strong support for their acquisition of higher-order thinking skills and team-building skills from the offline resources, but moderate support from the online resources and technologies provided in the course, which was in opposition to the grades received. It also seems that those in the traditional computer lab setting perceived online resources as more supportive of higher-order thinking skills than those in other sections and those in the electronic classroom perceived the least support. The results were mixed for team-building skills and for offline resources support for higher-order thinking skills. In particular, distance students deemed the text and material in Blackboard less important for developing these skills than onsite students.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.356
Teacher spread0.351 · 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 designObservational
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

Citations10
Published2010
Admission routes1
Has abstractyes

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