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Record W1559328834

The Role of Information Technology in Technology-Mediated Learning: A Review of the Past for the Future

2006· review· en· W1559328834 on OpenAlexaff
Zeying Wan, Yulin Fang, Derrick J. Neufeld

Bibliographic record

VenueJournal of the Association for Information Systems · 2006
Typereview
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsInformation technologyDimension (graph theory)Process (computing)Knowledge managementEducational technologyComputer scienceLearning sciencesPsychologyMathematics educationMathematics
DOInot available

Abstract

fetched live from OpenAlex

Technology-mediated learning refers to an environment in which the learner's interactions with learning materials, peers, and/or instructors are mediated through information technologies (Alavi and Leidner, 2001). The objective of this paper is to review current research on technology-mediated learning using a theoretical framework derived from the existing literature. The framework presents three dimensions (primary participant, instructional design, and information technology) that influence students' psychological learning processes, and eventually lead to different learning outcomes. The literature review reveals that certain relationships identified by this framework have received significant attention (e.g., the influence of a technology feature on learning outcomes), while others have been ignored (e.g., the influence of IT on psychological processes). Research questions that can help advance our understanding of technology-mediated learning are discussed.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.007
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.299
Teacher spread0.290 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations91
Published2006
Admission routes1
Has abstractyes

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