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Record W1790138082 · doi:10.1558/cj.v30i0.137-165

The design of effective mobile-enabled tasks for ESP students: A longitudinal study

2013· article· en· W1790138082 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCALICO Journal · 2013
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsGeorge Brown CollegeAthabasca University
Fundersnot available
KeywordsDesign-based researchComputer scienceContext (archaeology)MultimediaHuman–computer interactionMathematics educationPsychology

Abstract

fetched live from OpenAlex

This paper describes and reports on the findings of the Enactment Phase of a longitudinal Design Based Research (DBR) study aiming to develop effective design principles for learning materials for English for Special Purpose (ESP) students, enabled by means of mobile devices. The process of data collection and analysis over an eighteen-month period, resulted in a conceptual model and design principles for a mobile-enabled language learning (MELL) solution. The study also generated a broader understanding of the context-embedded nature of ESP learning using mobile devices, specifically the role of aspects of the whole learning environment, ultimately contributing to real-life praxis of the Ecological Constructivist framework and the complementary approach of DBR methodology. This paper focuses on the intervention design and development completed during the Enactment phase (Phase 2). The key outcome of this phase, namely the prototype of the Mobile-Enabled Language Learning Eco-System (MELLES), encompassed eight ESP tasks accessible through a mobile-web portal which served as a gateway to the MELLES network. The design of the MELLES intervention and its constituent tasks are presented.

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.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.622
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.023
GPT teacher head0.327
Teacher spread0.304 · 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