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Record W2966292470 · doi:10.1075/itl.19009.hei

A longitudinal observation of technology-mediated feedback for L2 learners of German

2019· article· en· W2966292470 on OpenAlex
Trude Heift

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

VenueITL Review of Applied Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGermanGrammarComputer scienceCorrective feedbackRepetition (rhetorical device)Set (abstract data type)Mathematics educationFocus on formFocus (optics)Language acquisitionSecond-language acquisitionComputer-Assisted InstructionLinguisticsPsychologyMultimediaProgramming language

Abstract

fetched live from OpenAlex

Abstract This article provides a longitudinal study of L2 learners of German who used a computer-assisted language learning (CALL) system that formed part of their regular classroom instruction. The 42 learners were enrolled in four consecutive university language courses at a beginner and intermediate level. The study compares two different feedback types, metalinguistic feedback and repetition, which were provided for the same exercise type over the course of four semesters. The exercise type required learners to build sentences from a set of predefined, uninflected words. While the grammatical focus of the exercises changed over time, many of the same grammatical constructions were present in all four courses. The study discusses the changes in learner performance and error correction behavior as students became more proficient in their knowledge of the L2 grammar and were exposed to the technology-mediated feedback that remained consistent throughout system use over the four language courses.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.038
GPT teacher head0.293
Teacher spread0.255 · 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