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Record W4379795417 · doi:10.5430/wjel.v13n6p362

New Perspectives in Utilizing Non-Textbook Resources in EFL Classrooms and Perceptions

2023· article· en· W4379795417 on OpenAlexvenueno aff
Naeem Afzal

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
FundersPrince Sattam bin Abdulaziz University
KeywordsBoredomPerceptionMathematics educationContext (archaeology)PsychologySubject (documents)Computer sciencePedagogySocial psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

Usually, most textbooks are formally prescribed with fixed and time-bound content. There is hardly any choice for course teachers/instructors to deviate from the prearranged course content of such textbooks. This study assumes that such formal, skills and knowledge-based, textbooks may cause boredom in EFL classrooms with repeated tasks/exercises and, even sometimes, their course content may not be interesting to all students. On the contrary, the use of non-textbook resources in EFL classrooms can have multiple contributions, for instance, by enhancing students’ competency and proficiency in language skills and by reinforcing their knowledge. Thus based on such assumptions, this study investigates university teachers’ perceptions on incorporating non-textbook recourses as supplementary materials in teaching EFL to non-native undergraduate speakers, in the selected context. To achieve these goals, this quantitative study uses an online questionnaire as a data collection tool. The questionnaire seeks university teachers’ responses/perceptions on the subject under investigation. The findings of this study, given the university teachers’ perceptions, reveal various useful roles and contributions of non-textbook resources. To cite, for instance, the use of non-textbook resources can trigger students’ participation, increases motivation and learning possibilities, enhances their understanding and performance, breaks monotony and boredom, contextualizes teaching-learning situations, and provides students with space for extra language skills practice.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.019
GPT teacher head0.335
Teacher spread0.317 · 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 designQualitative
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

Citations2
Published2023
Admission routes1
Has abstractyes

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