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Record W3216041875 · doi:10.5430/elr.v10n4p42

Exploring a Rural English Teacher’s Lived Experiences of Assessment Practices in a Blended Learning Enactment: A Narrative Inquiry

2021· article· en· W3216041875 on OpenAlexvenueno aff
Haris Sugianto

Bibliographic record

VenueEnglish Linguistics Research · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsBlended learningNarrativeContext (archaeology)PedagogyPortfolioPsychologyReflective practiceSociologyMathematics educationEducational technologyBusiness

Abstract

fetched live from OpenAlex

Although a large number of studies have put a focus on the enactment of blended learning in English as a foreign language (EFL) classroom, there is a paucity of research into the teacher’s lived experiences of how they enact assessment in the blended learning activities. To fill such a gap, this paper reports on a narrative inquiry of an EFL teacher’s lived experiences of conducting assessment during blended learning in the pandemic era. The finding of the study shed light on the ineffectiveness of the assessment practice during the blended learning enactment, particularly in the context of rural schools. Albeit the participating teacher in this study was fully engaged to conduct assessment from his past experiences, two major problems hinder such a practice: students’ unsubmitted assignments and poor Internet connection. Based on these findings, teachers are encouraged to find an alternative assessment practice during the blended learning, portfolio assessment can be an option. This suggestion is anchored by the fact that the assessment practice was not technically supported during the blended learning activities.

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)0.003
metaresearch head score (Gemma)0.116
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.116
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.314
GPT teacher head0.428
Teacher spread0.114 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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