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Record W4386912009 · doi:10.5539/elt.v16n10p21

On-site and Online Classroom Activities and Thai EFL Learners’ Language Anxiety before and during the COVID-19 Pandemic

2023· article· en· W4386912009 on OpenAlexvenueno aff
Sirinan Nuypukiaw, Wichuta Chompurach

Bibliographic record

VenueEnglish Language Teaching · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAnxietyCoronavirus disease 2019 (COVID-19)PandemicMedical educationSession (web analytics)Data collectionReading (process)Academic yearMathematics educationPedagogyMedicineSociology

Abstract

fetched live from OpenAlex

The present study has been conducted in order to study the classroom activities and the levels of learning anxiety of the third-year English majors both who show high academic performance (HAP) and low academic performance (LAP), before and during the COVID-19 pandemic. The researchers have compared the different levels of the students’ anxiety and the effects of classroom activities on the students during both periods. The participants of the study were 114 third-year English majors from two public universities in Thailand: 1) Southern University (SU) and 2) Northeastern University (NEU) (the university names used are pseudonyms). The number of HAP students was 57, and the remaining 57 students showed LAP. To collect the data, the researchers adopted a questionnaire and an in-deph interview as data collection tools. The questionnaire was administered to 114 students, and four of whom voluntarily participated in the interview session - two of whom held HAP while the other two had LAP. The results showed that before the pandemic, both HAP and LAP students similarly experienced the activities in the aspect of participation mode activity most often. The least often experienced activities for those with HAP were speaking activities, but ones for the LAP students were reading activities. During the pandemic, the HAP students most often experienced the participation mode activities while those with LAP experienced all types of classroom activities at a moderate level. However, the two groups were similar in that they experienced speaking activities the least. In terms of the level of English learning anxiety, both groups similarly had overall learning anxiety at a high level both before and during the pandemic. However, the data revealed that their levels of English learning anxiety before the outbreak were statistically significantly different, but the two groups had the similar levels of learning anxiety during the pandemic. In terms of the effects of classroom activities on the English majors, the data showed that the activities used before the pandemic including speaking activities (i.e., oral presentations and reading pronunciation) and taking exams (scheduled exams and pop-up quizzes) affected both groups similarly. This was because they were nervous and excited about being in front of the class while doing speaking tasks and they were worried about their marks when taking exams. However, two activities including 1) being unexpectedly called on and 2) group work had different effects on the two groups. That means only LAP students experienced anxiety when being randomly called on while group work only increased the learning anxiety of those with HAP. In addition, the activities causing both groups to have learning anxiety during the pandemic were doing group work, being called on randomly, online listening, and doing activities with evaluations. The data revealed the two causes of the anxiety that were 1) the problems about online communication and being unacquainted with other members when doing group work and 2) their unfamiliarity with the foreign teachers’ accent and pronunciation. In addition, the results revealed online presentations can reduce both groups’ learning anxiety while online examinations could reduce only LAP English majors’ learning anxiety.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.334
Teacher spread0.316 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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