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Record W2914421903 · doi:10.5539/elt.v12n3p108

Attitude of Male and Female Students Towards Computer Assisted Language Learning at Intermediate Level

2019· article· en· W2914421903 on OpenAlexvenueno aff
Muhammad Arfan Lodhi, Ayesha Fatima, Farzana Ismail, Nadia Amin, Faiza Khalid, Ayesha Siddiqa

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyStatisticLanguage acquisitionMathematics educationDescriptive statisticsPedagogy

Abstract

fetched live from OpenAlex

Today’s world is the era of technology and it is playing a dominant role in the field of education. The current research work is quantitative in nature. It aims to investigate students’ attitudes, their interests and difficulties regarding the concept of computer assisted language learning at intermediate level. A self reported questionnaire (SRQ) was designed and administered to obtain the objectives of the current study. The sampling statistic comprised of 300 students with equal gender from public and private colleges. The collected data was statistically analyzed by running descriptive statistic technique. The findings revealed that male students had more positive attitude towards CALL as compared to their female counterparts. However, it was also revealed that male students found CALL interesting for the development of language proficiency while female students faced more difficulties in using computer technology for English Language learning. The results provide ideas, paths and suggestions to the future researchers to undergo further investigations in developing computer mediating language learning programs for the benefits of learners and learning.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.041
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.023
GPT teacher head0.277
Teacher spread0.254 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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