Saudi (EFL) Learners’ Attitudes Toward Google Sites E-portfolio Systems in Relation to Their Language Learning Motivation
Bibliographic record
Abstract
Electronic portfolios have emerged as an essential element of e-learning systems in the Saudi educational system, particularly in higher education. Google Sites e-portfolios enable prompt sharing of assignments and foster reflection and collaboration among students and instructors. The aim of this study is to investigate Saudi female EFL students’ attitudes toward the Google Sites e-portfolio system in relation to language learning motivation. The sample comprised 138 EFL female Saudi students. The participants were enrolled in an English advanced course and were required to submit an e-portfolio as part of a course assessment. A quantitative research approach was employed. Data were collected through questionnaires. Descriptive statistics, means, standard deviation, and correlation were conducted. The study revealed that the learners showed positive attitudes toward the ease of use of Google’s e-portfolio system. Furthermore, they perceived it as a valuable tool, recognizing its usefulness in facilitating timely submissions, effective feedback, and reflective learning. The study also found a positive correlation between learners’ attitudes toward the system and their language learning motivation; a stronger correlation was observed for instrumental motivation. The study highlighted the significant educational value of Google e-portfolios and indicated possibilities for educators to enhance learners’ experiences by tackling current obstacles and promoting motivation. Educators should encourage the use of the Google Sites e-portfolio system for reflective practices and highlight its useful features. They should also focus on students’ language learning motivation, which affects attitudes toward technology implementation. Educational institutions should enhance students’ comprehension of the system through focused instruction, workshops, and peer assistance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".