Investigating the Adaptation of Saudi High School Students to Electronic Dictionaries as Language Learning Tools
Bibliographic record
Abstract
Following the COVID-19 pandemic, the traditional education system has been moved to alternative online solutions worldwide. This research aims to uncover the experiences of Saudi secondary school students in using electronic dictionaries as an assistive language learning tool in the Madrasati online learning platform for English. Mixed methods research is employed to understand students’ experiences, knowledge, expectations, and thoughts about the electronic dictionaries they used during the COVID-19 crisis and the sudden and unplanned movement to online teaching tools in their language learning and practices. A total of 145 male students enrolled in a secondary school in the Ar-Rass educational directorate were asked to respond to the questionnaire, and 5 of them were randomly chosen to participate in the semi-structured interviews. Findings showed that a majority of the participants dislike the dictionary currently available on the Madrasati platform. They stated that they either favored using free dictionaries available on their mobile phone app stores or other online dictionaries. They consulted their dictionaries mainly to check the meanings of the new words because as compared to other language skills, they engaged more in reading. The data showed that a majority of the students neither sought the help of their teachers about the unknown words nor their friends. They also thought that the pandemic drastically altered their style of learning. Data also showed some disadvantages, difficulties, and concerns of using electronic dictionaries during the virtual classes through Madrasati.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".