English Digital Dictionaries as Valuable Blended Learning Tools for Palestinian College Students
Bibliographic record
Abstract
Digital technology has become an indispensible aspect of foreign language learning around the globe especially in the case of college students who are often required to finish extensive reading assignments within a limited time period. Such pressure calls for the use of efficient tools such as digital dictionaries to help them achieve their academic purposes. Efficient use of such tools, however, would not be possible unless students are familiar with their special features so as to use them to their maximum advantage. This paper investigates the extent to which Palestinian college students are familiar with the features and advantages of different dictionary types especially digital ones. It also probes the effect of using certain activities on the students' level of awareness and dictionary use. A structured interview and a test were applied on a group of 148 Arts and Science students at two local universities. The interview measured the purposes (skills) that these dictionaries are used for and the domains they are used in. At a later stage in the experiment, these students were involved in an eight week activity that required them to use an online dictionary on a daily basis to complete an assignment. After the experiment, it was found that the frequency of dictionary use declined in the case of the print dictionary and increased remarkably in the case of the electronic (CD-Rom) and online dictionaries. It was also found that exposing students to the authentic language available in electronic dictionaries notably enhanced the students' overall language proficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".