A Linguistic Integrative Model for Enhancing College Students’ English Reading Competence
Bibliographic record
Abstract
This quantitative correlational research focused on investigating the relationship between linguistic technology-based integrative teaching approaches and college students’ reading competence. The study occurred in five phases. The first phase involved observing four reading classes to collect data on teachers’ teaching methodologies. The second phase was based on identifying the problems that affect students’ English reading performance. The researcher selected a random sample of 100 female freshmen students from the College of Languages and Translation at Al-Imam Mohamed Ibn Saud Islamic University (IMAMU Univ.), Riyadh, Saudi Arabia. The participants responded to a Likert questionnaire regarding their reading problems and strategies. In the third phase, the participants took a reading comprehension exam to determine their exact reading levels. The preliminary data showed the presence of a high degree at the scale of difficulties that students faced in reading comprehension. Students had problems in loud and silent reading, reading speed, and critical and inferential reading, which reflected students’ weak reading skills. The study also pointed to the ineffective traditional teaching strategies as the main cause of this problem. Traditional teaching strategies which depend on general lectures and explaining the mechanical structure of the reading passages did not help students use their cognitive abilities to improve their reading comprehension. The fourth phase of the present study required selecting an experimental group of 35students from the same sample to be taught using the linguistic integrative model for five weeks. At the end of the fifth week, a reading comprehension exam was given to the group to determine the impact of the new teaching methodology on students’ reading competence. The comprehension test was adopted from ACCUPLACER, an integrated computer-assessment designed to evaluate students’ reading skills. The test is designed by Board College in USA, which is a specialized agency in college students’ exams, and it offers diagnostics and intervention support to help students prepare for academic course work. The reading exam covers six skills, including: understanding the text’s purpose and tone; identifying the central ideas; recognizing supporting details; understanding sentences and vocabulary relationships; distinguishing illustration, comparison and contrast, and cause and effect relationships; and understanding inferential meanings. The data analysis showed a significant difference in favor of students who used the linguistic integrative model, indicating the positive impact of technology-based teaching approaches on students’ proficiency in reading. Based on the results of this study, the researcher made the following recommendations: integrate educational technology into teaching the reading courses at the college; provide professional programs for teachers to train them to use the linguistic integrative approaches; and provide linguistic laboratories that are equipped with modern technologies, including reading software, to intensify students’ reading practices. The significance of this study is that it is a contribution in the field of teaching English as a foreign language in general, and reading in particular since it provides a new model that integrates the technology of hypertexts, e-learning, and data mining analysis into a number of linguistic theories including schema theory, the information processing theory, and Krashen’s (1981; 1995) language theory. Providing teachers with training pertinent to the integration of technology into teaching is an important step towards implementing cognitive and metacognitive teaching methods, which will reinforce the efforts of the College of Languages and Translation towards achieving international accreditation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".