A Linguistic Integrative Model for Enhancing College Students’ English Reading Competence
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».