The Effect of Digital Dialogued Journaling on Improving English Writing: A Linguistic Communicative Approach
Notice bibliographique
Résumé
Writing is a complex process that requires advanced linguistic skills. Although many college students studied English as a foreign language (EFL) for twelve years in preparatory and high schools, they still face major problems in producing correct writings that meet their colleges’ requirements. Students’ problems include inability to generate ideas, organize discourse, control sentence structures, choose appropriate vocabulary, and use effective styles. A potential solution to such problems can be found in the application of modern technologies in the classrooms. Telecommunication technologies which include synchronous and asynchronous communication have provided various tools that can be used to assist EFL students to learn writing skills. Therefore, the current quantitative, quasi-experimental study aimed at examining the effect of asynchronous communication, specifically digital dialogued journaling on students’ writing skills. Digital dialogued journaling includes blogs, webpages, discussion forms, or word-processed applications such as Google documents. Using the platform of Google documents, the present study attempted to provide new strategies for teaching writing courses at higher education to help EFL students develop their writing skills. Data was collected from undergraduate students in the College of Languages and Translation, at Al-Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia. Data collection depended on a number of instruments: First, a pretest was used to measure the participants’ level of writing before implementing the teaching strategies of dialogued journaling. Secondly, an online dialogued journal, designed by the researcher using Google documents, was employed for the experiment. The journal was sent to the same sample via emails, and the participants posted their reflective writings on different issues regarding their academic journey learning English. Students’ interactive dialogues included prose writing, descriptive and argumentative paragraphs, poetry, and their personal stories. The students-teacher dialogues made the corpus data which enabled investigating the effectiveness of dialogued journaling on improving students’ writing. Thirdly, a posttest was used to collect data regarding the degree of change that occurred as a result of the experiment. Fourthly, a Likert scale questionnaire was used at the end of the experiment to identify the participants’ levels of satisfaction with dialogued journaling. Data analysis was based on using the Analysis of Variance (ANOVA) to compare the results of pretest and posttest. A rubric with five scale criteria was used to examine each rank of students’ writing, and to report each student’s score before and after treatment. The Text Analyzer Software was also employed to examine the participant’s writing lexical density and phrase frequencies. Data analysis results indicated a significant statistical difference between the overall writing scores of the pretest and the posttest. Moreover, the examination of the participants’ writing revealed much improvement in writing styles, word choice, and the student’s voice, which are critical factors in writing. Hence, the significance of the current study is that it provides a new technological tool, such as Google document, for teaching writing skills at higher education. This study includes an instructional model that incorporates digital journaling into teaching English writing. The present research is also a contribution in the field of teaching English, adopting the communicative approach by integrating theories of connectives and constructivism into linguistic theories.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,009 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».