The Effect of Digital Dialogued Journaling on Improving English Writing: A Linguistic Communicative Approach
Bibliographic record
Abstract
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.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".