The Effects of Paper-based Portfolios and Weblog-based Electronic Portfolios on Limited English Proficiency Students in Writing for Service Industry Course
Bibliographic record
Abstract
The purposes of this study were to investigate the effects of the use of paper-based and weblog-based electronic portfolios on the writing achievement of limited English proficiency students, to survey the students’ attitudes towards the use of the portfolio assessment, and to compare the viewpoints of the students in the control and experimental groups. The study was conducted with 60 second-year hotel and tourism students enrolled in the Writing for the Service Industry course. They had limited English proficiency, as their previous English grades were C or below in average. The simple random sampling technique was used for subject selection and group assignment. Google’s free weblog website (located at www.blogger.com) was used as a tool for creating and developing the students’ personal electronic portfolios. At the beginning of the course, the students in the control group and the experimental group were trained in the concept of portfolios, and the purposes, content, and criteria used for assessment were discussed with the students. A writing achievement test and a closed-ended questionnaire were used for the quantitative data collection, while the qualitative data were gathered from the open-ended questions, interviews, and reflection. Descriptive statistics and t-tests were employed for the data analysis. It was found that the effects of the use of paper-based portfolios and weblog-based electronic portfolios on the writing achievement were not significantly different, but some promising results of the use of weblog-based electronic portfoliosfor language learning and assessment are indisputable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".