Design and Development of Cloud Learning Tool for English Sentence Patterns
Bibliographic record
Abstract
This study aims at providing Taiwanese students at different levels of study with a cloud language learning tool. The design of the learning tool can be divided into three stages. The first stage is the design of the curriculum. It combines paper learning log sheet with a simple computer program to help students write short essays and “tell” the stories. Findings of this study showed that students were highly motivated to make use of the computer learning tool to practice English sentence patterns and to create short essays. In the second stage, the researcher developed a cloud English learning tool that can be used in different types of computer facilities. This learning tool aims at helping students practicing English sentence patterns and at providing teachers with tools to monitor students learning process and performance. In the third stage, the researcher applied this computer learning tool to vocational high school students. The participants were two classes of high school seniors in New Taipei city, with 34 students in each class. One of the two classes was assigned as the experimental group and the other class the control group. The experimental group practiced English sentence patterns, using the cloud English learning tool. The control group was provided with paper handouts and was taught in a traditional way. The results showed a significant difference in performance between the two groups of students, with an F value 4.563 (p<.05). The experimental group had higher mean scores than the control group had. We may conclude that the cloud English learning tool can improve vocational high school students’ English writing skills. In the three-stage study, the researcher found that students at different levels of study used different types of computer facilities, for example, senior high school students used smart phones or notebooks, junior high school students used desktops in the computer lab, and elementary school students used learning log sheets to learn. Therefore, the researcher integrated HTML 5 and jQuery into a learning system and provided teachers with learning logs forms to download. That is, teachers can apply this learning system to students of different levels of study. The goal of this learning tool is to help students write short English essays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".