Automated Author ProfileVimolchaya Yanasugondha
Vimolchaya Yanasugondha
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 5.2 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This study examined the effectiveness of the application of the three types of coding, which were L2 L1 (Thai) translation, pictorial, and in particular simultaneous L2 L1 and pictorial—Dual Coding Theory (DCT), employing 36 low frequency seven-letter English concrete nouns among 58 Thai EFL tertiary students who were at the beginning level. The investigation looked at the effectiveness of each type of coding in terms of the recognition rate (working memory) after three spaced presentations and the retention rate (long-term memory) among participants. The analyses were done by one way ANOVA. The findings suggested that the simultaneous L2 L1 and pictorial coding (DCT) group outperformed the other two groups both in the immediate posttest and the one-month delayed posttest, but no statistically significant differences were found among the three groups.
Authors
- Vimolchaya Yanasugondha