Automated Author ProfileXu, Zhe
Xu, Zhe
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: 8.2 (sum of 5 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This research tested eight hypotheses: H1:Perceived instructional support positively influences students' task performance in this context; H2: Perceived optimal challenge is positively associated with task performance in this context. H3:Perceived feedback is positively associated with task performance in this context. H4: Perceived relevance is positively associated with task performance in this context. H5: Flow state mediates the positive relationship between perceived instructional support and task performance. H6: Flow state mediates the positive relationship between perceived challenge and task performance.H7: Flow state mediates the positive relationship between perceived feedback and task performance . H8: Flow state mediates the positive relationship between perceived relevance and task performance. The excel file named "Engineering GBL Data" contains students’ self-reported feedback on their experience, flow state, and task performance after participating in the gamified bridge engineering course. For more details about the conceptual model, please read the research paper.
Authors
- Xu, Zhe ;
- Kim, Jinhee ;
- Yang, Guang ;
- li, na ;
- Clarke , Kathryn ;
- Zhao, Shiyu ;
- Zhang, Tao
This research tested eight hypotheses: H1:Perceived instructional support positively influences students' task performance in this context; H2: Perceived optimal challenge is positively associated with task performance in this context. H3:Perceived feedback is positively associated with task performance in this context. H4: Perceived relevance is positively associated with task performance in this context. H5: Flow state mediates the positive relationship between perceived instructional support and task performance. H6: Flow state mediates the positive relationship between perceived challenge and task performance.H7: Flow state mediates the positive relationship between perceived feedback and task performance . H8: Flow state mediates the positive relationship between perceived relevance and task performance. The excel file named "Engineering GBL Data" contains students’ self-reported feedback on their experience, flow state, and task performance after participating in the gamified bridge engineering course. For more details about the conceptual model, please read the research paper.
Authors
- Xu, Zhe ;
- Kim, Jinhee ;
- Yang, Guang ;
- li, na ;
- Clarke , Kathryn ;
- Zhao, Shiyu ;
- Zhang, Tao
Climatic fluctuations during the Pleistocene are usually considered as a significant factor in shaping intraspecific genetic variation and influencing demographic histories. To well-understand these processes in desert northwest China, we selected arid adapted Atraphaxis frutescens as the study species. Two cpDNA regions (psbK-psbI, psbB-psbH) were sequenced in 272 individuals from 33 natural populations across the range of this shrub, and 10 haplotypes were identified. It was found to contain high levels of total gene diversity (H T = 0.858), and low levels of within-population diversity (H S = 0.092). Analysis of molecular variance (AMOVA) indicates that genetic differentiation primarily occurs among groups of populations. Based on BEAST (Bayesian Evolutionary Analysis Sampling Trees) analysis, we suggest that intraspecific differentiation of the species, resulting from isolated populations, accompanied enhanced desertification during the middle and late Pleistocene. The expansion of the Gurbantunggut and Kumtag deserts in this area appears to have triggered divergence among populations of the western, central, and eastern portions of the region and shaped genetic differentiation among them. Two possible independent glacial refugia were predicted, the Ili Valley and the northern Junggar Basin. Extensive development of arid habitats (desert margin and arid piedmont grassland) coupled with a more equable climate because the early Holocene are factors likely to have generated recent expansion of A. frutescens.
Authors
- Xu, Zhe ;
- Zhang, Ming-Li
No description available
Authors
- Xu, Zhe ;
- Zhang, Ming-Li