全文获取类型
收费全文 | 846篇 |
免费 | 20篇 |
专业分类
教育 | 579篇 |
科学研究 | 84篇 |
各国文化 | 11篇 |
体育 | 111篇 |
文化理论 | 22篇 |
信息传播 | 59篇 |
出版年
2023年 | 7篇 |
2022年 | 11篇 |
2021年 | 12篇 |
2020年 | 20篇 |
2019年 | 34篇 |
2018年 | 35篇 |
2017年 | 53篇 |
2016年 | 50篇 |
2015年 | 31篇 |
2014年 | 38篇 |
2013年 | 159篇 |
2012年 | 31篇 |
2011年 | 26篇 |
2010年 | 39篇 |
2009年 | 27篇 |
2008年 | 41篇 |
2007年 | 18篇 |
2006年 | 22篇 |
2005年 | 21篇 |
2004年 | 23篇 |
2003年 | 16篇 |
2002年 | 21篇 |
2001年 | 10篇 |
2000年 | 9篇 |
1999年 | 3篇 |
1998年 | 4篇 |
1997年 | 6篇 |
1996年 | 7篇 |
1995年 | 5篇 |
1994年 | 6篇 |
1993年 | 6篇 |
1992年 | 8篇 |
1991年 | 7篇 |
1989年 | 8篇 |
1988年 | 5篇 |
1987年 | 3篇 |
1986年 | 4篇 |
1985年 | 2篇 |
1984年 | 2篇 |
1982年 | 4篇 |
1980年 | 2篇 |
1978年 | 4篇 |
1974年 | 2篇 |
1969年 | 2篇 |
1957年 | 3篇 |
1954年 | 1篇 |
1944年 | 3篇 |
1927年 | 1篇 |
1866年 | 1篇 |
1860年 | 1篇 |
排序方式: 共有866条查询结果,搜索用时 125 毫秒
861.
Pearson S Hume P Slyfield D Cronin J 《Sports biomechanics / International Society of Biomechanics in Sports》2007,6(1):71-80
The reliability of grinding performance was assessed in 18 current Emirates Team New Zealand America's Cup sailors in two test sessions separated by 5 h using a custom-built ergometer. Sixteen different grinding conditions that varied by load (Light 39 N x m, Moderate 48 N x m, Heavy 68 N x m), deck heel (Flat 0 degrees control, Downhill 25 degrees, Uphill 25 degrees, Right 25 degrees, Left 25 degrees), and grinding direction (forwards, backwards) were assessed using peak power and external work over 5 s during maximal-effort 8-s grinds. Reliability statistics included the difference in mean (M(diff)), standard error of measurement (SEM), and intraclass correlation coefficients (ICC). External work (SEM = 1.6-6.9%; ICC = 0.91-0.99) was a more consistent performance measure than peak power (SEM = 1.3-9.6%; ICC = 0.84-0.99) across all test conditions. Testing under different load conditions resulted in external work SEMs of 1.6-3.9% with performance more reliable in lighter load conditions. Grinding performance during different heel conditions was less reliable (external work SEMs = 4.6-6.9%). Grinding direction (forward or backward) did not appear to affect performance reliability, although external work was 10-15% higher in forward grinding. Reliability is acceptable across various loads, but testing under different heel conditions may need some protocol development to allow the detection of smaller differences in performance. 相似文献
862.
863.
864.
Kirsty Kitto Ben Hicks Simon Buckingham Shum 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1095-1124
An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.
Practitioner notes
What is already known about this topic
- ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
- Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
- Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.
What this paper adds
- An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
- A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
- An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.
Implications for practice and/or policy
- Causal models can help us to explicitly specify educational theories in a testable format.
- It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
- Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.
865.
866.
Marina Klimovich Simon P. Tiffin-Richards Tobias Richter 《Journal of Research in Reading》2023,46(2):123-142