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831.
Kate Talsma Andrew Chapman Allison Matthews 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(6):1917-1938
Predictors of academic success at university are of great interest to educators, researchers and policymakers. With more students studying online, it is important to understand whether traditional predictors of academic outcomes in face-to-face settings are relevant to online learning. This study modelled self-regulatory and demographic predictors of subject grades in 84 online and 80 face-to-face undergraduate students. Predictors were effort regulation, grade goal, academic self-efficacy, performance self-efficacy, age, sex, socio-economic status (SES) and first-in-family status. A multi-group path analysis indicated that the models were significantly different across learning modalities. For face-to-face students, none of the model variables significantly predicted grades. For online students, only performance self-efficacy significantly predicted grades (small effect). Findings suggest that learner characteristics may not function in the same way across learning modes. Further factor analytic and hierarchical research is needed to determine whether self-regulatory predictors of academic success continue to be relevant to modern student cohorts.
Practitioner Notes
What is already known about this topic- Self-regulatory and demographic variables are important predictors of university outcomes like grades.
- It is unclear whether the relationships between predictor variables and outcomes are the same across learning modalities, as research findings are mixed.
- Models predicting university students' grades by demographic and self-regulatory predictors differed significantly between face-to-face and online learning modalities.
- Performance self-efficacy significantly predicted grades for online students.
- No self-regulatory variables significantly predicted grades for face-to-face students, and no demographic variables significantly predicted grades in either cohort.
- Overall, traditional predictors of grades showed no/small unique effects in both cohorts.
- The learner characteristics that predict success may not be the same across learning modalities.
- Approaches to enhancing success in face-to-face settings are not automatically applicable to online settings.
- Self-regulatory variables may not predict university outcomes as strongly as previously believed, and more research is needed.
832.
Mohamed Hashim Mohamed Ashmel Tlemsani Issam Matthews Robin 《Education and Information Technologies》2022,27(3):3171-3195
Education and Information Technologies - Digital transformation in the global higher education industry determines the future roadmap to a sustainable education management strategy. This research... 相似文献
833.
Jeremy Hodgen Becky Taylor Becky Francis Nicole Craig Nicola Bretscher Antonina Tereshchenko Paul Connolly Anna Mazenod 《British Educational Research Journal》2023,49(2):209-230
Despite extensive research on attainment grouping, the impact of attainment grouping on pupil attainment remains poorly understood and contested. This paper presents evidence from a study conducted with 2944 12–13 year olds, from 76 schools in England, who were allocated to between-class attainment groups (‘setting’) in English and mathematics over the first 2 years of secondary schooling. After controlling for prior attainment, pupils in the top set performed significantly better than pupils in the middle and bottom sets in both English and mathematics. The findings indicate a widening gap in attainment, especially in the case of English. Findings, especially in the case of mathematics, provide more evidence of a relative benefit for pupils placed in top sets than a relative detriment for those in bottom sets. 相似文献
834.
This paper provides introductory statistics instructors with the capacity to use engaging National Basketball Association (NBA) data within a web application to either strengthen students' understanding or introduce the concept of variance and one-way analysis of variance. Using engaging data within the classroom provides context to data that students deem applicable to their lives. This paper not only provides a lesson plan for teaching these concepts but also provides a web application and the engaging NBA dataset if the instructor decides to use the app or the data in another context. The NBA data selected to focus on the debate “Who is the greatest NBA player of all time?”. By using context students are familiar with and interested in, we can get them interested in and further engaged in statistics. 相似文献