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861.
Cultural Studies of Science Education - 相似文献
862.
Daniel Paech Kerstin Klopries Ralph Nawrotzki Heinz-Peter Schlemmer Frederik L. Giesel Joachim Kirsch Jobst-Hendrik Schultz Thomas Kuner Sara Doll 《Anatomical sciences education》2022,15(1):143-154
Cadaver-specific postmortem computed tomography (PMCT) has become an integral part in anatomy teaching at several universities. Recently, the feasibility of contrast-enhanced (CE)-PMCT has been demonstrated. The purpose of this study was to identify particular strengths and weaknesses of both non-enhanced and contrast-enhanced PMCT compared to conventional cadaver dissection. First, the students’ perception of the learning effectiveness of the three different modalities have been assessed using a 34-item survey (five-point Likert scale) covering all anatomy course modules. Results were compared using the nonparametric Friedman Test. Second, the most frequent artifacts in cadaver CT scans, were systematically analyzed in 122 PMCT and 31 CE-PMCT data sets to quantify method-related limitations and characteristics. Perfusion quality was assessed in 57 vascular segments (38 arterial and 19 venous). The survey was answered by n = 257/320 (80.3%) students. Increased learning benefits of PMCT/ CE-PMCT compared to cadaver dissection were found in osteology (2/3 categories, P < 0.001), head and neck (2/5 categories, P < 0.01), and brain anatomy (3/3 categories, P < 0.01). Contrast-enhanced-PMCT was perceived particularly useful in learning vascular anatomy (10/10 categories, P < 0.01). Cadaver dissection received significantly higher scores compared to PMCT and CE-PMCT in all categories of the abdomen and thorax (7/7 categories, P < 0.001), as well as the majority of muscular anatomy (5/6 categories, P < 0.001). Frequent postmortem artifacts (total n = 28, native-phase n = 21, contrast injection-related n = 7) were identified and assessed. The results of this work contribute to the understanding of the value of integrating cadaver-specific PMCT in anatomy teaching. 相似文献
863.
David Gibson Vitomir Kovanovic Dirk Ifenthaler Sara Dexter Shihui Feng 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1125-1146
This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.
Practitioner notes
What is already known about this topic- Numerous learning theories exist with significant cross-over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning.
- Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified.
- Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices.
- A three-level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education.
- A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels.
- The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels.
- Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity.
- Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems.
- Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.
864.
Caviola Sara Toffalini Enrico Giofrè David Ruiz Jessica Mercader Szűcs Dénes Mammarella Irene C. 《Educational Psychology Review》2022,34(1):363-399
Educational Psychology Review - The relationship between anxiety and mathematics has often been investigated in the literature. Different forms of anxiety have been evaluated, with math anxiety... 相似文献
865.
866.
Sabrina J. Ashwell Patricia K. Baskin Stacy L. Christiansen Sara A. DiBari Annette Flanagin Tracy Frey Racquel Jemison Mia Ricci 《Learned Publishing》2023,36(1):94-99
- Inclusive language will make scholarly publishing more accurate and more respectful, and it has the potential to help authors reach a wider audience.
- JAMA and the JAMA Network journals, the American Chemical Society and the Coalition for Diversity and Inclusion in Scholarly Communications have free guides on inclusive language, formatting and images.
- The guides provide principles and rationale, as well as examples of preferred language, in order to equip people with knowledge to choose the most inclusive words even as terminology preferences change.