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731.
Ibrahim H. Osman Abdel Latef Anouze Zahir Irani Baydaa Al-Ayoubi Habin Lee Asım Balcı Tunç D. Medeni Vishanth Weerakkody 《Government Information Quarterly》2014
E-government services involve many stakeholders who have different objectives that can have an impact on success. Among these stakeholders, citizens are the primary stakeholders of government activities. Accordingly, their satisfaction plays an important role in e-government success. Although several models have been proposed to assess the success of e-government services through measuring users' satisfaction levels, they fail to provide a comprehensive evaluation model. This study provides an insight and critical analysis of the extant literature to identify the most critical factors and their manifested variables for user satisfaction in the provision of e-government services. The various manifested variables are then grouped into a new quantitative analysis framework consisting of four main constructs: cost; benefit; risk and opportunity (COBRA) by analogy to the well-known SWOT qualitative analysis framework. The COBRA measurement scale is developed, tested, refined and validated on a sample group of e-government service users in Turkey. A structured equation model is used to establish relationships among the identified constructs, associated variables and users' satisfaction. The results confirm that COBRA framework is a useful approach for evaluating the success of e-government services from citizens' perspective and it can be generalised to other perspectives and measurement contexts. 相似文献
732.
仿制、仿造已经成为了我国一部分企业的固定生产方式,针对市场热门产品的仿造品屡见不鲜,逆向工程的广泛应用在其中起到了不可忽视的作用。本文从逆向工程设计的概念出发,阐述了现代制造业中逆向工程的概念以及逆向工程在模具制造等行业中的作用。本文对于我们正确认识逆向工程技术有一定的意义。 相似文献
733.
Agnes S.K. Wong Matthew Moreno Samantha Burns Earl Woodruff 《Journal of Research in Reading》2023,46(2):143-162
Background
Research has suggested that background music can have a positive or negative effect that can influence the affective state of individuals. Although research has demonstrated that fear negatively influences our cognitive performance, there is a research gap in understanding the combined effects of different background music tempo and fear in influencing reading comprehension performance.Methods
Data were collected from 70 participants enrolled at a public university in Canada. Participants were required to listen to background music of varying speeds with three conditions (no music, slow music and fast music). We adopted a cross-sectional multi-level modelling approach for the main analyses, and further analyses using t-test and ANOVA.Results
Results indicated that expression of fear was not a significant predictor of participants' reading comprehension performance (Model 1). However, when music condition was added (Model 2) in addition to expression of fear, a significant relationship between reading comprehension performance and music condition was found, showing better reading comprehension performance in the slow music condition than in the no music condition. Furthermore, there was a significant interaction effect between music condition and expression of fear on reading comprehension performance (Model 3). Importantly, not all individuals were affected by the music to the same extent, with the possibility that baseline level of fear being the key issue in influencing comprehension performance.Conclusions
Considering both expression of fear and music condition is required to understand the combined effects on cognitive performance. Expression of fear during cognitive tasks such as reading could be an essential signal that interventions should be applied. Such strategies may be especially beneficial for task performers with higher baseline levels of fear and possibly provide us with insights for best practice and research implications in the field of reading comprehension among individuals with special needs.734.
735.
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.
736.
Andrew Emerson Wookhee Min Roger Azevedo James Lester 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):40-57
Game-based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individualised learning and support proactive scaffolding when students are struggling, game-based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with the learning environment with conventional methods, such as multiple choice content knowledge assessments. While previous student modelling approaches have leveraged machine learning to automatically infer students' knowledge, there is limited work that incorporates the fine-grained content from each question in these types of tests into student models that predict student performance at early junctures in gameplay episodes. This work investigates a predictive student modelling approach that leverages the natural language text of the post-gameplay content knowledge questions and the text of the possible answer choices for early prediction of fine-grained individual student performance in game-based learning environments. With data from a study involving 66 undergraduate students from a large public university interacting with a game-based learning environment for microbiology, Crystal Island , we investigate the accuracy and early prediction capacity of student models that use a combination of gameplay features extracted from student log files as well as distributed representations of post-test content assessment questions. The results demonstrate that by incorporating knowledge about assessment questions, early prediction models are able to outperform competing baselines that only use student game trace data with no question-related information. Furthermore, this approach achieves high generalisation, including predicting the performance of students on unseen questions.
Practitioner notes
What is already known about this topic- A distinctive characteristic of game-based learning environments is their capacity to enable fine-grained student assessment.
- Adaptive game-based learning environments offer individualisation based on specific student needs and should be able to assess student competencies using early prediction models of those competencies.
- Word embedding approaches from the field of natural language processing show great promise in the ability to encode semantic information that can be leveraged by predictive student models.
- Investigates word embeddings of assessment question content for reliable early prediction of student performance.
- Demonstrates the efficacy of distributed word embeddings of assessment questions when used by early prediction models compared to models that use either no assessment information or discrete representations of the questions.
- Demonstrates the efficacy and generalisability of word embeddings of assessment questions for predicting the performance of both new students on existing questions and existing students on new questions.
- Word embeddings of assessment questions can enhance early prediction models of student knowledge, which can drive adaptive feedback to students who interact with game-based learning environments.
- Practitioners should determine if new assessment questions will be developed for their game-based learning environment, and if so, consider using our student modelling framework that incorporates early prediction models pretrained with existing student responses to previous assessment questions and is generalisable to the new assessment questions by leveraging distributed word embedding techniques.
- Researchers should consider the most appropriate way to encode the assessment questions in ways that early prediction models are able to infer relationships between the questions and gameplay behaviour to make accurate predictions of student competencies.
737.
By taking into account the functions of socially shared metacognitive regulation (SSMR) (i.e. the role that SSMR plays in the (dis)continuation of ongoing interaction), the present study sheds light on the differential effectiveness of SSMR. The study more particularly investigates how individual students' involvement in SSMR that confirms, changes, activates, or stops interaction, predicts their understanding of the learning content on the short and middle-long term, taking into account students' prior knowledge. Sixty university students were involved. Multilevel models were run to investigate the relation between individual students' engagement in the functions of SSMR and their conceptual understanding. Contributing to SSMR that changes and that activates collaborative learning appeared significantly positively related to students' immediate understanding of the learning content, whereas participating in SSMR that confirms or that stops ongoing interaction was not. Contributing to SSMR (regardless of its function) appeared not significant for predicting students’ conceptual understanding on the middle-long term. 相似文献
738.
Track recommendations provided to students in the final grade of primary education lead the allocation to specific school tracks in secondary education in the Netherlands. Where the results of a standardised test indicate that students are able to go to a higher track level, primary schools are required to reconsider and potentially adjust the track recommendation to a higher level. The current research aimed to (1) investigate trends in the level of track recommendations, double track recommendations and reconsiderations over the years 2014–2015 to 2018–2019, (2) explore the variation in (trends of) track recommendations between Dutch primary schools and their school boards, and (3) assess the association between track recommendations and the school level variables degree of urbanisation and type of primary education. We used multilevel growth curve modelling for continuous and count data based on publicly available school-level population data regarding track recommendations and school leavers tests from 2014–2015 to 2018–2019. The number of double track recommendations has increased over the cohorts, with a slightly decreasing gap between schools in rural and urban areas. The number of reconsiderations first decreased and then increased. The differences in reconsiderations between rural and urban areas are increasing over time. An initial trend towards higher average recommendations stabilising in the later cohorts appeared with no clear pattern for degree of urbanisation. The current study adds to the existing knowledge by assessing longitudinal trends instead of cross-sectional analyses and including multiple stakeholders and factors simultaneously. 相似文献