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Previous studies have documented the promising results from student-constructed representations, including stop-motion animation (SMA), in supporting mechanistic reasoning (MR), which is considered an essential thinking skill in science education. Our current study presents theoretically and empirically how student-constructed SMA contributes to promoting MR. As a theoretical perspective, we propose a framework hypothesizing the link between elements of MR and the construction nature of SMA, that is, chunking and sequencing. We then examined the extent to which this framework was consistent with a multiple-case study in the domain of static electricity involving five secondary school students constructing and using their own SMA creation for reasoning. In addition, students' reasoning in pre- and postconstruction of an SMA was examined. Our empirical findings confirmed our framework by showing that all students identified the basic elements of MR, that is, entities and activities of entities, when engaging in chunking and sequencing. Chunking played a role in facilitating students to identify entities responsible for electrostatic phenomena, and sequencing seemed to elicit students to specify activities of these entities. The analysis of students' reasoning in pre- and postconstruction of SMA found that student-generated SMA has a potential effect on students' retention of the use of MR. Implications for instruction with SMA construction to support MR are discussed.  相似文献   
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Journal of Science Education and Technology - The ICT-based learning model has been a catalyst in the field of modern education to teach higher-order thinking skills (HOTS). However, a few studies...  相似文献   
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Hate speech is an increasingly important societal issue in the era of digital communication. Hateful expressions often make use of figurative language and, although they represent, in some sense, the dark side of language, they are also often prime examples of creative use of language. While hate speech is a global phenomenon, current studies on automatic hate speech detection are typically framed in a monolingual setting. In this work, we explore hate speech detection in low-resource languages by transferring knowledge from a resource-rich language, English, in a zero-shot learning fashion. We experiment with traditional and recent neural architectures, and propose two joint-learning models, using different multilingual language representations to transfer knowledge between pairs of languages. We also evaluate the impact of additional knowledge in our experiment, by incorporating information from a multilingual lexicon of abusive words. The results show that our joint-learning models achieve the best performance on most languages. However, a simple approach that uses machine translation and a pre-trained English language model achieves a robust performance. In contrast, Multilingual BERT fails to obtain a good performance in cross-lingual hate speech detection. We also experimentally found that the external knowledge from a multilingual abusive lexicon is able to improve the models’ performance, specifically in detecting the positive class. The results of our experimental evaluation highlight a number of challenges and issues in this particular task. One of the main challenges is related to the issue of current benchmarks for hate speech detection, in particular how bias related to the topical focus in the datasets influences the classification performance. The insufficient ability of current multilingual language models to transfer knowledge between languages in the specific hate speech detection task also remain an open problem. However, our experimental evaluation and our qualitative analysis show how the explicit integration of linguistic knowledge from a structured abusive language lexicon helps to alleviate this issue.  相似文献   
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Learning Environments Research - Studies of connections between teacher behaviour and student outcomes are numerous, but those specifically addressing such connections in a competence-based...  相似文献   
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ABSTRACT

The purpose of this research is to evaluate the role of society-based library or Taman Bacaan Masyarakat (TBM) in empowering Surabaya City people. Society-based library will be a place of Surabayans’ live-long learning according to the Law on Even Distribution of and Rights to Education. This research adopts a quantitative approach with qualitative data analysis. The sample of the research is fourteen TBMs that have an empowerment program or is near to a small and medium enterprise. Findings of the research include: (1) that TBM role in community empowerment is beginning to be noticeable in their empowerment programs such as trainings for local community, (2) that empowerment programs at TBM are conducted informally and intermittently, such as trainings of producing aromatherapy candles, beads, flannelette, and waste recycling, and (3) the TBM are constrained by shortage of collection and funding for programs. The research recommends that: community empowerment programs should be increased through TBM programs; TBM personnels should be encouraged to innovate; and collection development should be tailored with local needs.  相似文献   
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Journal of Science Education and Technology - This article reports on a case study that aims to help students develop mechanistic reasoning through constructing a model based stop-motion animation...  相似文献   
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