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21.
Learners’ ability in dealing with socio-scientific issues has been highlighted in contemporary science education. This study
explored the effects of different on-line searching activities on high school students’ cognitive structure outcomes and informal
reasoning outcomes. By using a quasi-experimental research approach, thirty-three students were assigned to a “guided searching
task group”, while thirty-five students were assigned to an “unguided searching task group”. The treatments of this study
were two different on-line searching activities. All the participants were asked to search relevant information regarding
nuclear power usage on the Internet during the period of two classes (100 min). However, the students in the un-guided searching
task group were asked to search freely, while those in the guided searching task group were provided with a searching guideline.
The participants’ cognitive structures outcomes as well as their informal reasoning outcomes regarding nuclear power usage
were assessed before and after the conduct of on-line searching tasks. The results of ANCOVA revealed that the students in
the guided on-line searching task group significantly outperformed their counterparts in the extent (p < 0.01) and the richness of their cognitive structures (p < 0.01). Also, they significantly outperformed their counterparts in the usage of the two information processing strategies,
“comparing” (p < 0.05) and “inferring or explaining” (p < 0.05). Moreover, it was also found that the students in the guided on-line searching task group only outperformed their
counterparts in their supportive argument construction (p < 0.05). In other words, the guided searching tasks did help the students obtain better cognitive structure outcomes; however,
the increments on their cognitive structure outcomes may only help them to propose more supportive arguments, but their rebuttal
construction (an important indicator for their reasoning quality) was not particularly improved. 相似文献
22.
Affordances of Augmented Reality in Science Learning: Suggestions for Future Research 总被引:2,自引:2,他引:0
Augmented reality (AR) is currently considered as having potential for pedagogical applications. However, in science education, research regarding AR-aided learning is in its infancy. To understand how AR could help science learning, this review paper firstly has identified two major approaches of utilizing AR technology in science education, which are named as image-based AR and location-based AR. These approaches may result in different affordances for science learning. It is then found that students’ spatial ability, practical skills, and conceptual understanding are often afforded by image-based AR and location-based AR usually supports inquiry-based scientific activities. After examining what has been done in science learning with AR supports, several suggestions for future research are proposed. For example, more research is required to explore learning experience (e.g., motivation or cognitive load) and learner characteristics (e.g., spatial ability or perceived presence) involved in AR. Mixed methods of investigating learning process (e.g., a content analysis and a sequential analysis) and in-depth examination of user experience beyond usability (e.g., affective variables of esthetic pleasure or emotional fulfillment) should be considered. Combining image-based and location-based AR technology may bring new possibility for supporting science learning. Theories including mental models, spatial cognition, situated cognition, and social constructivist learning are suggested for the profitable uses of future AR research in science education. 相似文献
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Jason Sheng-Hong Tsai Meng-Che Wu Leang-San Shieh 《Journal of The Franklin Institute》2005,342(7):852-876
This paper presents a newly developed digital redesign control scheme with a weighted switching strategy and output-reference tracking, for a cascaded analog system with state saturation and external loads. The proposed method improves the generally poor transition response and output deviation caused by state-saturation constraint and external loads. It replaces an existing or theoretically well-designed analog controller with state saturation, by a digital controller with almost identical performance. 相似文献
25.
In this paper, a modified adaptive neural network for the compensation of deadzone is described, and simulated on a hydraulic positioning system, in which the dynamic model is separated into a series of connection of a nonlinear (deadzone) subsystem and a linear plant. The proposed approach uses two neural networks. One is the radial basis function (RBF) neural network, which is used for identifying parameters of deadzone. Based on the penalty function used in optimization theory, a multi-objective cost function with constraint is adopted to provide the best deadzone approximation. The result is used to train the other neural network for the inverse compensation of deadzone. The RBF neural network also generates the parameters of the linear plant for the design of an adaptive controller. A convergence analysis for the network training process is also presented. 相似文献
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Li Pei-Jung Hong Huang-Yao Chai Ching Sing Tsai Chin-Chung Lin Pei-Yi 《Research in Science Education》2020,50(5):2035-2053
Research in Science Education - The aim of this study was to investigate whether and how engaging with a computer-supported collaborative knowledge-building environment helps students to develop a... 相似文献
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Yi-Shan Tsai Carlo Perrotta Dragan Gašević 《Assessment & Evaluation in Higher Education》2020,45(4):554-567
AbstractThe emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and larger in size. However, the current approach to learning analytics presents tensions between increasing student agency in making learning-related decisions and ‘datafying’ students in the process of collecting, analysing and interpreting data. This article presents a study that explores staff and student experience of agency, equity and transparency in existing data practices and expectations towards learning analytics in a UK university. The results show a number of intertwined factors that have contributed to the tensions between enhancing a learner’s control of their studies and, at the same time, diminishing their autonomy as an active agent in the process of learning analytics. This article argues that learner empowerment should not be automatically assumed to have taken place as part of the adoption of learning analytics. Instead, the interwoven power relationships in a complex educational system and the interactions between humans and machines need to be taken into consideration when presenting learning analytics as an equitable process to enhance student agency and educational equity. 相似文献
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