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Titlebook: Higher Education Computer Science; A Manual of Practica Jenny Carter,Michael O‘Grady,Clive Rosen Book 20181st edition Springer Nature Switz

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發(fā)表于 2025-3-21 18:49:32 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Higher Education Computer Science
副標(biāo)題A Manual of Practica
編輯Jenny Carter,Michael O‘Grady,Clive Rosen
視頻videohttp://file.papertrans.cn/427/426841/426841.mp4
概述Offers guidance for educators in Higher Education teaching Computer Science.Includes numerous, varied case studies and practical examples, highlighting a range of successful teaching strategies.Presen
圖書(shū)封面Titlebook: Higher Education Computer Science; A Manual of Practica Jenny Carter,Michael O‘Grady,Clive Rosen Book 20181st edition Springer Nature Switz
描述.There is currently a great emphasis on teaching quality in Higher Education. In the UK, the Teaching Excellence Framework and the National Student Survey have contributed significantly to this focus. Additional support for staff to develop teaching skills has also come from the Higher Education Academy, whose fellowship scheme encourages HE staff to focus on their practice in the classroom..The growth in the number of students attending university has resulted in a much wider range of learning styles amongst them. Many students do not fit the idealised average of being adept at learning from primarily text-based media. Two further trends are also driving change and innovation in academic staff teaching. The first is the availability of online teaching materials such as MOOCs. The second is the emphasis now given to student postgraduate employability, represented by certain aspects of the Teaching Excellence Framework that require students not only to know information, but alsoto be able to articulate that knowledge and to demonstrate their skills..With a desire to enable our students to achieve their highest potential, many staff undertake initiatives to facilitate learning that a
出版日期Book 20181st edition
關(guān)鍵詞Computer science education; STEM education; Pedagogy; Learning styles; Higher education; The computing pr
版次1
doihttps://doi.org/10.1007/978-3-319-98590-9
isbn_softcover978-3-030-07510-1
isbn_ebook978-3-319-98590-9
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

書(shū)目名稱(chēng)Higher Education Computer Science影響因子(影響力)




書(shū)目名稱(chēng)Higher Education Computer Science影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Higher Education Computer Science網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Higher Education Computer Science網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Higher Education Computer Science被引頻次




書(shū)目名稱(chēng)Higher Education Computer Science被引頻次學(xué)科排名




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書(shū)目名稱(chēng)Higher Education Computer Science讀者反饋




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Liz Coulter-Smithe ontological difference between . and beings. This includes the deliberation on the difference between perception and observation, as well as that between actuality and reality. In short, this chapter is about the prospect of phenomenology in architectural scholarship.
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Diane Kitchinless modern people are ignorant, the consequences of their lifestyle on climate change will lead to self-destruction. To avoid this, this essay argues there is a necessity for rethinking our approach to planning/design and technology about the built environment. The purpose of this essay is retrospe
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Michael O’Gradye ontological difference between . and beings. This includes the deliberation on the difference between perception and observation, as well as that between actuality and reality. In short, this chapter is about the prospect of phenomenology in architectural scholarship.
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Thomas Lancasteriterate external formal and aesthetic qualities as they relate to pattern, color, proportion, hierarchy, and formal language. The human engagement in this design process is limited to the initial curation of input data in the form of image repositories of non-architectural disciplines that the machi
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veral times in order to record the preference. After being trained with the test result, the neural network can evaluate a new inputted form with a score from 0 to 1, indicating the predicted preference of the architect, showing the possibility of using machine learning to quantitatively evaluate pe
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Carlton McDonaldveral times in order to record the preference. After being trained with the test result, the neural network can evaluate a new inputted form with a score from 0 to 1, indicating the predicted preference of the architect, showing the possibility of using machine learning to quantitatively evaluate pe
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David Collinsiterate external formal and aesthetic qualities as they relate to pattern, color, proportion, hierarchy, and formal language. The human engagement in this design process is limited to the initial curation of input data in the form of image repositories of non-architectural disciplines that the machi
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