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Pratiche basate sui dati nella valutazione e l’analisi della qualità didattica: il caso dell’Università di Padova 基于数据的教学质量评估和分析实践:帕多瓦大学的案例
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-06-01 DOI: 10.30557/QW000036
J. Raffaghelli, V. Grion, M. Rossi
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引用次数: 0
La presentazione di una falsa identità nell’era digitale 在数字时代展示假身份
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-06-01 DOI: 10.30557/QW000037
Maria Grazia Monaci, Naomi Cerisetti
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引用次数: 0
Contribution of technology innovation acceptance and organizational innovation climate on innovative teaching behavior with ICT in indonesian education 技术创新接受度和组织创新氛围对印尼ICT教育创新教学行为的影响
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-06-01 DOI: 10.30557/QW000035
M. Sofwan, R. Pratama, M. Muhaimin, Y. Yusnaidar, A. Mukminin, Akhmad Habibi
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引用次数: 5
Diari di apprendimento e learning analytics, strumenti integrabili per capire i processi di studio? Giudizi di difficoltà e tracciamento delle attività online 学习日记和学习分析,理解学习过程的集成工具?困难评估和在线活动跟踪
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-06-01 DOI: 10.30557/QW000038
Riccardo Fattorini, G. Paoletti
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引用次数: 0
Sviluppo di una OER per l’insegnamento delle biotecnologie: risultati di una sperimentazione eseguita nell’ultimo anno dei Licei 生物技术教学OER的开发:去年进行的一项实验的结果
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-06-01 DOI: 10.30557/QW000034
Giovanni Guarguaglini, Cristina Miceli, Daniela Amendola
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引用次数: 0
Quali competenze e quali strategie formative per l’industria 4.0? Lo stato dell’arte 行业的哪些技能和培训策略4.0?最先进的
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2021-01-01 DOI: 10.30557/QW000039
Marco Perini, F. Tommasi, R. Sartori
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引用次数: 4
Will knowledge building remain uniquely human? 知识的积累还会是人类独有的吗?
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2020-12-23 DOI: 10.30557/QW000028
M. Scardamalia, C. Bereiter
If AI is to have a positive transforming effect on education, it will be through the community norms, collective practices, and solidarity that emerge around it. In Knowledge Building, this means fuller realization of such principles as collective responsibility for idea improvement, idea diversity, and knowledge building as a way of life. AI can aid the development of this kind of community by providing powerful tools students themselves can use to strengthen their knowledgebuilding efforts and eventually by making intelligent machines active collaborators in these efforts. This paper describes advances currently taking place in Knowledge Building technology. Although full collaboration between humans and machines in knowledge creation may be years away, education can start preparing students for their role in it by emphasizing those capabilities that arise from the multifarious personal and social lives they lead.
如果人工智能要对教育产生积极的变革作用,就必须通过围绕它出现的社区规范、集体实践和团结来实现。在知识建设中,这意味着更充分地实现诸如思想改进的集体责任、思想多样性和作为一种生活方式的知识建设等原则。人工智能可以通过提供强大的工具来帮助学生自己加强知识建设,并最终使智能机器成为这些努力的积极合作者,从而帮助这种社区的发展。本文描述了知识构建技术的最新进展。虽然人类和机器在知识创造方面的全面合作可能还需要数年时间,但教育可以通过强调学生在各种个人和社会生活中产生的能力,开始为他们在其中的角色做准备。
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引用次数: 2
Student teachers’ pedagogical reasoning in TPCK-based design tasks. A multiple case study 学生教师在基于tpc的设计任务中的教学推理。多案例研究
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2020-12-23 DOI: 10.30557/QW000031
Ottavia Trevisan, M. Rossi
Teachers’ professional expertise cannot ignore anymore a technological component to it. Technology is nowadays accessible more and more widely, but it does not automatically translate into learning improvement. It is crucial to understand how educators give meaning to technology integration in their practices, i.e. investigate teachers’ professional reasoning. The paper reports on part of a wider study on Initial Teacher Education (ITE) institutions’ capability to engage student- teachers’ reasoning. Within the broader multiple case study across Europe, the paper reports on data emerging from document analysis and focused interviews with pre-service teachers (Ntot 36). The findings suggest an activation of reasoning whose roots might find place outside ITE influence, encouraging further research.
教师的专业知识不能再忽视其中的技术成分。如今,技术的使用越来越广泛,但它并不能自动转化为学习的改进。了解教育工作者如何在实践中赋予技术整合意义是至关重要的,即调查教师的专业推理。本文报告了一项关于初级教师教育(ITE)机构参与学生教师推理能力的更广泛研究的一部分。在整个欧洲更广泛的多个案例研究中,本文报告了从文件分析和对职前教师的集中访谈中得出的数据(ntot36)。研究结果表明,推理的激活可能在it影响之外找到根源,这鼓励了进一步的研究。
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引用次数: 1
Perusall: University learning-teaching innovation employing social annotation and machine learning Perusall:运用社会注释和机器学习的大学学与教创新
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2020-12-23 DOI: 10.30557/QW000030
G. Cecchinato, L. Foschi
This paper presents the learning-teaching innovation process of a University course. The traditional elements of the teaching-learning process (lecture, study, exam) involving students in ongoing activities have changed. The paper focuses on the learning changes introduced by social annotation activities carried out through the Perusall web environment. In particular, Perusall functionalities that assess students’ participation were examined. These rely on multiple indicators set by the teacher, and a Machine Learning algorithm, which assesses the quality of annotations. A study was carried out to examine the validity of this process by analysing the relationship between Perusall algorithm’s scores and teacher’s scores, and how students perceive the automated scoring. The relationship was investigated through the Spearman correlation coefficient and Kendall’s coefficient of concordance. Thematic analysis was used to analyse the qualitative data concerning students’ perceptions. The results indicate that the Perusall algorithm provided scores quite similar to those of the teacher, and that students positively perceived the automated scoring.
本文介绍了大学课程学与教的创新过程。让学生参与持续活动的教学过程的传统要素(讲座、学习、考试)发生了变化。本文主要研究通过Perusall web环境开展社会标注活动所带来的学习变化。特别是,Perusall功能评估学生的参与进行了检查。这些依赖于教师设置的多个指标,以及评估注释质量的机器学习算法。通过分析Perusall算法得分与教师得分之间的关系,以及学生如何看待自动评分,进行了一项研究,以检验这一过程的有效性。通过Spearman相关系数和Kendall协调系数对二者的关系进行了研究。主题分析法用于分析学生认知的定性数据。结果表明,Perusall算法提供的分数与老师的分数非常相似,学生对自动评分有积极的看法。
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引用次数: 6
From the Teaching machines to the Machine learning. Opportunities and challenges for Artificial Intelligence in education 从教学机器到机器学习。人工智能在教育中的机遇与挑战
IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2020-12-01 DOI: 10.30557/QW000027
V. Grion, G. Cecchinato
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引用次数: 0
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