Teaching early numeracy to students with autism using a school staff delivery model

Mathematics is one of the core school subjects in the UK and an emphasis is placed on developing pupils’ mathematical competencies throughout all key stages. Despite that, the attainment of students with disabilities in mathematics remains low. The current study explored ways in which the Teaching Early Numeracy to children with Developmental Disabilities (TEN-DD) programme could be implemented by teaching staff in a special school in the UK to improve the numeracy skills of students with autism. Adaptations to the delivery of the programme were made during the study as a result of continued collaboration with the participating school. The findings suggest that it may be feasible to implement the TEN-DD programme using a school staff delivery model and it may help learners improve their early numeracy skills. Practical aspects of TEN-DD’s implementation highlighted the need to incorporate more systematic adaptations for minimally verbal students, as well as for learners who might need additional training with prerequisite skills.     

Adaptive Functioning in Williams Syndrome: A Systematic Review

Literature on the level of adaptive functioning and relative strengths and weaknesses in functioning of individuals with Williams syndrome (WS) was reviewed. The electronic databases PsycINFO, PubMed, Expanded Academic, Web of Science, Scopus and ProQuest were searched electronically for relevant articles and dissertations using the search terms ‘Williams syndrome’ or ‘Williams-Beuren syndrome’ combined with ‘adaptive function*’, ‘adaptive behavio*’, ‘independ*’ and ‘autonomy’. Selection criteria included English language articles, theses and book chapters, participants with a diagnosis of Williams syndrome and inclusion of a standardised assessment of adaptive functioning. Twenty-two published articles and two PhD. dissertations (one subsequently published as a poster presentation) met the selection criteria for inclusion in the review. Fifteen investigated adaptive functioning in children and adolescents aged up to 19 years of age, five investigated adaptive functioning in adults and four included participants across a wider age range and included both children and adults. Along with identifying methodological issues, the review addressed the following areas: overall level of adaptive functioning in WS, domain strengths and weaknesses, evidence of heterogeneity, relationship to intellectual ability, changes with chronological age, relationship with maladaptive behaviour, gender differences and the potential influence of environmental factors which may be related to adaptive functioning in WS.     

体能与体能训练的系统结构分析

运用系统论观点对体能与体能训练系统的结构分别从动态和静态的视角进行分析,认为体能系统的框架（静态）结构包括身体素质、身体形态、身体机能和心理智能4个方面的子系统,而体能训练的运行（动态）结构体系主要是基础体能训练、专项体能训练和综合体能训练3个相互联系、相互影响的子系统构成,以期对体能与体能训练的系统结构有新的认识,从而给体能训练的科学化提供些许参考。     

课堂教学的系统性与大学生的全面发展

高等教育不是简单的技能培训,不能出现技能学习"泛化"的状况。高等教育的目标是培养全面发展的大学生,为了实现这一目标,必须从最基础的课堂教学的系统化开始,因为这符合心理学的原理、符合文化社会学的要求、符合大学生发展的教育规律。     

Infusing educational technologies in the heart of the university—A systematic literature review from an organisational perspective

Educational technologies have experienced unprecedented prominence on university agendas with many institutions motivated to keep the lessons learned from the COVID-19 sparked transition with regard to online teaching. In response to this renewed interest in ensuring the longevity of educational technologies in higher education, this systematic review analysed the various organisational factors—for example, leadership, infrastructure, strategy—considered essential in the literature for the successful implementation of educational technologies. Specifically, we reviewed 1614 papers published in five prominent educational technology journals in the last decade. From this sample, we identified 47 papers that discussed organisational factors. Drawing on these studies, we constructed an organisational framework, which outlines the different organisational factors, actors and processes involved in implementing educational technologies. The identified organisational factors are structured into three main categories: (1) Leadership and Strategy, (2) Infrastructure and Resources and (3) Recognition and Motivation. Our aim was to further the scholarly understanding of the organisational layer involved in digital change as well as provide concrete recommendations for practitioners.

Practitioner notes

What is already known about this topic

• Previous research has stressed the importance of taking organisational factors such as infrastructure, leadership, strategy and staff commitment into account when implementing educational technologies.
• However, review papers have failed to systematically organise these studies to create a comprehensive understanding of the organisational factors involved in implementing educational technologies and ensuring their longevity at an institution.
• There is currently a high level of interest in how educational technologies can be implemented in the higher education landscape, as many institutions are facing the question of what lessons they can learn from the crisis and how they can continue on their path of digitalisation.

What this paper adds

• This review paper addresses a gap in our scholarly understanding of the organisational layers involved in the implementation of educational technologies in higher education institutions (HEIs).
• This paper provides a framework on organisational factors, which influence the implementation of educational technologies in HEIs.
• This review paper demonstrates that bottom-up and opinion leadership, support structures tailored to the need and time of faculty as well as recognition and incentives have the largest impact on a sustainable implementation of educational technologies in HEIs.

Implications for practice and/or policy

• Universities should create structures that enable innovation and creativity by promoting bottom-up and opinion leadership as well as shared decision-making processes as they are important for the successful implementation of educational technologies in HEIs.
• Besides providing a reliable and suitable infrastructure, institutional support and resources in terms of technical advice and training tailored to specific needs, should be in place when planning the implementation of educational technologies in HEIs.
• The additional workload instructors face when implementing digital teaching should be recognised and incentivised as it strengthens instructor engagement which is crucial for the implementation of educational technologies in HEIs.

     

智能制造研究述评

基于1990—2022年Web of Science和CSSCI文献库的样本文献，结合文献计量法和系统性文献回顾法，对智能制造的研究现状进行梳理与总结，识别出研究不足并提出未来的研究方向。研究发现：智能制造现有研究包括七大知识模块：概念内涵、核心系统、测量方式、情境、理论视角、前因与后果因素和发展路径，它们共同构成了“What—Why—How”（2W1H）知识框架。What维度（概念内涵与外延）包括智能制造的概念内涵、功能和支持两大核心系统以及对智能制造水平的测量方式；Why维度（前因后果与作用机制）包括技术革命和商业文明时代两大情境，赶超战略、微笑曲线理论、行动者网络和价值共创四大理论视角，技术、组织和环境层面的前因因素，以及国家、产业和企业层面的后果；How维度（发展路径）包括技术和创新追赶、价值链攀升与集成、行动者网络和价值共创四种发展路径。综合现有研究不足、研究热点与研究前沿，对2W1H三个关键问题提出具体的研究议题方向。结合文献计量法和系统性文献回顾法，更加科学客观地提出了智能制造整体知识框架以及未来研究方向。     

Understanding privacy and data protection issues in learning analytics using a systematic review

The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.

Practitioner notes

What is already known about this topic

• Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
• Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.

What this study contributes

• Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
• Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
• Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.

Implications for practice and/or policy

• Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
• Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
• Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.