The Role of Anatomy Computer-Assisted Learning on Spatial Abilities of Medical Students

Currently, medical education context poses different challenges to anatomy, contributing to the introduction of new pedagogical approaches, such as computer-assisted learning (CAL). This approach provides insight into students' learning profiles and skills that enhance anatomy knowledge acquisition. To understand the influence of anatomy CAL on spatial abilities, a study was conducted. A total of 671 medical students attending Musculoskeletal (MA) and Cardiovascular Anatomy (CA) courses, were allocated to one of three groups (MA Group, CA Group, MA + CA Group). Students' pre-training and post-training spatial abilities were assessed through Mental Rotations Test (MRT), with scores ranging between 0-24. After CAL training sessions, students' spatial abilities performance improved (9.72 ± 4.79 vs. 17.05 ± 4.57, P < 0.001). Although male students in both MA Group and CA Group show better baseline spatial abilities, no sex differences were found after CAL training. The improvement in spatial abilities score between sessions (Delta MRT) was correlated with Musculoskeletal Anatomy training sessions in MA Group (r = 0.333, P < 0.001) and MA + CA Group (r = 0.342, P < 0.001), and with Cardiovascular Anatomy training sessions in CA Group (r = 0.461, P = 0.001) and MA + CA Group (r = 0.324, P = 0.001). Multiple linear regression models were used, considering the Delta MRT as dependent variable. An association of Delta MRT to the amount of CAL training and the baseline spatial abilities was observed. The results suggest that CAL training in anatomy has positive dose-dependent effect on spatial abilities.     

Cloud-based big data analytics for customer insight-driven design innovation in SMEs

Fast development of IT and ICT facilitate customers to post a large volume of their concerns and expectation online, which are widely accepted to be a valuable resource for product designers. However, it is found that only a small number of small and medium-sized enterprises (SMEs) have capabilities to leverage customer online insights for design innovation, which often demonstrate a significant share in national economies growth. To discover the beneath reasons regarding the barrier that prevent them to make effective utilization, in this study, as a concrete example, manufacturing SMEs in the South Wales and Greater Manchester industrial areas of the UK are focused and their potential motivations for using and knowledge of big data-based customer analytics are investigated. An exploratory survey was conducted in terms of the type of customer data they have, the storage approaches, the volume of customer data, etc. Next, a carefully devised exploratory study was undertaken to understand how SMEs perceive the relations between customer data and product design, how about their expectations from big customer data analytics and what really challenges SMEs to exploit the value of big customer data. Besides, a demonstration platform is developed to present SMEs an automatic process of analysing customer online reviews and the capacity on customer insights acquisition and strategic decision making. Finally, findings from two focus groups indicate the different managerial and technical considerations required for SMEs considering implementing big data and customer analytics. This study encourages SMEs to welcome big customer data and suggests that a cloud-based approach may be the most appropriate way of giving access to big data analytics techniques.     

院校研究与学习分析

院校研究与学习分析在学习研究方面同异并存,皆利用大数据探寻学生学习的优劣.院校研究主要依赖大学的管理和调查数据,学习分析主要使用学习管理系统收集的数据.院校研究聚焦于帮助大学“知己知彼”,学习分析旨在助力“知学生”“知学习”“知教育”.院校研究为学校层面决策提供实证研究证据,学习分析识别学生个体学习行为从而支持个性化学...     

The use of player physical and technical skill match activity profiles to predict position in the Australian Football League draft

This study investigated the extent to which position in the Australian Football League (AFL) national draft is associated with individual game performance metrics. Physical/technical skill performance metrics were collated from all participants in the 2014 national under 18 (U18) championships (18 games) drafted into the AFL (n = 65; 17.8 ± 0.5 y); 232 observations. Players were subdivided into draft position (ranked 1–65) and then draft round (1–4). Here, earlier draft selection (i.e., closer to 1) reflects a more desirable player. Microtechnology and a commercial provider facilitated the quantification of individual game performance metrics (n = 16). Linear mixed models were fitted to data, modelling the extent to which draft position was associated with these metrics. Draft position in the first/second round was negatively associated with “contested possessions” and “contested marks”, respectively. Physical performance metrics were positively associated with draft position in these rounds. Correlations weakened for the third/fourth rounds. Contested possessions/marks were associated with an earlier draft selection. Physical performance metrics were associated with a later draft selection. Recruiters change the type of U18 player they draft as the selection pool reduces. juniors with contested skill appear prioritised.     

Risk terrain modeling predicts child maltreatment

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.     

Governing software: networks,databases and algorithmic power in the digital governance of public education

This article examines the emergence of ‘digital governance’ in public education in England. Drawing on and combining concepts from software studies, policy and political studies, it identifies some specific approaches to digital governance facilitated by network-based communications and database-driven information processing software that are being discursively promoted in education by cross-sectoral intermediary organizations. Such intermediaries, including National Endowment for Science, Technology and the Arts, Demos, the Innovation Unit, the Education Foundation and the Nominet Trust, are increasingly seeking to participate in new digitally mediated forms of educational governance. Through their promotion of network-based pedagogies and database-driven analytics software, these organizations are seeking to delegate educational decision-making to socio-algorithmic forms of power that have the capacity to predict, govern and activate learners' capacities and subjectivities.     

基于数据挖掘的学生投入模型与学习分析

学生投入是高等教育质量和学习成果的重要影响因素,对于高等教育评估与改革具有积极作用,受到了国内外研究者的广泛关注。文章以构建学生投入模型为基础,采用典型相关分析和数据挖掘方法相结合,识别学生投入的相关因素,并对学生学习行为进行分类研究。分析发现学生投入与学生家庭背景、学生入学前特征、学校特征及课程作业之间存在着显著相关关系,不同的学生投入及其学习行为表现有助于加深学校对学生学习行为的了解,更好地研究学习规律的新趋势,为审视高校以生为本、以学为中心的人才培养措施和多元性发展,提供了重要的参考与支持。     

Big and open linked data analytics ecosystem: Theoretical background and essential elements

Big and open linked data are often mentioned together because storing, processing, and publishing large amounts of these data play an increasingly important role in today's society. However, although this topic is described from the political, economic, and social points of view, a technical dimension, which is represented by big data analytics, is insufficient. The aim of this review article was to provide a theoretical background of big and open linked data analytics ecosystem and its essential elements. First, the key terms were introduced including related dimensions. Then, the key lifecycle phases were defined and involved stakeholders were identified. Finally, a conceptual framework was proposed. In contrast to previous research, the new ecosystem is formed by interactions of stakeholders in the following dimensions and their sub-dimensions: transparency, engagement, legal, technical, social, and economic. These relationships are characterized by the most important requisites and public policy choices affecting the data analytics ecosystem together with the key phases and activities of the data analytics lifecycle. The findings should contribute to relevant initiatives, strategies, and policies and their effective implementation.     

学习分析：从源起到实践与研究

学习分析是“大数据”在教育领域的应用,引发了教育技术发展的第三次浪潮,并获得学术界的广泛关注。本文梳理了学习分析的形成过程,然后从利益相关者、研究目标、研究对象、技术方法四个维度,回顾了近五年来国内外学者在学习分析方面的研究成果,并提出未来发展趋势和可能遇到的挑战,便于相关人员制定教育决策、优化教育管理过程以及完善学习过程。研究结果表明,学习分析研究主题主要涵盖学习者知识建模、学习情绪建模、学习行为特征抽取、学习活动跟踪、学习者建模、学位获取分析、教学资源和教学策略优化、自适应学习系统和个性化学习、在线学习影响因素分析九个方面;分析数据主要来源于集中式学习环境、分布式学习环境以及身体活动数据;常用分析方法包括统计分析、信息可视化、数据挖掘、社会网络分析、话语分析和网站分析。目前,学习分析研究遇到的挑战包括教育数据预处理难度大、数据访问权限不明确、学习分析适用性有限。虽然学习分析尚处于发展初期,但由于能够为教育系统各级决策提供科学参考,已经成为教育信息化的重要内容之一。