学习分析视域下S-ISAL在大学英语混合式教学中的构建与评估

学习分析技术在教育教学中的应用,其根本目的是为了构建新的教学法,更新教学与管理的方式。针对高校大学英语线上、线下混合教学无法有机融合的现状,S-ISAL（Space-based Information search and Analysis Learning）应运而生,它是基于网络学习空间的信息搜集与分析的教学模式,将学习分析技术引入大学英语混合式课程中。探究该教学模式在大学英语教学中的适切性和实效性是文章的研究重点,目的是为了实现大学英语线上、线下教学优势互补,为大学生在线英语学习提供更具个性化的学习内容和路径,并提升大学生自适应学习能力和在线学习绩效。     

Getting to Where You Want to Go: Designing Information Systems and Using Your Data Through the Application of Analytics and Informatics

ABSTRACT

This article, based on the closing session from the 2017 NC Serials Conference, describes a systems perspective where data serve as both real-time and summative information and knowledge about how an organization is performing, what services and resources its users are using and interacting with, and areas for which the organization is doing well or needs improvement. The author posits that data are most useful when the context is informed by clear goals that provide a direction to where the organization is and wants to be. Applied examples are discussed for several environments, including an academic department, school of education, regional libraries, and public libraries.     

Racing to a Bright Future

Abstract

Serials Review visits the National Baseball Hall of Fame and Museum Library in Cooperstown, NY, which opened in 1939 with two shelves of donated books. The museum is now home to the largest collection of baseball information in the world.     

A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.     

基于在线数据的网络调查研究方法

在线数据分析法是依赖于网络技术,对网民网络行为的海量数据进行统计分析的一种调查研究方法。这种方法基于网站流量监测系统及浏览跟踪系统自动获得的网民行为数据,对网民的网络行为进行统计分析,更容易掌握网民的真实想法甚至是无意识的行为。按获取数据位置的不同,可以将在线数据分析法分为网站流量监测法、网民行为跟踪法及搜索引擎关键词统计法三种主要方法。网站流量监测法利用网站流量监测系统对来访网民的行为进行全面的记录和统计;网民行为跟踪法利用客户端插件对网民样本进行记录分析,以推断整体网民的网络行为;搜索引擎则对巨量网民的搜索行为进行记录和统计,为人们了解网民行为提供客观数据。     

一阶微分方程奇解的存在性及其求法

本文对相当广泛的一类一阶微分方程研究奇解的存在问题,提出一种不借助几何直观,而是用分析方法寻求一阶微分方程的奇解.     

Mitigating Sports Injury Risks Using Internet of Things and Analytics Approaches

Sport injuries restrict participation, impose a substantial economic burden, and can have persisting adverse effects on health‐related quality of life. The effective use of Internet of Things (IoT), when combined with analytics approaches, can improve player safety through identification of injury risk factors that can be addressed by targeted risk reduction training activities. Use of IoT devices can facilitate highly efficient quantification of relevant functional capabilities prior to sport participation, which could substantially advance the prevailing sport injury management paradigm. This study introduces a framework for using sensor‐derived IoT data to supplement other data for objective estimation of each individual college football player's level of injury risk, which is an approach to injury prevention that has not been previously reported. A cohort of 45 NCAA Division I‐FCS college players provided data in the form of self‐ratings of persisting effects of previous injuries and single‐leg postural stability test. Instantaneous change in body mass acceleration (jerk) during the test was quantified by a smartphone accelerometer, with data wirelessly transmitted to a secure cloud server. Injuries sustained from the beginning of practice sessions until the end of the 13‐game season were documented, along with the number of games played by each athlete over the course of a 13‐game season. Results demonstrate a strong prediction model. Our approach may have strong relevance to the estimation of injury risk for other physically demanding activities. Clearly, there is great potential for improvement of injury prevention initiatives through identification of individual athletes who possess elevated injury risk and targeted interventions.     

Envisioning the power of data analytics

It could be argued that the power of data is located in what they are used to reveal. Yet we have little understanding of the role played by the emerging industry of data analytics in the interpretation and use of big data. These data analytics companies act as intermediaries in the digital data revolution. Understanding the social influence of big data requires us to understand the role played by data analytics within organisations of different types. This particular article focuses very specifically upon the way in which data and data analytics are envisioned within the marketing rhetoric of the data analytics industry. It is argued that to understand the spread of data analytics and the adoption of certain analytic strategies, we first need to look at the projection of promises upon that data. The way that data and analytics are imagined shapes their incorporation and appropriation into practices and organisational structures – what I call here the data frontiers. This article draws upon a sample of 34 data analytics companies in order to explore the way in which data analytics are envisioned within that increasingly powerful industry.     

Computing brains: learning algorithms and neurocomputation in the smart city

ABSTRACT

This article examines IBM’s ‘Smarter Education’ program, part of its wider ‘Smarter Cities’ agenda, focusing specifically on its learning analytics applications (based on machine learning algorithms) and cognitive computing developments for education (which take inspiration from neuroscience for the design of brain-like neural networks algorithms and neurocomputational devices). The article conceptualizes the relationship between learning algorithms, neuroscience, and the new learning spaces of the city by combining the notion of programmable ‘code/space’ with ideas about the ‘social life of the brain’ to suggest that new kinds of ‘brain/code/spaces’ are being developed where the environment itself is imagined to possess brain-like functions of learning and ‘human qualities’ of cognition performed by algorithmic processes. IBM’s ambitions for education constitute a sociotechnical imaginary of a ‘cognitive classroom’ where the practices associated with data analytics and cognitive computing in the smart city are being translated into the neuropedagogic brain/code/spaces of the school, with significant consequences for how learners are to be addressed and acted upon. The IBM imaginary of Smarter Education is one significant instantiation of emerging smart cities that are to be governed by neurocomputational processes modelled on neuroscientific insights into the brain’s plasticity for learning, and part of a ‘neurofuture’ in-the-making where nonconscious algorithmic ‘computing brains’ embedded in urban space are intended to interact with human cognition and brain functioning.