A Challenge for the Developer: Issues of interactivity and linguistic-cognitive appropriateness in English language learning

It is argued that language learning ought to be interactive. The traditional language classroom provides a favourable interactive situation for language learners. By contrast, the distance education mode is limited in some ways regarding language learning. Necessarily, distance education involves, primarily, self-learning. Face-to-face learning opportunities are scant compared with traditional classroom-based learning. It is, therefore, a challenge for language professionals to explore new ways of teaching language, the speaking skills in particular, within the constraints of the distance education mode. Being able to master English--an increasingly important international language--provides a key to information and learning societies in the 21 st century. In this paper, I will suggest some ways of maximizing the amount of interactive activities in English language distance learning, based on my work-based experience as a course developer of a new English enhancement course offered by the Open University of Hong Kong. I will focus on the development of listening and speaking skills, which are in fact the most problematic areas--the speaking skill, in particular, involves two-way communication. I will consider the issue from the perspectives of learners' motivation, peer support and technological support. Finally, I will present findings of a survey on the learners and tutors' feedback on the teaching materials of this new English language enhancement course.     

学习分析技术:挖掘大数据时代下教育数据的价值

当前,大数据时代已经来临,教育领域同样积累了海量数据。教育领域已经部署了众多的学习管理系统,在这些软件系统中存储着海量的学习者信息及学习过程数据。如何利用这些数据,使这些数据转变为信息、知识,并为教学决策、学习优化服务,已成为教育工作者以及学习者们所关注的内容。学习分析技术有助于发挥学习过程数据的价值,使数据成为审慎决策、过程优化的重要依据。该文介绍了国内外学习分析技术研究现状,归纳出学习分析技术的关键技术及分析模式,并以实例从不同用户视角包括管理者、辅导教师、学习者展示了学习分析技术在网络学习过程分析中的应用过程。     

Bridging the gap between supply chain management practice and curriculum: A location analytics exercise

This teaching brief explores how and why location analytics should be taught in business schools using three objectives. First, an explanation is provided for the importance of including location analytics in the standard business school curriculum—especially in the field of supply chain management. Second, a lack of GIS-based location analytics methods in business school curricula is demonstrated. Third, a three-part location analytics exercise is introduced to contribute to the supply chain management curriculum. The proposed exercise utilizes the output of a GIS-based location analytics software (namely, Esri's ArcGIS Online) as the input for a location set covering problem that can be solved using an integer programming solver. The exercise can also be used as a stand-alone example of GIS in a supply chain management course. This teaching brief aims to (1) develop a new method to use in teaching location analytics in supply chain management and analytics courses and (2) bridge an important gap between supply chain management practice and curriculum.     

Student satisfaction with R vs. Excel in Data Mining and Business Analytics: A Herzberg's motivation-hygiene theory perspective

Applying Herzberg's motivation-hygiene theory, we studied the determinants of student satisfaction in using R in a Decision Support Systems course that previously used Excel to teach Data Mining and Business Analytics (DMBA). The course is a degree requirement, and prior programming experience is not a prerequisite. We hypothesized that motivators for student satisfaction with R would include: (i) an intrinsic interest in DMBA, (ii) a perception that R is a better tool than Excel for DMBA, and (iii) a favorable view of R as a facilitator of career advancement. We postulated that the hygiene factor was the compulsory course requirement to learn R, a new and challenging language to students in this study. Data from 120 students, analyzed using Principal Component Analysis (PCA) and Generalized Additive Modeling (GAM), showed that motivators were positively related to satisfaction while hygiene factors were neutral. Students showed willingness to take on a harder challenge in exchange for intrinsic and future career benefits. The model can be useful in curriculum design or career advisement to increase student satisfaction in learning new software skills while meeting market demands.     

How learners produce data from text in classifying clickbait

Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order to observe how students reason with text data in scenarios designed to elicit certain aspects of the domain, we employed a task-based interview method using a structured protocol with six pairs of undergraduate students. Our goal was to shed light on students' understanding of text as data using a motivating task to classify headlines as “clickbait” or “news.” Three types of features (function, content, and form) surfaced, the majority from the first scenario. Our analysis of the interviews indicates that this sequence of activities engaged the participants in thinking at both the human-perception level and the computer-extraction level and conceptualizing connections between them.     

Six Uses of Analytics: Digital Editors’ Perceptions of Audience Analytics in the Newsroom

ABSTRACT

This article investigates how digital news editors perceive the uses and implications of audience analytics in contemporary digital newsrooms. Based on 21 interviews with digital news editors at 11 Belgian news organisations, including 7 national newspapers, one news magazine, one public and one commercial broadcaster, and one digital-born news medium, the study shows how audience analytics have become normalised in these digital newsrooms and how, in the perception of those who use them, tools for capturing audience behaviour data inform and shape their daily work practices and organisational strategies. Combining insights from literature with empirical findings, the study distinguishes six uses of audience analytics: Not only do analytics inform editorial decisions on (1) story placement, (2) story packaging, (3) story planning and (4) story imitation, but they can also serve as instruments for (5) performance evaluation and (6) audience conception. Overall, the digital news editors are convinced that audience analytics support rather than harm their journalism.     

The role of business analytics capabilities in bolstering firms’ agility and performance

Many companies invest considerable resources in developing Business Analytics (BA) capabilities to improve their performance. BA can affect performance in many different ways. This paper analyses how BA capabilities affect firms’ agility through information quality and innovative capability. Furthermore, it studies the moderating role of environmental turbulence, both technological and in the market. The proposed model was tested using statistical data from 154 firms with two respondents (CEO and CIO) from each firm. The data were analysed using Partial Least Squares (PLS)/Structured Equation Modelling (SEM). Our results indicate that BA capabilities strongly impact a firm’s agility through an increase in information quality and innovative capability. We also discuss that both market and technological turbulence moderate the influence of firms' agility on firms' performance.     

Role of visual analytics in supporting mental healthcare systems research and policy: A systematic scoping review

The availability of healthcare data has exponentially grown, both in quantity and complexity. The speed of this evolution has generated new challenges for translating complex data into effective evidence-informed policy. Visual analytics offers new capacity to analyze healthcare systems and support better decision-making. We conducted a systematic scoping review to look for evidence of visual analytics approaches being applied to mental healthcare systems and their use in driving policy. We found 79 relevant studies and categorized them in two ways: by study purpose and by type of visualization. The majority (67.1%) of the studies used geographical maps, and 11% conducted highly complex studies requiring novel visualizations. Significantly, only 15% of the studies provided information indicating high levels of usability for policy and planning. Our findings suggest that while visual analytics continues to evolve rapidly, there is a need to ensure this evolution reflects the practical needs of policy makers.     

Theory building with big data-driven research – Moving away from the “What” towards the “Why”

Data availability and access to various platforms, is changing the nature of Information Systems (IS) studies. Such studies often use large datasets, which may incorporate structured and unstructured data, from various platforms. The questions that such papers address, in turn, may attempt to use methods from computational science like sentiment mining, text mining, network science and image analytics to derive insights. However, there is often a weak theoretical contribution in many of these studies. We point out the need for such studies to contribute back to the IS discipline, whereby findings can explain more about the phenomenon surrounding the interaction of people with technology artefacts and the ecosystem within which these contextual usage is situated. Our opinion paper attempts to address this gap and provide insights on the methodological adaptations required in “big data studies” to be converted into “IS research” and contribute to theory building in information systems.