Structural technological direction in AIS design for regional analytical services

Results are presented, which were acquired by the author during research and development activities on the design, adaptation, introduction and elaboration of automated information systems of different types and purposes used in a variety of technologies for planning and making managerial decisions, namely: national corporate STI information centers, departmental systems of management (DSM), scientific production associations, firm analysis systems (FAS), and information analytical services at the federal and regional levels.     

‘You can’t show impact with a new pair of shoes’: negotiating disadvantage through Pupil Premium

The Pupil Premium policy was introduced in 2010 by the UK coalition government to tackle the attainment gap disproportionately affecting children from low-income families. Semi-structured interviews and policy documents are examined for the way the policy has been enacted in a single comprehensive secondary school in England. In 2014, this school had a lower population of Pupil Premium pupils (18%) compared to (29%) nationally and (25%) countywide. Despite this, the study provides evidence that the Pupil Premium has become invested within and gives rise to, a number of neoliberal techniques, technologies and practices. The study bridges insights from Mitchell Dean’s ‘analytics of government’ and Ball et al.’s work on policy enactment to provide an in-depth, grounded analysis of the way the policy plays out within this school’s context. It argues that the combination of national accountability measures used to show impact for Pupil Premium, and the school’s ongoing struggle to raise overall attainment, leads school leaders and staff members to rethink the concept of disadvantage for their school population. This results in disadvantage being reconceptualised to fit a matrix of moral/pastoral obligations and efficiency/economic competitiveness, in which the tensions between these two orientations are uncomfortable and unresolved.     

A systematic review of visual representations for analyzing collaborative discourse

Visual analytics combines automated data analysis and human intelligence through visualisation techniques to address the complexity of current real-world problems. This review uses the lens of visual analytics to examine four dimensions of visual representations for analysing collaborative discourse: goals, data sources, visualisation designs, and analytical techniques based on 89 studies. We found visual analysis approaches to be suitable and advantageous for decomposing the temporality of collaborative discourse. However, it has been challenging for current research to simultaneously consider learning theories and follow visualisation design principles when adopting visualisations to analyse collaborative discourse. At the same time, existing visual analysis approaches have mainly targeted learners or researchers in online contexts and mainly focused on mirroring collaborative discourse rather than providing advanced affordances such as alerting or advising. Informed by these findings, we propose a possible future research agenda and offer suggestions for the features of successful collaboration to guide the design of advanced affordances.     

Unpacking MOOC scholarly discourse: a review of nascent MOOC scholarship

The rapid rise of MOOCs (Massive Open Online Courses) signals a shift in the ways in which digital teaching and learning are engaged in and understood. Drawing upon a comprehensive search of nine leading academic databases, this paper examines the initial phase of MOOC scholarship (2009–2013), and offers an analysis of these empirical studies that conceptualizes themes in MOOC scholarship and locates them within a chronological framework. Two key phases of scholarship about MOOCs are identified, each with associated research imperatives and themes. Phase One: Connectivist MOOCs, Engagement and Creativity 2009–2011/2012. Themes of Phase One include: development of Connectivism as a learning theory, and technological experimentation and innovation in early cMOOCs. Phase Two: xMOOCs, Learning Analytics, Assessment, and Critical Discourses about MOOCs 2012–2013. Themes of Phase Two include: the rise of xMOOCs, further development of MOOC pedagogy and platforms, growth of learning analytics and assessment, and the emergence of a critical discourse about MOOCs.     

学习分析:正在浮现中的数据技术

随着教育信息化的普及与逐渐深入,学习管理系统已经获取并存储了大量的有关学生复杂学习行为的数据,从这些数据中挖掘出改进教学系统、提升学习效果的信息,在教育信息化领域一直有着巨大的吸引力。因此,有必要从分析数据以改进学习的角度,对日益受到关注的学习分析技术进行解读。首先,学习分析技术是测量、收集、分析和报告有关学生的学习行为以及学习环境的数据,用以理解和优化学习及其产生的环境的技术。接着,综述学习分析技术的发展,指出其在教育中有着广泛的应用前景和巨大的发展潜力:学习分析技术可作为教师教学决策、优化教学的有效支持工具,也可为学生的自我导向学习、学习危机预警和自我评估提供有效数据支持,还可为教育研究者的个性化学习设计和增进研究效益提供数据参考。最后,提出学习分析技术也存在隐私、准确性和兼容性等诸多挑战和问题。     

Actualizing big data analytics for smart cities: A cascading affordance study

This study investigates how Big Data Analytics (BDA) can be leveraged to support a city’s transformation into a smart destination. We conduct an in-depth case study of a city-in-transformation and adopt the perspective of technology affordances to uncover the varying opportunities enabled by BDA to facilitate the attainment of smart tourism goals. Our findings unveil three types of BDA affordances and demonstrate how these affordances are actualized in a cascading manner to enable informed decisions and a sustainable development of smart tourism. Implications are presented for future investigation of the affordances of BDA in smart tourism, as well as for policy makers and practitioners who engage in the development of innovative tourism services for the smart citizens.     

Business intelligence and analytics for value creation: The role of absorptive capacity

Firms continuously report increased competitive value gains from the use of business intelligence and analytics (BI&A), however, little is known about how insights from BI&A are transformed to added value to date. We have conducted fourteen in-depth, semi-structured interviews with a sample of informants in CEO positions, IT managers, CIO, Heads of R&D, as well as Market Managers from nine medium or large-sized European firms. Applying the absorptive capacity’s theoretical lens, we have provided evidence that absorptive capacity’s capabilities are an underlying foundation in the process of transforming BI&A triggered insights into valuable knowledge. Moreover, this process is supported by technological, human, and relationship assets.     

Students' personal networks in virtual and personal learning environments: a case study in higher education using learning analytics approach

The main objective of this paper is to analyse the effect of the affordances of a virtual learning environment and a personal learning environment (PLE) in the configuration of the students' personal networks in a higher education context. The results are discussed in light of the adaptation of the students to the learning network made up by two undergraduate, inter-university and online courses. Besides, we also examine the influence of this effect in the learning process. The findings reflect the effectiveness of a PLE for facilitating student participation and for assisting students in the creation of larger and more balanced personal networks with richer social capital. However, the findings do not provide evidences about a difference in the learning performance between the two environments. From a methodological point of view, this paper serves as an illustration of the analysis of personal networks on digital data collected from technology-enhanced learning environments.     

Where to go: Computational and visual what-if analyses in soccer

ABSTRACT

To prepare their teams for upcoming matches, analysts in professional soccer watch and manually annotate up to three matches a day. When annotating matches, domain experts try to identify and improve suboptimal movements based on intuition and professional experience. The high amount of matches needing to be analysed manually result in a tedious and time-consuming process, and results may be subjective. We propose an automatic approach for the realisation of effective region-based what-if analyses in soccer. Our system covers the automatic detection of region-based faulty movement behaviour, as well as the automatic suggestion of possible improved alternative movements. As we show, our approach effectively supports analysts and coaches investigating matches by speeding up previously time-consuming work. We enable domain experts to include their domain knowledge in the analysis process by allowing to interactively adjust suggested improved movement, as well as its implications on region control. We demonstrate the usefulness of our proposed approach via an expert study with three invited domain experts, one being head coach from the first Austrian soccer league. As our results show that experts most often agree with the suggested player movement (83%), our proposed approach enhances the analytical capabilities in soccer and supports a more efficient analysis.     

The effectiveness of learning analytics for identifying at-risk students in higher education

Abstract

In this article we investigate the effectiveness of learning analytics for identifying at-risk students in higher education institutions using data output from an in-situ learning analytics platform. Amongst other things, the platform generates ‘no-engagement’ alerts if students have not engaged with any of the data sources measured for 14 consecutive days. We tested the relationship between these alerts and student outcomes for two cohorts of first-year undergraduate students. We also compared the efficiency of using these alerts to identify students at risk of poorer outcomes with the efficiency of using demographic data, using widening participation status as a case study example. The no-engagement alerts were found to be more efficient at spotting students not progressing and not attaining than demographic data. In order to investigate the efficacy of learning analytics for addressing differential student outcomes for disadvantaged groups, the team also analysed the likelihood of students with widening participation status generating alerts compared with their non-widening participation counterparts. The odds of students with widening participation status generating an alert were on average 43% higher, demonstrating the potential of such a system to preferentially target support at disadvantaged groups without needing to target directly based on immutable factors such as their socio-economic background.