The logic of data-sense: thinking through Learning Personalisation

Big Data and Learning Analytics’ promise to revolutionise educational institutions, endeavours, and actions through more and better data is now compelling. Multiple, and continually updating, data sets produce a new sense of ‘personalised learning’. A crucial attribute of the datafication, and subsequent profiling, of learner behaviour and engagement is the continual modification of the learning environment to induce greater levels of investment on the parts of each learner. The assumption is that more and better data, gathered faster and fed into ever-updating algorithms, provide more complete tools to understand, and therefore improve, learning experiences through adaptive personalisation. The argument in this paper is that Learning Personalisation names a new logistics of investment as the common ‘sense’ of the school, in which disciplinary education is ‘both disappearing and giving way to frightful continual training, to continual monitoring'.     

The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage

The number of firms that intend to invest in big data analytics has declined and many firms that invested in the use of these tools could not successfully deploy their project to production. In this study, we leverage the valence theory perspective to investigate the role of positive and negative valence factors on the impact of bigness of data on big data analytics usage within firms. The research model is validated empirically from 140 IT managers and data analysts using survey data. The results confirm the impact of bigness of data on both negative valence (i.e., data security concern and task complexity), and positive valence (i.e., data accessibility and data diagnosticity) factors. In addition, findings show that data security concern is not a critical factor in using big data analytics. The results also show that, interestingly, at different levels of data security concern, task complexity, data accessibility, and data diagnosticity, the impact of bigness of data on big data analytics use will be varied. For practitioners, the findings provide important guidelines to increase the extent of using big data analytics by considering both positive and negative valence factors.     

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.     

Examining the Relationship between Social Media Analytics Practices and Business Performance in the Indian Retail and IT Industries: The Mediation Role of Customer Engagement

Social media analytics (SMA) is a dynamic field which has received considerable attention from both academics and management practitioners alike. A significant number of the scholarly research currently being conducted in SMA, however, is conceptual. Industry experts know that SMA creates new opportunities for organisations who want to more strongly engage with their customers and improve business performance. However, the relationship between social media analytic practices (SMAP), customer engagement (CE), and business performance (BP) has not yet been sufficiently investigated from an empirical perspective. In order to gain a better understanding of the relationship between SMAP and BP and the mediation role of CE in that process, a large-scale survey was conducted among senior and mid-level managers as well as consultants in the Retail and information technology (IT) industries in India. Specifically, a structured closed-ended questionnaire was administered to managers and management consultants country-wide and gathered usable responses from 281 respondents holding positions such as: Digital Marketing Executive/Digital Marketing Specialist, Management Consultant, Analytics Manager, Customer Relationship Manager, Marketing Director, Engagement Manager, etc. who were in charge of digital marketing strategies in the respondent retail and IT organisations. The questionnaire addressed issues related to the way in which SMAP contribute to an enhanced business performance through the mediation role of customer engagement. Structural Equation Modelling was employed to analyse the received empirical data. On the basis of the findings our research concludes that there is a significant positive relationship between SMAP and BP mediated by CE in the Indian retail and IT industries.     

A Study of Detecting Student Engagement Modes in an Online Learning Platform

In this paper, we used the platform log data to extract three features (proportion of passive video time, proportion of active video time, and proportion of assignment time) aligning with different learning activities in the Interactive- Constructive-Active-Passive (ICAP) framework, and applied hierarchical clustering to detect student engagement modes. A total of 840 learning rounds were clustered into four categories of engagement: passive (n = 80), active (n = 366),constructive (n = 75) and resting (n = 319). The results showed that there were differences in the performance of the four engagement modes, and three types of learning status were identified based on the sequences of student engagement modes: difficult, balanced and easy. This study indicated that based on the ICAP framework, the online learning platform log data could be used to automatically detect different engagement modes of students, which could provide useful references for online learning analysis and personalized learning.     

CSCL情境中的群体感知工具:设计类型与效果评估

为了帮助学习者建立在CSCL情境中的群体感知意识,研究者往往会设计群体感知工具,为学习者呈现协作过程中的情境化信息,以此触发高质量的协作学习生成。通过对近15年30篇国外采用群体感知工具开展的实证研究进行系统分析,探讨了不同类型(认知型、行为型和社会型)的群体感知工具如何支持学习者的协作学习过程,具体从信息来源、信息可比性、细粒度、表征方法等维度对这些工具进行了较为详细的对比分析,并介绍了工具对协作过程、群体绩效和个人绩效三方面的影响。最后基于文献分析的结果,从信息可比性和人机协同两方面提出了未来潜在的研究方向。     

Speaking the unspoken in learning analytics: troubling the defaults

Abstract

Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments about the quality of learning. Learning analytics fall in the nexus between assessment of and for learning. As such it has the potential to deliver value in the form of (1) understanding student learning, (2) analysing learning behaviour (looking to identify not only factors that may indicate risk of failing, but for opportunities to deepen learning), (3) predicting students-at-risk (or identifying where students have specific learning needs), and (4) prescribing elements to be included to ensure not only the effectiveness of teaching, but also of learning. Learning analytics have underlying default positions that may not only skew their impact but also impact negatively on students in realising their potential. We examine a selection of default positions and point to how these positions/assumptions may adversely affect students’ chances of success, deepening the understanding of learning.