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.     

联结学习设计与学习分析:教师技术创新教学的突破口——访西班牙巴利亚多利德大学雅尼斯·迪米特里亚迪斯教授

雅尼斯·迪米特里亚迪斯(Yannis Dimitriadis)教授任职西班牙巴利亚多利德大学远程信息技术工程系,兼任信息通讯技术与教学研究中心GSIC/EMIC研究团队负责人,主持和参与了五十多项国际国内科研项目,在教育技术顶级期刊发表了八十多篇学术论文,组织了多次学习科学高端国际学术专题研讨会和工作坊。研究方向包括学习设计、计算机支持协作学习、学习分析等。本次访谈着重于介绍学习设计的核心思想,即无论外部学习技术如何变化,“教学法为王”的原则在教育教学情境中的主导地位不能变。因此,人工智能时代的学习技术仍需为创新教学服务。从教师角度看,联结学习设计与学习分析为教师技术创新教学提供了突破口。一方面,学习设计为学习分析提供理论引导,便于教师精准知晓学习者在不同学习活动中的学习情况,另一方面,学习分析为优化学习设计提供基于数据驱动的方法指导,便于教师靶向式更新学习活动设计。践行学习设计与学习分析双向互动的发展路径,需要建立研究者与教师合作设计机制,引导教师参与学习设计;推进隐性设计知识在学习设计中的应用;关注教师探究学习者学习过程。本次访谈对新时代背景下信息技术与教学应用的深度融合提供了新的理念和方法指导。     

Analyzing the public discourse on works of fiction – Detection and visualization of emotion in online coverage about HBO’s Game of Thrones

This paper presents a Web intelligence portal that captures and aggregates news and social media coverage about “Game of Thrones”, an American drama television series created for the HBO television network based on George R.R. Martin’s series of fantasy novels. The system collects content from the Web sites of Anglo-American news media as well as from four social media platforms: Twitter, Facebook, Google+ and YouTube. An interactive dashboard with trend charts and synchronized visual analytics components not only shows how often Game of Thrones events and characters are being mentioned by journalists and viewers, but also provides a real-time account of concepts that are being associated with the unfolding storyline and each new episode. Positive or negative sentiment is computed automatically, which sheds light on the perception of actors and new plot elements.     

Multimodal learning analytics to investigate cognitive load during online problem solving

To have insight into cognitive load (CL) during online complex problem solving, this study aimed at measuring CL through physiological data. This study experimentally manipulated intrinsic and extraneous load of exercises in the domain of statistics, resulting in four conditions: high complex with hints, low complex with hints, high complex without hints and low complex without hints. The study had a within-subject-design in which 67 students solved the exercises in a randomized order. Self-reported CL was combined with physiological data, namely, galvanic skin response (GSR), skin temperature (ST), heart rate (HR) and heart rate variability (HRV). Multiple imputation was used for handling missing data from resp. 16 and 19 students for GSR/ST and HR/HRV. First, differences between conditions in view of physiological data were examined. Second, we investigated how much variance of self-reported CL and task performance was explained by physiological data. Finally, we investigated which features can be used to assess (objective) CL. Results revealed no significant differences between the manipulated conditions in terms of physiological data. Nonetheless, HR and ST were significantly related to self-reported CL, whereas ST to task performance. Additionally, this study revealed the potential of ST and HR to assess high CL.     

How does open government data driven co-creation occur? Six factors and a ‘perfect storm’; insights from Chicago's food inspection forecasting model

It is becoming increasingly clear that the concepts of open government data (OGD) and co-creation are related; however, there is currently only limited empirical material available exploring the link between the two. This paper aims to help clarify the relationship between these two concepts by exploring a recently coined phenomenon: OGD-driven co-created public services. These services 1) utilize or are driven by OGD; 2) are co-created by stakeholders from different groups; and 3) produce public value for society. Due to the relative newness of the phenomenon an inductive exploratory case study is undertaken on Chicago's use of OGD in the co-creation of their food safety inspection forecasting model. This model forecasts critical food safety violations at food serving establishments and sends inspectors to the highest risk establishments first. The results of this exploratory work led to the discovery of a ‘perfect storm’ of six factors that seem to play a key role in allowing OGD-driven public service co-creation to take place. These factors are motivated stakeholders, innovative leaders, proper communication, an existing OGD portal, external funding, and agile development.     

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.