A framework for strategic planning of data analytics in the educational sector

The field of big data and data analysis is not a new one. Big data systems have been investigated with respect to the volume of the data and how it is stored, the data velocity and how it is subject to change, variety of data to be analysed and data veracity referring to integrity and quality. Higher Education Institutions (HEIs) have a significant range of data sources across their operations and increasingly invest in collecting, analysing and reporting on their data in order to improve their efficiency. Data analytics and Business Intelligence (BI) are two terms that are increasingly popular over the past few years in the relevant literature with emphasis on their impact in the education sector. There is a significant volume of literature discussing the benefits of data analytics in higher education and even more papers discussing specific case studies of institutions resorting on BI by deploying various data analytics practices. Nevertheless, there is a lack of an integrated framework that supports HEIs in using learning analytics both at strategic and operational level. This research study was driven by the need to offer a point of reference for universities wishing to make good use of the plethora of data they can access. Increasingly institutions need to become ‘smart universities’ by supporting their decisions with findings from the analysis of their operations. The Business Intelligence strategies of many universities seems to focus mostly on identifying how to collect data but fail to address the most important issue that is how to analyse the data, what to do with the findings and how to create the means for a scalable use of learning analytics at institutional level. The scope of this research is to investigate the different factors that affect the successful deployment of data analytics in educational contexts focusing both on strategic and operational aspects of academia. The research study attempts to identify those elements necessary for introducing data analytics practices across an institution. The main contribution of the research is a framework that models the data collection, analysis and visualisation in higher education. The specific contribution to the field comes in the form of generic guidelines for strategic planning of HEI data analytics projects, combined with specific guidelines for staff involved in the deployment of data analytics to support certain institutional operations. The research is based on a mixed method approach that combines grounded theory in the form of extensive literature review, state-of-the-art investigation and case study analysis, as well as a combination of qualitative and quantitative data collection. The study commences with an extensive literature review that identifies the key factors affecting the use of learning analytics. Then the research collected more information from an analysis of a wide range of case studies showing how learning analytics are used across HEIs. The primary data collection concluded with a series of focus groups and interviews assessing the role of learning analytics in universities. Next, the research focused on a synthesis of guidelines for using learning analytics both at strategic and operational levels, leading to the production of generic and specific guidelines intended for different university stakeholders. The proposed framework was revised twice to create an integrated point of reference for HEIs that offers support across institutions in scalable and applicable way that can accommodate the varying needs met at different HEIs. The proposed framework was evaluated by the same participants in the earlier focus groups and interviews, providing a qualitative approach in evaluating the contributions made during this research study. The research resulted in the creation of an integrated framework that offers HEIs a reference for setting up a learning analytics strategy, adapting institutional policies and revising operations across faculties and departments. The proposed C.A.V. framework consists of three phases including Collect, Analysis and Visualisation. The framework determines the key features of data sources and resulting dashboards but also a list of functions for the data collection, analysis and visualisation stages. At strategic level, the C.A.V. framework enables institutions to assess their learning analytics maturity, determine the learning analytics stages that they are involved in, identify the different learning analytics themes and use a checklist as a reference point for their learning analytics deployment. Finally, the framework ensures that institutional operations can become more effective by determining how learning analytics provide added value across different operations, while assessing the impact of learning analytics on stakeholders. The framework also supports the adoption of learning analytics processes, the planning of dashboard contents and identifying factors affecting the implementation of learning analytics

Improving educational and training programmes through learning analytics and visualisation of educational data

This paper provides a step-by-step approach on how institutions can use their information for improving educational provision and subsequently becoming more competitive in the higher education sector. The paper focuses on three scenarios in explaining how the visualisation of data that are available in higher education institutions can become a key element for gaining competitive advantage

Google Glass as a learning tool: sharing evaluation results for the role of optical head mounted displays in education

This paper provides an overview of the findings from an evaluation of the role of Google Glass in education over the past three years. The authors have experimented with Optical Head Mounted Displays as a support tool for various learning activities over the past few years. The study described in the paper commenced back in 2014 and continued despite the fact that the development of the Google Glass technology was paused and then shifted towards enterprise clientele. This was a result of our confidence that the future of learning interfaces is aligned to the proliferation of augmented reality and the fact that the Google Glass interface offers an ideal tool for learners due to its light structure and seamless wearing experience. The paper discusses how Google Glass has been used for a range of learning activities and describes the learners’ experiences from using the device. The main contribution of the paper is in the form of measuring the success of the specific interface by sharing the results of three years of evaluations. The evaluation results are further analysed taking under consideration a number of profiling techniques of the learners involved including their personality type and learning style

THE COMPETITION IN PRESCHOOL AGE: A SHORT REVIEW

Competition matters in our daily life. Humans compete in their jobs, in their relationships, in the classroom etc. for different reason and with different way. We know a lot about competition for the adults but we lack of knowledge on what is happening during the preschool age and this is the focus of the present paper. The literature review shows that young children from the age of 4 years old perceive the concept of competition and express competitive behaviour. The factors which affect young children’s competitive behaviour are the gender, the age and the composition of the team as to gender and size. Moreover, during the implementation of the curriculum children express competitive behaviour in kindergarten classroom. They express competitive behaviours, which are divided into two main categories, verbally and physically, which include and subcategories. Competitive behaviour is expressed by children more often during organized activities and less during free activities, like breakfast time and discussion.  Article visualizations

The role of data analytics in managing higher education quality

This paper provides a range of scenarios where data analytics can be used to help institutions to improve the quality of their provision. In this paper the authors share some key benefits from using analysis of institutional data and will explain how the visualisation of educational information can help institutions in decision-making, as well as determine areas of concern

Preschoolers' perceptions of performance and satisfaction under competitive and non-competitive conditions

The aim of this study was to explore preschool children's perceptions of their performance under competitive and non-competitive conditions (NCC) and their satisfaction. Eighty preschool children (40 boys, 40 girls) aged 4–6 years (M age = 5.48, SD =.57) took part in this study. Preschool children built a tower under competitive and NCC and expressed their perception of their performance and their satisfaction using a ladder scale and a faces scale, respectively. The results showed that the majority of preschool children evaluate their performance as ‘high' under both conditions independently of the outcome. Under competitive condition (CC), 52.5% of children evaluated their performance accurately, while under NCC this percentage was 47.5%. Furthermore, the majority of children responded that they felt happy under both conditions. © 2014 Taylor & Francis

Evaluating the use of augmented reality in learning portfolios for different team roles

Increasingly augmented reality becomes an integrated component of modern learning environments. This paper builds on previous work to focus on the evaluation of how augmented reality and in particular the use of an Optical Head Mounted Display (OHMD) for the creation of learning portfolios. Emphasis is given on the evaluation of the use of such tools for different learning activities. The paper provides an in depth analysis of how the technology is evaluated by using Belbin's team role theory for classifying participants' responses. The paper's final contribution is in the form of a discussion of a range of issues associated with privacy and security of personal information collected with the use of OHMD in learning environments

Offering smarter learning support through the use of biometrics

The Internet of Things (IoT) has evolved into a mainstream keyword describing the use of interconnected device for data transfer. The authors present their current research study that aims at collecting biometric data from learners and using them for providing innovative feedback for a number of learning tasks. The paper discusses how measuring Galvanic Skin Response heartbeat rate and voice patterns can help to provide an alternative type of learner support. The discussion also covers how biometrics data are filtered by applying a number of profiling techniques to classify learners in different groupings. The paper also briefly touches on hardware aspects of the work carried out, as well as analysis of data sets from a student cohort

Assessing preschool children's competitive behaviour: an observational system

The aim of this study was to develop a direct observational system in order to assess competitive behaviours in preschool children. Participants were 176 children (90 boys, 86 girls; Mage = 5.2 years) from 10 kindergarten classes of one town of Central Greece. A new observational system (Observational System Assessing Competition in Kindergarten) was developed for the objective measurement of children's competitive behaviours. This system will allow researchers to monitor and evaluate children's competitive behaviour in kindergarten classes. Preliminary direct observation data are presented in order to illustrate the potential uses of the observational system. Results showed that boys express more often competitive behaviours than do girls. Furthermore, the majority of competitive behaviours were observed during organised activities and much less during free activities, breakfast time and discussion. © 2014, © 2014 Taylor & Francis