A cognitive processing framework for learning analytics

Incorporating a learner’s level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom’s taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.

Designing an e-portfolio for assurance of learning focusing on adoptability and learning analytics

The Assurance of Learning for Graduate Employability framework is a quality assurance model for curriculum enhancement for graduate employability, enabling graduates to achieve "the skills, understandings and personal attributes that make [them] more likely to secure employment and be successful in their chosen occupations to the benefit of themselves, the workforce, the community and the economy" (Yorke, 2006). Of particular note is the framework's dependence on three foundations, including easy access to integrated and accessible tools for staff and student self-management. In other words, this approach to curriculum quality depends on staff and student access to tools that enable them to self-manage their learning. This paper examines two aspects which informed the design of a student e-portfolio system, iPortfolio, intended for students' self-management of their learning, particularly recording evidence of their achievement of capabilities. The paper focuses on two particular considerations in the design of the iPortfolio: adoptability and learning analytics. Adoptability means the phase preceding adoption, whether students have the devices, platforms and technology skills to be able to use such an innovation. The iPortfolio also facilitates learning analytics: it has the capability to gather data related to learning indicators for course quality assurance purposes. Both adoptability and analytics are very dynamic fields: new devices, platforms and applications constantly spark changes in user habits, and policy changes mean institutions need to be able to provide new data, often at short notice. In the conclusion, the paper suggests how tools such as the iPortfolio can be designed for 'future proofing' and sustainability.

A telecom analytics framework for dynamic quality of service management

Since the beginning of Internet, Internet Service Providers (ISP) have seen the need of giving to users? traffic different treatments defined by agree- ments between ISP and customers. This procedure, known as Quality of Service Management, has not much changed in the last years (DiffServ and Deep Pack-et Inspection have been the most chosen mechanisms). However, the incremen-tal growth of Internet users and services jointly with the application of recent Ma- chine Learning techniques, open up the possibility of going one step for-ward in the smart management of network traffic. In this paper, we first make a survey of current tools and techniques for QoS Management. Then we intro-duce clustering and classifying Machine Learning techniques for traffic charac-terization and the concept of Quality of Experience. Finally, with all these com-ponents, we present a brand new framework that will manage in a smart way Quality of Service in a telecom Big Data based scenario, both for mobile and fixed communications.

Enabling 21st century learning spaces : practical interpretations of the MCEETYA Learning Spaces Framework at Bounty Boulevard State School, Queensland, Australia

While the Queensland and Australian Governments have recognised the importance of new spaces for teaching and learning, particularly with the Rudd Government's Building the Education Revolution, the practical implementation of new spaces is largely left to schools and even individual teachers. This article proposes a theory for the consideration of 21st century learning spaces in relation to the learner, desired knowledge and understanding, digital technology and digital pedagogy. New and emerging learning spaces at Bounty Boulevard State School are analysed and critiqued through an analysis of the guiding principles offered by the 'Learning in an Online World: Learning Spaces Framework' (MCEETYA, 2008) publication, including flexibility, inclusivity, collaboration, creativity and efficiency. The argument put forward in this article is that 21st century learning spaces can be enabled while acknowledging barriers of resourcing and current ICT infrastructure.

Learning analytics beyond the LMS : the Connected Learning Analytics toolkit

We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. Core to the toolkit is the notion of learner access to their own data. A number of implementational issues are discussed, and an ontology of xAPI verb/object/activity statements as they might be unified across 7 different social media and online environments is introduced. After considering some of the analytics that learners might be interested in discovering about their own processes (the delivery of which is prioritised for the toolkit) we propose a set of learning activities that could be easily implemented, and their data tracked by anyone using the toolkit and a LRS.

Analysing reflective text for learning analytics : an approach using anomaly recontextualisation

Reflective writing is an important learning task to help foster reflective practice, but even when assessed it is rarely analysed or critically reviewed due to its subjective and affective nature. We propose a process for capturing subjective and affective analytics based on the identification and recontextualisation of anomalous features within reflective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Recontextualisation process for Learning Analytics.

FlexAnalytics: A flexible data analytics framework for big data applications with I/O performance improvement

Increasingly larger scale applications are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. Instead of moving data from its source to the output storage, in-situ analytics processes output data while simulations are running. However, in-situ data analysis incurs much more computing resource contentions with simulations. Such contentions severely damage the performance of simulation on HPE. Since different data processing strategies have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. In this paper, we explore and analyze several potential data-analytics placement strategies along the I/O path. To find out the best strategy to reduce data movement in given situation, we propose a flexible data analytics (FlexAnalytics) framework in this paper. Based on this framework, a FlexAnalytics prototype system is developed for analytics placement. FlexAnalytics system enhances the scalability and flexibility of current I/O stack on HEC platforms and is useful for data pre-processing, runtime data analysis and visualization, as well as for large-scale data transfer. Two use cases – scientific data compression and remote visualization – have been applied in the study to verify the performance of FlexAnalytics. Experimental results demonstrate that FlexAnalytics framework increases data transition bandwidth and improves the application end-to-end transfer performance.

Recipe for success — Lessons learnt from using xAPI within the Connected Learning Analytics toolkit

An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.

The Connected Learning Analytics toolkit

This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.

The application of learning analytics to improve learning: Questions from the academy

Since 2007, close collaboration between the Learning and Teaching Unit’s Academic Quality and Standards team and the Department of Reporting and Analysis’ Business Objects team resulted in a generational approach to reporting where QUT established a place of trust. This place of trust is where data owners are confident in date storage, data integrity, reported and shared. While the role of the Department of Reporting and Analysis focused on the data warehouse, data security and publication of reports, the Academic Quality and Standards team focused on the application of learning analytics to solve academic research questions and improve student learning. Addressing questions such as: • Are all students who leave course ABC academically challenged? • Do the students who leave course XYZ stay within the faculty, university or leave? • When students withdraw from a unit do they stay enrolled on full or part load or leave? • If students enter through a particular pathway, what is their experience in comparison to other pathways? • With five years historic reporting, can a two-year predictive forecast provide any insight? In answering these questions, the Academic Quality and Standards team then developed prototype data visualisation through curriculum conversations with academic staff. Where these enquiries were applicable more broadly this information would be brought into the standardised reporting for the benefit of the whole institution. At QUT an annual report to the executive committees allows all stakeholders to record the performance and outcomes of all courses in a snapshot in time or use this live report at any point during the year. This approach to learning analytics was awarded the Awarded 2014 ATEM/Campus Review Best Practice Awards in Tertiary Education Management for The Unipromo Award for Excellence in Information Technology Management.

Code of practice for learning analytics

Setting out the responsibilities of educational institutions to ensure that learning analytics is carried out responsibly, appropriately and effectively.

Making sense of game based user data: learning analytics in applied games

Digital learning games are useful educational tools with high motivational potential. With the application of games for instruction there comes the need of acknowledging learning game experiences also in the context of educational assessment. Learning analytics provides new opportunities for supporting assessment in and of educational games. We give an overview of current learning analytics methods in this field and reflect on existing challenges. An approach of providing reusable software assets for interaction assessment and evaluation in games is presented. This is part of a broader initiative of making available advanced methodologies and tools for supporting applied game development.