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.

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.

The user query based learning system for lifetime prediction of metallic components

Real-World Data Mining Applications generally do not end up with the creation of the models. The use of the model is the final purpose especially in prediction tasks. The problem arises when the model is built based on much more information than that the user can provide in using the model. As a result, the performance of model reduces drastically due to many missing attributes values. This paper develops a new learning system framework, called as User Query Based Learning System (UQBLS), for building data mining models best suitable for users use. We demonstrate its deployment in a real-world application of the lifetime prediction of metallic components in buildings

A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application

Critical success elements for the design and implementation of organisational e-learning

Organisations are engaging in e-learning as a mechanism for delivering flexible learning to meet the needs of individuals and organisations. In light of the increasing use and organisational investment in e-learning, the need for methods to evaluate the success of its design and implementation seems more important than ever. To date, developing a standard for the evaluation of e-learning appears to have eluded both academics and practitioners. The currently accepted evaluation methods for e-learning are traditional learning and development models, such as Kirkpatrick’s model (1976). Due to the technical nature of e-learning it is important to broaden the scope and consider other evaluation models or techniques, such as the DeLone and McLean Information Success Model, that may be applicable to the e-learning domain. Research into the use of e-learning courses has largely avoided considering the applicability of information systems research. Given this observation, it is reasonable to conclude that e-learning implementation decisions and practice could be overlooking useful or additional viewpoints. This research investigated how existing evaluation models apply in the context of organisational e-learning, and resulted in an Organisational E-learning success Framework, which identifies the critical elements for success in an e-learning environment. In particular this thesis highlights the critical importance of three e-learning system creation elements; system quality, information quality, and support quality. These elements were explored in depth and the nature of each element is described in detail. In addition, two further elements were identified as factors integral to the success of an e-learning system; learner preferences and change management. Overall, this research has demonstrated the need for a holistic approach to e-learning evaluation. Furthermore, it has shown that the application of both traditional training evaluation approaches and the D&M IS Success Model are appropriate to the organisational e-learning context, and when combined can provide this holistic approach. Practically, this thesis has reported the need for organisations to consider evaluation at all stages of e-learning from design through to implementation.

The learning capability of construction organisations engaged in collaborative contracting - Literature Review [industry report]

Background Value for money (VfM) on collaborative construction projects is dependent on the learning capabilities of the organisations and people involved. Within the context of infrastructure delivery, there is little research about the impact of organisational learning capability on project value. The literature contains a multiplicity of often un-testable definitions about organisational learning abilities. This paper defines learning capability as a dynamic capability that participant organisations purposely develop to add value to collaborative projects. The paper reports on a literature review that proposes a framework that conceptualises learning capability to explore the topic. This work is the first phase of a large-scale national survey funded by the Alliancing Association of Australasia and the Australian Research Council. Methodology Desk-top review of leading journals in the areas of strategic management, strategic alliances and construction management, as well as recent government documents and industry guidelines, was undertaken to synthesise, conceptualise and operationalise the concept of learning capability. The study primarily draws on the theoretical perspectives of the resource-based view of the firm (e.g. Barney 1991; Wernerfelt 1984), absorptive capacity (e.g. Cohen and Levinthal 1990; Zahra and George 2002); and dynamic capabilities (e.g. Helfat et al. 2007; Teece et al. 1997; Winter 2003). Content analysis of the literature was undertaken to identify key learning routines. Content analysis is a commonly used methodology in the social sciences area. It provides rich data through the systematic and objective review of literature (Krippendorff 2004). NVivo 9, a qualitative data analysis software package, was used to assist in this process. Findings and Future Research The review process resulted in a framework for the conceptualisation of learning capability that shows three phases of learning: (1) exploratory learning, (2) transformative learning and (3) exploitative learning. These phases combine both internal and external learning routines to influence project performance outcomes and thus VfM delivered under collaborative contracts. Sitting within these phases are eight categories of learning capability comprising knowledge articulation, identification, acquisition, dissemination, codification, internationalisation, transformation and application. The learning routines sitting within each category will be disaggregated in future research as the basis for measureable items in a large-scale survey study. The survey will examine the extent to which various learning routines influence project outcomes, as well as the relationships between them. This will involve identifying the routines that exist within organisations in the construction industry, their resourcing and rate of renewal, together with the extent of use and perceived value within the organisation. The target population is currently estimated to be around 1,000 professionals with experience in relational contracting in Australia. This future research will build on the learning capability framework to provide data that will assist construction organisations seeking to maximise VfM on construction projects.

Curiosity driven reinforcement learning for motion planning on humanoids

Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.

A preliminary investigation into technology and processes facilitating the assurance of learning

This paper reports on the outcomes from a preliminary evaluation of technologies and processes intended to support the Assurance of Learning initiative in the business faculty of an Australian university. The study investigated how existing institutional information systems and operational processes could be used to support direct measures of student learning and the attainment of intended learning goals. The levels at which learning outcomes had been attained were extracted from the University Learning Management System (LMS), based on rubric data for three assessments in two units. Spreadsheets were used to link rubric criteria to the learning goals associated with the assessments as identified in a previous curriculum mapping exercise, and to aggregate the outcomes. Recommendations arising from this preliminary study are made to inform a more comprehensive pilot based on this approach, and manage the quality of student learning experiences in the context of existing processes and reporting structures.

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'.