Setting learning analytics in context: overcoming the barriers to large-scale adoption

Once learning analytics have been successfully developed and tested, the next step is to implement them at a larger scale – across a faculty, an institution or an educational system. This introduces a new set of challenges, because education is a stable system, resistant to change. Implementing learning analytics at scale involves working with the entire technological complex that exists around technology-enhanced learning (TEL). This includes the different groups of people involved – learners, educators, administrators and support staff – the practices of those groups, their understandings of how teaching and learning take place, the technologies they use and the specific environments within which they operate. Each element of the TEL Complex requires explicit and careful consideration during the process of implementation, in order to avoid failure and maximise the chances of success. In order for learning analytics to be implemented successfully at scale, it is crucial to provide not only the analytics and their associated tools but also appropriate forms of support, training and community building

Expectation-Centered Analytics for Instructors and Students

Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts. An outcome and primary goal of learning analytics should be to inform instructors, who are primary stakeholders, so that they can make effective decisions in their courses. To support instructor inquiry, I apply theory on reflective practice to learning analytic development. Articulating an instructor\u27s pedagogical expectations is one way to begin facilitating a reflective practice. Expectations based on instructor goals serve as a natural next step and the springboard from which data can be collected. I hypothesize that a learning analytic that encodes and reifies instructors\u27 individual expectations will better support reflective practice for instructors and allow students to more reliably meet set expectations. I took a user-centered approach to learning analytic research and development. First I triangulated empirical analysis of analytic use with focus groups to understand how instructors interacted with analytics. Instructors had a wide range of behaviors, needs and expectations. For most instructors, analytics were used very briefly (less than 1 minute). Instructors also requested a way to aggregate data from different analytics to better support their information needs. Based on these findings, I developed learning analytics within TrACE to allow for instructors to specify expectations and see student progress related to those expectations. Students could also view their progress towards completing expectations. Finally, I conducted a field study to compare both instructor analytic use and student compliance to expectations without and with the presence of these analytics. The results of the field study did not support the hypothesis. Instructors for the most part did not change their behaviors with the introduction of these analytics. Students also did not meet expectations more reliably, but one course saw a significant improvement in performance. Without visible expectations, students met significantly fewer posting expectations than other expectations. With explicit expectations, posting performance was no longer significantly less

A conceptual analytics model for an outcome-driven quality management framework as part of professional healthcare education

BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach

How do professional learners engage with learning analytics dashboards?

Learning Analytics Dashboards (LAD) research is mainly carried out in schools and higher education, whilst professional learning has often been overlooked. To address this gap, this fine-grained longitudinal study of 12 professionals over a period of five months has taken exploratory steps into the context of professional accountancy learning. It investigates perceptions and use of a static assessment LAD which incorporates data visualization and personalized written feedback, aiming to purposefully affect learning. Overall, findings demonstrate learners took a positive view of and subsequently actively used the LAD, choosing elements directly related to their needs to inform next revision steps. Learning insights which both provide an understanding of past performance and also specifically recommend how the professional learners might improve performance had the highest frequency of use. The implication for the LAD design community is LAD should include context and learner specific elements, providing personalized next step guidance

What learning analytics based prediction models tell us about feedback preferences of students

Learning analytics (LA) seeks to enhance learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators (Siemens & Long, 2011). This study examined the use of preferred feedback modes in students by using a dispositional learning analytics framework, combining learning disposition data with data extracted from digital systems. We analyzed the use of feedback of 1062 students taking an introductory mathematics and statistics course, enhanced with digital tools. Our findings indicated that compared with hints, fully worked-out solutions demonstrated a stronger effect on academic performance and acted as a better mediator between learning dispositions and academic performance. This study demonstrated how e-learners and their data can be effectively re-deployed to provide meaningful insights to both educators and learners

Student profiling in a dispositional learning analytics application using formative assessment

How learning disposition data can help us translating learning feedback from a learning analytics application into actionable learning interventions, is the main focus of this empirical study. It extends previous work where the focus was on deriving timely prediction models in a data rich context, encompassing trace data from learning management systems, formative assessment data, e-tutorial trace data as well as learning dispositions. In this same educational context, the current study investigates how the application of cluster analysis based on e-tutorial trace data allows student profiling into different at-risk groups, and how these at-risk groups can be characterized with the help of learning disposition data. It is our conjecture that establishing a chain of antecedent-consequence relationships starting from learning disposition, through student activity in e-tutorials and formative assessment performance, to course performance, adds a crucial dimension to current learning analytics studies: that of profiling students with descriptors that easily lend themselves to the design of educational interventions

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201