Learning Analytics
Applying web-tracking tools to improve student engagement and provide a high-quality, personalized experience for learners.
Using Big Data to Predict Student Behavior
Learning analytics is an educational application of “big data,” a branch of statistical analysis that was originally developed as a way for businesses to analyze commercial activities, identify spending trends, and predict consumer behavior.
Web-Tracking Tools to Tailor Learning Experience
As web-tracking tools became more sophisticated, many companies built vast reserves of information to individualize the consumer experience. Education is embarking on a similar pursuit into new ways of applying to improve student engagement and provide a high-quality, personalized experience for learners.
Learning Analytics - Informed Decisions
Learning analytics research uses data analysis to inform decisions made on every tier of the education system, leveraging student data to deliver personalized learning, enable adaptive pedagogies and practices, and identify learning issues in time for them to be solved.
Improving Educational Assessment With "Big Data"
Other hopes are that the analysis of education-related data on a much larger scale than ever before can provide policymakers and administrators with indicators of local, regional, and national education progress that can allow programs and ideas to be measured and improved.
Tracking and Ranking - Online Content Accessed
Adaptive learning data is already providing insights about student interactions with online texts and courseware. One pathway to creating the level of data needed for effective learning analytics is seen in creating student devices that will capture data on how, when, and in what context they are used, and thus begin to build school-level, national, and even international datasets that can be used to deeply analyze student learning, ideally as it happens.
Interest of Education Policymakers, Leaders and Practitioners
Since the topic first appeared three years ago in the far-term horizon of the NMC Horizon Report: 2011 Higher Education Edition, learning analytics has steadily captured the interest of education policymakers, leaders, and practitioners.
Same Tailored Consumer Experience as Amazon, Netflix and Google
Big data are now being used to personalize every experience users have on commercial websites, and education systems, companies, and publishers see tremendous potential in the use of similar data mining techniques to improve learning outcomes. The idea is to use data to adapt instruction to individual learner needs in real-time in the same way that Amazon, Netflix, and Google use metrics to tailor recommendations to consumers.
Transforming Education from "One Size Fits All"
Analytics can potentially help transform education from a standard one-size-fits-all delivery system into a responsive and flexible framework, crafted to meet the students’ academic needs and interests. For many years, these ideas have been a central component of adaptive software, programs that make carefully calculated adjustments to keep learners motivated as they master concepts or encounter stumbling blocks.
Visual and Analytic Reports
New kinds of visualizations and analytical reports are being developed to guide administrative and governing bodies with empirical evidence as they target areas for improvement, allocate resources, and assess the effectiveness of programs, schools, and entire school systems. As online learning environments increasingly accommodate thousands of students, researchers and companies are looking at very granular data around student interactions, building on the tools of web analytics.
Stanford Pioneering Education "Big Data" Analysis
Pearson Learning Studio, for example, provides an LMS infrastructure that is aggregating data from the millions of learners using their systems, with the aim of enabling school leaders and national policy makers to more effectively design personalized learning paths. Similarly, a group at Stanford University is examining vast datasets generated by online learning environments.
Stanford Lytic Lab - Analytics Dashboard
These efforts are taking place through the Stanford Lytic Lab, where researchers, educators, and visiting experts are currently building an analytics dashboard that will help online instructors track student engagement in addition to conducting a study of peer assessment in a MOOC on human-computer interaction, based on 63,000 peer-graded assignments.
Bill & Melinda Gates - $200,000 Funding
In April 2013, the Bill & Melinda Gates Foundation awarded Stanford more than $200,000 in funding to support the Learning Analytics Summer Institute, which provided professional training to researchers in the field.
Sophisticated Web-Tracking at Leading Institutions
Sophisticated web-tracking tools are already being used by leading institutions to capture precise student behaviors in online courses, recording not only simple variables such as time spent on a topic, but also much more nuanced information that can provide evidence of critical thinking, synthesis, and the depth of retention of concepts over time.
New Analytic Tools to Manage Growing Complexity
As behavior specific data is added to an ever-growing repository of student-related information, the analysis of educational data is increasingly complex, and many statisticians and researchers are working to develop new kinds of analytical tools to manage that complexity.
Predictive Analytics Reporting Framework
The most visible current example of a wide-scale analytics project in higher education is the Predictive Analytics Reporting Framework, which is overseen by the Western Interstate Commission for Higher Education (WICHE), and largely funded by the Bill & Melinda Gates Foundation.
16 Institutions = 1.7M Student Records + 8.1M Course Records
The 16 participating institutions represent the public, private, traditional, and progressive spheres of education. According to the WICHE website, they have compiled over 1,700,000 student records and 8,100,000 course level records in efforts to better understand student loss and student momentum.
Social Media - Researching Online Discussions
Companies such as X-Ray Research are conducting research in online discussion groups to determine which behavioral variables are the best predictors of student performance. The tools reflect the potential of analytics to develop early warning systems based on metrics that make predictions using linguistic, social, and behavioral data. Similarly, studies at universities are proving that pedagogies informed by analytics can prove the quality of interaction taking place online.
Solution: Visualization - Improving Student Engagement and Discussion
At Simon Fraser University in British Columbia researchers applied analytics to solve an issue that past experiments revealed — discussion forums used for online courses were not supporting productive engagement or discussion. They developed a Visual Discussion Forum in which students could visualize the structure and depth of the discussion, based on the number of threads extending from their posts. Learners in this study were also able to easily detect which topics needed more of their attention.
Johnson, L., Adams Becker, S., Estrada, V., Freeman, A. (2014). NMC Horizon Report: 2014 Higher Education Edition. Austin, Texas: The New Media Consortium.
