Learning Analytics And Data Mining
LEARNING ANALYTICS and DATA MINING
LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. Learning analytics is both an academic field and commercial marketplace, which have taken rapid shape over the last decade. As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centred Design (e.g. usability, participatory design, sociotechnical systems thinking).
People have been researching learning and teaching, tracking student progress, analysing school or university data, designing assessments and using evidence to improve teaching and learning for a long time. Learning Analytics builds on these well-established disciplines, but seeks to exploit the new opportunities once we capture new forms of digital data from students? learning activity, and use computational analysis techniques from data science and AI.
Learning Analytics provides researchers with exciting new tools to study teaching and learning. Moreover, as data infrastructures improve - from data capture and analysis, to visualization and recommendation - we can close the feedback loop to learners, offering timelier, precise, actionable feedback. In addition, educators, instructional designers and institutional leaders gain new insights once the learning process is persistent and visible.
Analysis of data at all stages of the student lifecycle starting from admissions process, to student orientation, enrolments, pastoral care, study support, exams and graduations.
Analysis of data to inform and uplift key performance indicators across the organization
Analysis of patterns to design appropriate metrics
Equity access reporting and analysis of effective strategies to support students
Learning management system metrics to improve student engagement
Student feedback gathered from student satisfaction and graduate surveys
Combining historical data to identify patterns in the data, applies statistical models and algorithms to capture relationships between various data sets to forecast trends, and includes:
Development of Staff Dashboards to help predict student numbers and cohort mobility through programs to assist in identifying areas for improvement
Focusing on subject/courses where small changes could have a big impact on improving student engagement, feedback and outcomes
Data visualisation via specific tools to provide program/degree level metrics on student enrolments, program stage, results and survey feedback to give teaching staff visual snapshots of students in their programs
Historically, some of the most common uses of learning analytics is prediction of student academic success, and more specifically, the identification of students who are at risk of failing a course or dropping out of their studies. While it is reasonable that these two problems attracted a lot of attention, learning analytics are far more powerful. The evidence from research and practice shows that there are far more productive and potent ways of using analytics for supporting teaching and learning.
* Supporting student development of lifelong learning skills and strategies
* Provision of personalised and timely feedback to students regarding their learning
* Supporting development of important skills such as collaboration, critical thinking, communication and creativity
* Develop student awareness by supporting self reflection
* Support quality learning and teaching by providing empirical evidence on the success of pedagogical innovations