About Analytics Learning
Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. A related field is educational data mining.
Methods for learning analytics include:
Descriptive Analytics: Insight into the past
Uses data aggregation and data mining to understand trends and evaluative metrics over time. The majority of statistics use falls into this category which is limited to past data and includes:
1) Student feedback gathered from student satisfaction and graduate surveys.
2) Analysis of data at all stages of the student life-cycle starting from admissions process , to student orientation, enrollments, pastoral care, study support, exams and graduations.
Diagnostic Analytics: why did it happen
This form of advanced analytics is characterised by techniques such as drill-down, data discovery, data mining and correlations to examine data or content to answer the question ‐ “Why did it happen?” and includes:
1) Analysis of data to inform and uplift key performance indicators across the organization.
2) Analysis of patterns to design appropriate metrics.
3) Equity access reporting and analysis of effective strategies to support students.
4) Learning management system metrics to improve student engagement.
5) Development of Staff Dashboards to help predict student numbers and cohort mobility through programs to assist in identifying areas for improvement.
Prescriptive Analytics: advise on possible outcomes
Goes beyond descriptive and predictive by recommending one or more choices using a combination of machine learning, algorithms, business rules and computational modelling such as:
1) Focusing on subject/courses where small changes could have a big impact on improving student engagement, feedback and outcomes.
2) Data visualization via specific tools to provide program/degree level metrics on student enrollments, program stage, results and survey feedback to give teaching staff visual snapshots of students in their programs.
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 more timely, precise, actionable feedback. In addition, educators, instructional designers and institutional leaders gain new insights once the learning process is persistent and visible.