OpenColleges.edu (2013) cites numerous key advantages to using learning analytics.  First, learning analytics allow the prediction of “future student performance (based on past patterns of learning across diverse student bodies)”.  Because analytics use large samples of student performance data, the analysis of that data can reveal whether a given learning strategy is likely to be effective.  A large, diverse data sample should produce reliable predictions of student performance.

 

Learning analytics help instructors/designers “intervene when students are struggling”.  Analysis of data from an assessment such as an essay assignment may reveal the need to add additional resources such as learning labs and tutoring to help struggling students.  Learning management systems track the results of assessments in real time.  The gradebook in Moodle tracks the number of participants, individual grades, and the class average on an assessment in real time.  The instructor/designer may then identify struggling students and offer resources such as the Writing Center to help those students improve their performance.

 

Personalizing “the learning process for each and every student” is another important advantage of using learning analytics.  Information such as the average number of hours students work at a job or whether the student is a parent can be extremely valuable to an instructor/designer.  An instructor/designer may change assessment due dates to weekends to accommodate parents who may be busy with parenting activities during the week.    Simply publishing the schedule of assignments at the beginning of the course may help students budget their time effectively.

 

Long and Siemens (2011) point out another advantage of using learning analytics and this advantage is focused on the learner.  Learning analytics “can provide learners with insight into their own learning habits and can give recommendations for improvement” (Educausereview September/October 2011, p. 36).  For instance, a learner may use the Learning Management System to discover how many hours a week they spend working on a given course.  The learner can then compare the number of hours they spend on a class to the number of hours suggested by the instructor or the institution.  The use of “Learning-facing analytics” may help a student discover strategies to improve their performance in a course (p. 36). 

 

2.     How do you see yourself personally using learning analytics?

As an English instructor and instructional designer, I benefit from learning analytics already.    Student retention and completion rates are tracked by community colleges.  Retention rates tell the instructor/designer how many students registered for the course, and how many took the entire course of study.  In an online course, the analysis of retention rates allows the instructor/designer to change course elements to increase retention.  For instance, the instructor/designer may identify a course with a low retention rate, and then change that course to include more direct communication with students through email or live video discussions.  Completion rates track how many students completed the course with the grade of C or better.  The collection and analysis of completion rates indicate how effective the institution is at producing students who are eligible for transfer to a four-year college.  Instructors/designers may analyze completion rates and make changes to the design of their courses.  An instructor/designer may change the pacing or step size of lessons, revise lessons to ensure they meet the learning objectives, add course materials such as video lessons, and create more live interaction with the instructor to increase student completion rates.