This is the first in a three-part series on learner analytics, cutting edge insight for the course instructor; how to assess student behaviours in an online course using the LMS data collection tools in order to provide more effective course design and instruction.
Most course instructors strive to create a class where students are engaged with the content, appear eager to learn and participate. The indicators of student engagement in a face-to-face class are straightforward enough, attendance, participation in class discussions, and/or visits to the instructor during his or her office hours.
Measuring student engagement in an online course is more complex. However with the current learning management systems [LMS] such as Moodle and Blackboard now in use in virtually all education institutions, there is a treasure trove of data on student behaviour. This data has the potential to tell a story about a student’s engagement, even predict student success within a course. Each click or ‘view’ of a web page or resource on the course homepage is recorded in the activity database along with the time spent on each. The LMS platform becomes not only a resource provider and virtual space for students, but a source of information for instructors about student behaviour and actions.
Consider the potential if course instructors could access and interpret the data collected on the actions of students in a few simple steps. The good news, this is not only possible but takes minimal time on the instructor side, yet reaps big rewards in terms of getting feedback on what is, and is not working within a course. Online instructors that can assess patterns of student behaviours and interactions with course content and learning activities, can be responsive and adjust their teaching style accordingly.
Correlation between Engagement and Student Performance
Before exploring further, identifying the purpose of measuring student engagement in terms of data analysis is necessary to frame the discussion. Several studies have determined that a strong relationship exists between students’ LMS usage and academic performance. California State University, Chico identified that the more time students spent on learning tasks within the LMS ['dwell time'] along with a high number of visits to the course home page, was associated with higher student grades (Whitmer, Fernandes, & Allen, 2012). Another study conducted by scholars at Central Queensland University which used a sample population of 92,799 undergraduate online students, reported a statistically significant correlation between the number of student views on the course home page and students final grade (Beer, Clark and Jones, 2010). The more ‘views’ or visits to the course home page, the higher the final grade.
The amount of data stored in educational institutions is gargantuan, and the new term for data collection is Big Data. According to McKinsey Global Institute, the education sector ranks as one of the economy’s top ten in terms of the amount of data stored. The question becomes, what do we do with it. At the institutional level, there are numerous opportunities for data analysis where schools can identify many patterns, gaps in student performance is just one example. Arizona State is an example of an institution that uses data analytics extensively, and has done so for several years with sophisticated analysis programs.
However in this post we are looking at the micro level, how the course instructor can affect his or her instruction using the information stored within the course to improve instruction and support students. I’ve outlined below a few practical suggestions to get started, the basics to analytics.
Practical Applications for Course Instructors
Within virtually all learning management platforms there are reporting features that course instructors can access to display student data. Below are questions instructors may have about the students within their course that the data can answer through various reports that can be generated.
- Which course resources/tools are being used most frequently? Video clips, posted documents, etc.
- How often are students logging onto the course?
- When did the student review the assignment instructions? Submit an assignment?
- Which discussions boards generate the most traffic – have more students views? This is different from the number of discussion board postings, as many students may view [and read] the posts but not contribute.
- When was the last time students logged onto the course? How many times per week are students logging on?
- What are the patterns of performance in online tests? By question?
Learning to Use the Reports
Learning how to use student data is not complicated once you know where to access the information. I’ve included a selection of brief videos below [average time of each is three minutes] all created by course instructors from various institutions that demonstrate how to access student reports in Moodle and Blackboard. In my next post I’ll delve into what student engagement can tell you about your course design, how to adapt instruction to be more effective and how to troubleshoot student problems based upon my experience with analysis of the online courses at my workplace.
Click here for part two of this series, How Course Instructors can Improve Students Engagement with Analytics.
Resources: How To Videos
- Student Tracking in Blackboard, This five-minute video will take instructors through the basics of tracking student activity, including last date of access.
- How to use Student Statistics Tracking In Blackboard, two-minute video
- How to use Moodle Reports to identify Student Actions, two-minute video
- How to use Moodle Participation and Activity Reports, three-minute video
- Using Moodle Quiz Statistics, five-minute video showing how to use Moodle quiz statistics to improve questions.
- Whitmer J., Kelley F., and W. Allen. (August, 2012). Analytics in Progress: Technology Use, Student Characteristics, and Student Achievement. EDUCAUSE Review Online.
Photo credit: Big Data, metaroll’s photostream, Flickr