“This concern is particularly pronounced in relation to knowledge, teaching, and learning. Learning institutions and corporations make little use of the data learners ‘throw off’ in the process of accessing learning materials, interacting with educators and peers, and creating new content. In an age where educational institutions are under growing pressure to reduce costs and increase efficiency, analytics promises to be an important lens through which to view and plan for change at course and institutions levels.” (1st International Conference on Learning Analytics and Knowledge 2011)More specifically, if learning analytics were to adopted by an entire organization, they might well be used to assess curricula, departmental programs, and the organization’s learning performance as a whole.
So there appears to be a good case for an organizational commitment to learning analytics but what about the instructors and students? Why do they need learning analytics?
“Have you ever had the sense at the start of a new course or even weeks into the semester that you could predict which students will drop the course or which students will succeed? Of course, the danger of this realization is that it may create a self-fulfilling prophecy or possibly be considered ‘profiling’. But it could also be that you have valuable data in your head, collected from semesters of experience, that can help you predict who will succeed and who will not based on certain variables. In short, you likely have hunches based on an accumulation of experience. The question is, what are those variables? What are those data? And how well will they help you predict student performance and retention? More importantly, how will those data help you to help your students succeed in your course? Such is the promise of learning analytics.” (Dietz-Uhler & Hurn, 2013)
If learning analytics can indeed help to better predict student performance and retention then, depending on the circumstances, various remediation methods might be applied at an earlier stage than before, which should increase the success rates.What might some of the benefits really look like for an instructor?
- Improved course effectiveness. For example, if the analytics reveal that many students spend a large amount of time on one topic and yet score poorly on the topic assessment then chances are that either the learning activities, the content or the assessment needs improvement.
- Early warning of at risk students. Low to no activity online may indicate an at risk student. This is easy to identify in small, in-person classes but as the student number grows, it becomes progressively harder to detect a lack of engagement or, more subtly, a trend toward lower engagement which might respond to an early intervention. Analytics really start to pay off as the number of students increase.
- Student progress. Accurate collection of learning activity data such as time-on-task and assessment scores enables useful progress estimates for both the student and the instructor.
- Content to outcome relationships. The more a course is run, the better the ability to identify how performance on certain parts of the course might positively or negatively affect final outcomes. This may indicate the need to revise the content or possibly alter the order or nature of the learning activities etc.
- Student performance. Again, the more a course is run, the better the ability to predict student performance at an early enough stage to potentially find ways to increase the success rate.
Increasingly, analytics are affecting our lives in myriad ways. Web analytics had a transformative effect on marketing. Big data analytics is not just being touted as transforming businesses but revolutionizing how we live, work and think! Can learning analytics make a difference in education or, more specifically, help to improve learning outcomes? The history and impact of data analytics certainly make for a compelling argument.
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This post was written by ihart