We at the EdTech Center are closely tracking the ability of technology to provide personalized learning experiences that accelerate learning and increase student motivation and success. The future of personalized learning will depend on the ability to gather data that shows what part of the instruction is working well and what needs to be further supported – and then get that data into the hands of teachers and learners alike. We thank Steve Quann for penning this important piece before parting from the EdTech Center and look forward to seeing how he continues to incorporate personalized learning into future online course development. — The EdTech Center Staff
By Steve Quann
After putting the finishing touches on the creative lesson you just planned, you’re excited to see if your students will be engaged. You have been using a blended learning approach and are beginning to integrate more personalized learning into your classes. During class, you’re relieved that students watched the assigned videos at home, which you can see by their enthusiasm for all the entertaining ways the topics were covered. This enthusiasm made your in-class work very productive. The videos provided the background they needed to apply what they learned with classroom activities. You are pleased when students leave the class saying how they are looking forward to watching the next video at home. Although some ask for more help than others, you feel like these new approaches are working. You look forward to seeing the first draft of the project or paper or the results of the test to really know about students’ level of mastery.
But shouldn’t we be jumping in earlier? Many of us use quizzes or formative assessments for just that reason. (See how to create and grade quizzes using Google Forms.) Using learning analytics gathered from Google Forms, courses, and other platforms can provide a breadth of information to better understand what students need and when they need it, allowing us to improve the personalized learning as we go.
What are learning analytics?
Learning analytics can be collected from e-learning experiences whether from online courses, apps, or other platforms. Examples of what teachers might access could be: how quickly learners are moving through a section, how many times they have logged in, and of course how well they scored on tests, etc. Not all products or platforms gather the data, and often it is too daunting and time consuming for busy teachers to add one more task to the list of things to do. However, it can definitely be worth the effort. Instructors can learn more than just who needs help (they often know that already), more importantly, they can see where students need help. Learning analytics can guide you in ways to better your lesson planning and improve the personalization of learning. You can more accurately predict which learning materials are appropriate or relevant for your learners based on a student’s overall results, skills, and interests.
Why use learning analytics to inform teaching and instructional design?
Learning analytics can:
- Monitor student progress during a course so that teachers can take action quickly.
Why wait until the end? If you know a learner or a group is not keeping on track, why not nip it in the bud early? Instructors of distance and blended learning are often referred to as “guides on the side,” but I like to think of ourselves as coaches. Perhaps there is a learner who is not doing well on tests or completing writing assignments but who viewed the material. This could present as a lack of effort, if not for the data indicating he or she spent a lot of time on task. Conversely, perhaps a student spending very little time on task and scoring well is not being challenged and can advance at a greater pace. This is the beauty of a personalized approach. - Help to predict class performance.
An advantage to digging into analytics is the ability to gather data that would give insight into how learners will perform throughout the course as content gets more challenging. For example, you might be able to prepare your class for the vagaries of the conditional tense and provide additional scaffolding as needed with more support and supplementary material. - Support personalized learning experience.
Another related benefit is that not only can data provide guidance for a whole class, but it allows for custom tailoring for individual learners. Granted, it is time consuming, but if the data shows that a learner is having difficulty finishing a particular section, then you can offer the learner additional in-class practice or teachers can provide links to a video or create a quick screencast to explain a concept. - Boost learners’ persistence and increase retention rates.
As we know, adult students have busy lives and face numerous challenges that can make it difficult to continue their studies. Well-designed e-learning with content that is relevant and engaging can address some of the challenges of persistence. When students have the opportunity to take surveys and assessments throughout a course and not only at the end, teachers (and designers) will be able to see analytics that can allow teachers to offer support and adapt instruction. In essence, fewer learners are likely to go without their needs addressed, lose confidence, interest, or motivation. Any one of the above can impact retention, derailing students from reaching their educational goals. - Lead to improved curriculum and course.
As you review the class evaluations and reflect on needed changes to the overall curriculum and course content, make sure to revisit the learning analytics on a micro (student) and macro (class) level and look for any patterns. Were there questions few learners answered correctly? Were they just too tricky or was more explanation needed? Is there data to indicate a topic area was too difficult?
When teaching a class, it is of course near impossible to “get into the heads” of learners. With blended learning, we can occasionally meet one-on-one, but we can never watch everything they do online. (There are, however new advances using xAPI allowing for broader tracking of learning interactions.) The avid use of learning analytics can help us learn more about individual students and the class as a whole, thereby revising our instruction to enhance personalized learning.
Adapted from 5 Reasons Why Learning Analytics Are Important For eLearning.
Steve Quann was a proud staff member at World Education for many years. He was the past Director of the EdTech Center and now consults as an instructional designer on e-learning and mobile learning projects.
