People’s expectations of learning are often unrealistic and surrounded by hype. Enter the adaptive learning platform, one of the improvements in analytics showcased in OEB 2013’s learning analytics stream. Used wisely, this can give a better picture of student performance, and allow teachers to divine how to improve it.
by Pauline Bugler
The platform transports students to their learning goals at their own pace. It is driven by the engine of learning analytics, which students fuel themselves in interacting with the learning platform – leaving behind what Sara Al-Mazen of the National Centre of E-Learning and Distance Learning, Saudi Arabia, call “digital breadcrumbs”. Some students may need extra help while others fast-pace to the finish line: the system provides data, with a level of detail that was impossible before the advent of e-learning technology, which is analysed by teachers, who intervene if necessary.
Thus, the adaptive learning platform gives teachers time to coach students individually and guide them along more personalised learning routes.
Learning analytics is a relatively new branch of technology-enabled learning, but it is already proving that it could play an important part in making the teacher-pupil relationship more effective. But that’s not all: when part of a learning platform, it can provide an essential link between engagement and assessment.
Perry Samson, a professor of atmospheric science at the University of Michigan at Ann Arbor, was in seventh heaven amid the wild, stormy weather in Berlin during the OEB where he spoke about “Adopting Adaptive Learning”. Back home, he teaches meteorology, and has created web-based software called Lecture Tools that combines personal-response technology with other kinds of interactive tools. Students can use it on their laptops and he can teach in classrooms anywhere. Lecture Tools allows him to add a bookmark and browse slides. Crucially, it uncovers misconceptions in class.
Students can answer questions by pinpointing a location on a weather map on their screens, and the answers all show up – anonymously – for everyone to see. Students can also pose questions anonymously without seeing the names of other people asking questions. As a result, Samson began to discover that he was creating fascinating data about his interactions in class – for example, evidence that women are more inclined to ask questions than men. All data is being stored on a textmining database. But that does not mean that they actually get away with it.
Using the platform, buzzwords can be looked up and materials accessed from all sorts of open sources such as the Khan Academy and e-textbooks – providing an engaging way for students to interact with learning materials. In fact, the online connections it enables are so good that around 90 % of students said they could have achieved a good grade without even opening a book!
But that’s not to say that the platform lets students get away with sloppy practices. Samson cited the case of a student who came to him after class and moaned that he was struggling. The professor consulted his software and said: “Well, I am getting a different story. The interface tells me that you only looked at materials twice. You didn’t ask any questions in class and you didn’t come regularly.”
So obviously, it was time for the student to knuckle down, as diagnosed by the learning system. This is Samson’s core interest: what happens in the classroom, what students know and what they should know. Thus interactive participation and feedback is crucial to finding the answers to learning problems.
But it’s not just a matter of helping individual teachers to assess their students: according to Sara Al-Mazen, we can now use Open Educational Linked Data to improve entire education systems, all by using the same means – the tiny traces of data students leave behind.
In the old days, teachers often claimed they had “eyes in the back of their head” – so they could watch the class while they’re writing on the blackboard. Now, however, they have something much better: a second insight provided by learning analytics into their individual students’ levels of engagement, achievement and progress.