AI in general, and adaptive learning systems in particular, will have enormous long-term effect on teaching, learner attainment and student drop-out. This was confirmed by the results from courses run at Arizona State University in Fall 2015.
• reduce in dropout rates
• maintain student motivation
• increase teacher effectiveness
A dumb rich kid is more likely to graduate from college than a smart poor one. So, these increases in attainment are therefore hugely significant, especially for students from low income backgrounds, in high enrolment courses. Many interventions in education show razor thin improvements. These are significant, not just on overall attainment rates but, just as importantly, the way this squeezes dropout rates. It’s a double dividend.
A key indicator is the immediate impact on drop-out. It can be catastrophic for the students and, as funding follows students, also the institution. Between 41-45% of those who enrol in US colleges drop out. Given the 1.3 trillion student debt problem and the fact that these students dropout, but still carry the burden of that debt, this is a catastrophic level of failure. In the UK it is 16%. As we can see increase overall attainment and you squeeze dropout and failure. Too many teachers and institutions are coasting with predictable dropout and failure rates. This can change. The fall in drop out rate for the most experienced instructor was also greater than for other instructors. In fact the fall was dramatic.
An interesting effect emerged from the data. Both attainment and lower dropout were better with the most experienced instructor. Most instructors take two years until their class grades rise to a stable level. In this trial the most experienced instructor achieved greater attainment rises (13%), as well as the greatest fall in dropout rates (18%).
Adaptive learning systems do not follow the usual linear path. This often makes the adaptive interface look different and navigation difficult. The danger is that students don;t know what to do next or feel lost. In this case ASU saw good student acceptance across the board.
Beyond these results lie something even more promising. The CogBooks system slews off detailed and useful data on every student, as well as analyses of that data. Different dashboards give unprecedented insights, in real-time, of student performance. This allows the instructor to help those in need. The promise here, is of continuous improvement, badly needed in education. We could be looking at an approach that not only improves the performance of teachers but also of the system itself, the consequence being on-going improvement in attainment, dropout and motivation in students.
Over 80% of students on this first experience of an adaptive course, said they wanted to use this approach in other modules and courses. This is heartening, as without their acceptance, it is difficult to see this approach working well.
I have described the use of AI in learning in terms of a 5-Level taxonomy. This Level 4 application (hybrid of teacher plus adaptive system) assists instructors to increase attainment and combat dropout. So far, so good. If we can replicate this overall increase in attainment across all courses and the system as a whole, the gains are enormous. The immediate promise is one of blended learning, using adaptive systems to get immediate results. The future promise is of autonomous systems, even adaptive driven MOOCs, that deliver massive amounts of high quality learning at a minute cost per learner.
One course, Biology 100, delivered as blended learning, was examined in detail. The students did the adaptive work on the CogBooks platform then brought that knowledge to class, where group work and teaching took place – a flipped classroom model. This data was presented at the Educause Learning Initiative in San Antonio in February and is impressive.
Aims
The aim of this technology enhanced teaching system was to:
• increase attainment• reduce in dropout rates
• maintain student motivation
• increase teacher effectiveness
It is not easy to juggle all three at the same time but ASU want these undergraduate courses to be a success on all three fronts, as they are seen as the foundation for sustainable progress by students as they move through a full degree course.
1. Higher attainment
2. Lower dropout
A key indicator is the immediate impact on drop-out. It can be catastrophic for the students and, as funding follows students, also the institution. Between 41-45% of those who enrol in US colleges drop out. Given the 1.3 trillion student debt problem and the fact that these students dropout, but still carry the burden of that debt, this is a catastrophic level of failure. In the UK it is 16%. As we can see increase overall attainment and you squeeze dropout and failure. Too many teachers and institutions are coasting with predictable dropout and failure rates. This can change. The fall in drop out rate for the most experienced instructor was also greater than for other instructors. In fact the fall was dramatic.
3. Experienced instructor effect
4. Usability
Adaptive learning systems do not follow the usual linear path. This often makes the adaptive interface look different and navigation difficult. The danger is that students don;t know what to do next or feel lost. In this case ASU saw good student acceptance across the board.
5. Creating content
One of the difficulties in adaptive, AI-driven systems, is the creation of ustable content. By content, I mean content, structures, assessment items and so on. CogBooks has create a suite of tools that allow instructors to create a network of content, working back from objectives. Automatic help with layout and conversion of content is also used. Once done, this creates a complex network of learning content that students vector through, each student taking a different path, depending on their on-going performance. The system is like a satnav, always trying to get students to their destination, even when they go off course.
6. Teacher dashboards
7. Automatic course improvement
Adaptive systems, such as Cogbooks, take an AI approach, where the system uses its own data to automatically readjust the course to make it better. Poor content, badly designed questions and so on, are identified by the system itself and automatically adjusted. So, as the courses get better, as they will, the student results are likely to get better.
8. Useful across the curriculum
By way of contrast, ASU is also running a US History course, very different from Biology. Similar results are being reported. The CogBooks platform is content agnostic and has been designed to run any course. Evidence has already emerged that this approach works in both STEM and humanities courses.
9. Personalisation works
Underlying this approach is the idea that all learners are different and that one-size-fits-all, largely linear courses, delivered largely by lectures, do not deliver to this need. It is precisely this dimension, the real-time adjustment of the learning to the needs of the individual that produce the reults, as well as the increase in the teacher’s ability to know and adjust their teaching to the class and individual student needs through real-time data.
10. Student’s want more
Conclusion
I have described the use of AI in learning in terms of a 5-Level taxonomy. This Level 4 application (hybrid of teacher plus adaptive system) assists instructors to increase attainment and combat dropout. So far, so good. If we can replicate this overall increase in attainment across all courses and the system as a whole, the gains are enormous. The immediate promise is one of blended learning, using adaptive systems to get immediate results. The future promise is of autonomous systems, even adaptive driven MOOCs, that deliver massive amounts of high quality learning at a minute cost per learner.
2 comments:
Hi is there any reason the first graph starts at 77%?
it seems pass rates were much better with Knewton ones for a maths course in 2011? https://www.knewton.com/assets-v2/downloads/asu-case-study.pdf
is there any more info on how they conducted their procedure? as before and after test without controls is weak evidence
ta
mura
Way it was laid out in their slides. It is aknowledged that Maths is a subject that is easier to get grade improvment on as the subject matter is so well defined. But it's all good. The fact that results are good in both is heartening. I have presented the work as it stands. There's lots more on their procedure but this is a blog post, so kept readable. It is not a full research project, with contols. It is what it is.
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