Bolton College’s adaptive learning and assessment platform - Aftab Hussain
•Download as PPTX, PDF•
1 like•1,356 views
This document appears to be a presentation on an adaptive learning and assessment platform created by Bolton College. It discusses differentiating instruction for students in the classroom, personalizing and contextualizing content on virtual learning environments, using student profile data and machine learning to distribute tailored content and assessments, and the implementation of an adaptive employability module to test these concepts. It also questions how the adaptive environment could scale up across the college and whether initial uses of the platform were effective.
Recommended
More Related Content
Viewers also liked
Viewers also liked (20)
More from Jisc
More from Jisc (20)
Recently uploaded
Recently uploaded (20)
Bolton College’s adaptive learning and assessment platform - Aftab Hussain
- 1. 1 of 20 Bolton College’s Adaptive Learning and Assessment Platform
- 2. 2 of 20 Differentiation in the classroom
- 3. 3 of 20 Personalisation and contextualisation is not the norm on our VLEs
- 4. 4 of 20 Personalisation and contextualisation is not the norm on our VLEs
- 5. 5 of 20
- 7. 7 of 20 The Personal Learning Environment
- 8. 8 of 20 The Student Profile
- 10. 10 of 20 Machine Learning
- 11. 11 of 20 Distributing content and assessment
- 12. 12 of 20 Distributing content and assessment
- 13. 13 of 20 Distributing content and assessment
- 14. 14 of 20 Our Level 2 Employability Module
- 15. 15 of 20 Our Level 2 Employability Module
- 16. 16 of 20 What’s different?
- 17. 17 of 20 How does the adaptive environment scale up? – the easy stuff
- 18. 18 of 20 How does the adaptive environment scale up? – the hard stuff
- 19. 19 of 20 Does it work?
- 20. 20 of 20 What’s next….?
Editor's Notes
- Good afternoon. My presentation is about Bolton College’s Adaptive Learning and Assessment environment. Our platform enables us to adapt and present content and assessment materials so that they are unique to each student via their PLE.
- Differentiation in the classroom In the classroom teachers routinely use differentiation to present materials that best suit the needs of each student. Differentiation in the class is relatively simple – because teachers can adapt information and questions instantaneously. Differentiation in the classroom is the norm.
- Personalisation and contextualisation is not the norm on our VLEs (slide 1) Differentiation online is hard – because institutions haven’t had the tools to adapt content and assessment. In a typical VLE teachers distribute the same material to all students on a course regardless of ability.
- Personalisation and contextualisation is not the norm on our VLEs (slide 2) A student’s profile on an institution’s VLE is not well defined – teachers are comfortable in drawing up a profile for face-to-face delivery but lack the data or means to shape a student’s profile for the online component of their courses. At Bolton College this is no longer the case with our adaptive environment.
- Big Data The use of big data, analytics and business intelligence tools is changing the shape of student facing online services. The use of the College dataset has improved existing services and in many cases it has allowed us to create new applications or services such as our adaptive environment.
- Datasets Datasets from around the College inform our adaptive environment. Material is presented to students based on each student’s unique profile. We use information such as entry qualifications, course titles and assessment scores. The dataset also includes age, gender, learning styles, career goals and more.
- The Personal Learning Environment Our adaptive environment is part of the College’s wider PLE. We are developing new instrumentation, tools, and methodologies that bring learning closer to each student based on his or her profile, personal choices and preferences.
- The Student Profile Material presented to each student is shaped by each student’s unique personal profile. The personal profile is shaped by data that accompanies the student as he or she joins the course, academic progress made by the student during the course, the College’s learner journey management system and by information that is volunteered by the student.
- Agency In a traditional classroom setting there are two primary agents in the classroom – the teacher and the students. When working with an adaptive environment a new agent is introduced to the classroom; namely the PLE – because the PLE determines the content and assessment to be presented to each student.
- Machine Learning Techniques Our adaptive environment is a means by which big data, machine learning approaches and traditional educational theory converge. We have utilised machine-learning techniques to customise the student’s learning pathways.
- Distributing content and assessment (slide 1) Our Adaptive Environment delivers and adapts material based on the following variables: entrance qualifications, the level of the course, initial diagnostic test results, assessment scores during the course, assignment grades and individual interests and preferences that have been volunteered by the student.
- Distributing content and assessment (slide 2) Tutorials which lead to improved scores in subsequent assessment activities are ranked higher than others. Poor content is identified and goes through re-iteration by subject teachers before being put back into the pool of resources in the adaptive environment.
- Distributing content and assessment (slide 3) We have developed supervised (teacher in the loop) and unsupervised machine-learning techniques. Teachers author content and make revisions to their course content. Teachers define the algorithms to be used for their courses. Unsupervised machine learning adapts the material that is presented to each student.
- Bolton College’s Level 2 Employability Module (slide 1): Delivered to over 1,000 level 2 students across the College. Content and assessment adapt to suit the needs of each student. Content and assessment is delivered with little or no intervention on the part of teachers.
- Bolton College’s Level 2 Employability Module (slide 2): The wider College dataset automatically informs course content and assessment; and The course scales well – a small online course team can support thousands of students. The marginal cost of enrolling additional students to the course is negligible.
- What’s different - Things to bear in mind: The teacher to some degree does not know what content or online tests are being presented to his or her students Automated delivery of content is not new – we have successfully used the service to distribute material to students on the PLE for more than 2 years.
- How does the adaptive environment scale up? – the easy stuff The adaptive environment is easy to scale. Content and assessment adapts just as well with 10 students or 10,000 students. A larger student body produces a better adaptive environment.
- How does the adaptive environment scale up? – the hard stuff Adaptive environments require many more layers of content and assessment activities than traditional VLEs. At Bolton College the production of content and online tests is the single biggest barrier to making the service available to all students across all courses.
- Does it work? Yes – we get great feedback from teachers and students. When the adaptive learning and assessment environment is coupled with great teaching and student support we have seen a rise in attainment.
- What’s next…? Big Data has entered into the VLE space and changed VLEs forever so that they become true PLEs. Unsupervised machine learning will become the norm. Thank you for your time. That concludes my presentation.