Implementing Adaptive, Data-Driven Course Design to Improve Student Learning
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Presentation on adoption of adaptive learning systems and their use for data-driven course design practices. Delivered at NERCOMP 2017 with Frances Rowe (Quinnipiac University), Erik Moody (Marist College), and Matthew Maron (Quinnipiac University).
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Implementing Adaptive, Data-Driven Course Design to Improve Student Learning
- 1. Implementing Adaptive, Data- Driven Course Design to Improve Student Learning NERCOMP 2017
- 2. Agenda 1. Introductions name, institution, role, stage i.e. exploring, looking to pilot, undergoing an implementation, using… 2. What is adaptive/data driven courseware? 3. Steps to implementation? 4. How might adaptive courseware meet a teaching and learning need? 5. What does it take to design adaptive courseware? 6. Q/A
- 3. What is adaptive learning? LMS ADAPTIVE Learning Unit Fixed Variable Content Common Personalized Progression Class as a whole Individualized
- 4. Getting Ready Institutional Change Readiness
- 5. Steps Building awareness & a lexicon Comparing products Soften the ground
- 6. ID FacultyIT Academic Admin Circle the Wagons
- 7. Algorithm-based system White label Courseware library Direct and reliable integration LTI/SSO/API with LMS IMS analytics standards (pull data into other systems) Cloud-based Up time Accessibility standards conformance Licensing model – institutional versus student pass-through Support model – contact path, response time, user documentation Engine assesses frequently Learning record store (LRS) stop and start at any time Ability to import & author from any content e.g. OER and commercial Existing mapped competencies Ease of authoring and making modifications Content not hard wired, customization Assessment questions provided on the higher end of Bloom’s taxonomy Evidence of efficacy Uses metacognitive engagement such as hints and prompts Dashboards mapped to competencies Ease of alerts Proxy of learner time on task Types of adaptivity – pathway, content type, assessment Requirements?
- 9. Soften the Ground Pick tool Demo Solicit participation Secure vendor promises
- 10. Get It Out There Demo Sandbox
- 12. Launching a Pilot Supporting the Change
- 13. Starting a Pilot Eye to scale ID cadence Info sharing
- 14. Create, configure or a combination? Create Configure Time consuming Flexible Quick Locked in
- 15. Share Out Demo Share Data 1 2 3 4 5 1 3 5
- 16. How might adaptive courseware meet a teaching and learning need? Courseware is often regarded as technology that belongs in an online or large class setting Adaptive courseware can also provide new opportunities in a small traditional classroom Acrobatiq’s courseware employs empirically supported learning strategies: • Small & frequent “Spaced” assignments • Repeated exposure to the same material • Frequent & rapid feedback on relative performance
- 18. Automated Gradebooks & Dashboard Reports
- 20. Frequent Assessment = Early Intervention
- 21. How might adaptive courseware meet a teaching and learning need? Adaptive courseware is a powerful tool which can be used effectively in a breadth of educational settings Students like the flexibility of courseware and benefit from an effective integration of online content and classroom lectures Learning to Learn Student “Buy In” is Critical
- 27. Personalization
Editor's Notes
- Bloom’s 2-sigma dilemma One-on-one tutoring produces 2 SD improvement in outcomes How do we recreate this benefit at scale? 1. Individualize the student experience 2. Enable faculty to spend more time like a tutor
- ID – create shared definition and pitch, prep to answer questions What is adaptive? Personalized? Machine learning? IT – How does it integrate? Where does the data live? Who manages it? Faculty – What is it and how does it work? Can’t I just use the LMS? Why do I/my students need it? What does it do to my/their role? AA – How will it help with assessment and student mastery? How will it make faculty more effective? How much does it cost?
- Start with the requirements TAWC wanted: multiple forms of adaptivity, algorithm based system, flexible licensing had to be accessible, integrate with LMS, offer open access to data
- Investigate the alternatives & pick the one that meets the requirements Mention Tyton Partners’ Learning to Adapt 2.0 TAWC chose RIT, QU chose Acrobatic, others on panel??
- Pick the right participants –rock star faculty or SME helps, but the ability to move from big picture to granular/detailed thinking is most important Ask for the moon – training, custom documentation, direct ID/SME support; the space is so new that vendors are still willing to give a lot to win business
- Demo for anyone who is interested, but definitely the key stakeholders & early adopters; Sandbox a sample course for potential early adopters; talk to them to evaluate their ability to think big and small picture
- Find faculty and SMEs who can start at 10,000 feet and dive into the details quickly
- Eye to scale 1. make sure all the reqs you used to choose the tool pan out 2. if you haven’t yet, make sure it integrates – LMS, SIS 3. keep track of all of the time and bottlenecks
- ID & SME TOT is much higher in adaptive 1. curriculum, skill, concept mapping 2. more, objective assessment 3. tons of content; esp alternative content 4. more to load, connect, and publish
- Demo for anyone who is interested, but definitely the key stakeholders & early adopters; Sandbox a sample course for potential early adopters; talk to them to evaluate their ability to think big and small picture