July 15-17, 2013

1. Emerging Technologies to Improve Middle School Literacy: The Role of Data Analytics within a New Universal Literacy Network July 15-17, 2013

2. The Universal Literacy Network

3. The Goal/Challenge The Center funded under this priority will explore how emerging technologies can be used to: (1) accelerate remediation in basic reading skills in conjunction with content-area instruction; (2) enhance student motivation, engagement, and self-efficacy related to literacy learning; and (3) improve efficiency in the use of educational resources (e.g., through the use of open educational resources, increasing academic learning time).

4. Our Partners

5. Our Approach/Vision • The most transformative emerging technology is the web itself. • The most powerful lever for change is individual engagement and motivation • Optimizing individual choice in what to read • Optimizing individual choice in how to learn (support and challenge that is “tuned” to individual ) • Optimizing alternatives for why to read (authentic, timely, social, interest-driven practice) • Optimizing when and where to read (everywhere, anytime, any device) • The most effective pedagogy optimizes opportunities for extended apprenticeships • Optimizing opportunities for modeling and demonstration • Optimizing opportunities for voluminous practice with individual supports and gradual release • Optimizing opportunities for continuous feedback and progress monitoring (first to learner, but also to teacher, to system, etc.).

6. Universal Literacy Network Overall Deliverables

7. Universal Literacy Network Hub Detail

8. Progress

9. Udio Workshop

10. Wild Weather Down Under

11. Scaling Seven Summits

12. Student review

13. Student Products

14. Big Data and Learning Analytics • Data mining and data analytics are being rapidly adopted within industry and education to improve decision making. • The US Dept of Education National Technology Plan visions the potential use of data in online learning to improve instruction • Learning analytics, at least in concept, frames the challenge as: “If we can discern the pattern in the data and make sense of what is happening, we can predict what should come next and take the appropriate action.” • Making learning visible to students provides the potential for students to develop skills in monitoring their own learning and to explicitly see how their effort relates to success. Image sourced – from “The Matrix”

15. A Narrow View: Analytics & Disability • Disability has not been framed within this work at all. • There is the danger that learning analytics wont be about learning at all but rather significantly narrow the potential of what online learning could be based only on what we can reliably measure though “clicks” and online assessments. • Learning analytics could do to online learning what large scale assessment has done to learning and teaching.

16. Putting the Learning back in Learning Analytics Learning is more than performance and tracking of movement from one lesson to another. Big question: How can learning analytics collect and display information that more richly informs the learning and teaching process?

17. Feedback about Strategy, Affect, and Performance

18. Questions • Learning analytics (as opposed to “data” analytics) are still very much at an emerging phase of development, and models of how learning analytics can be leveraged are currently quite thin. What are the best models and implementations of learning analytics that, in your experience, can or should inform our future work? • What aspects of students’ behavior online are most useful to track and understand students’ progress in reading? • What potential pitfalls to implementing this approach at a wide scale should we be concerned about? • When thinking about data as feedback, what is the role of automated vs human-provided feedback?