Tailoring Teaching with Data

Personalized services have been one of the big promises of the Internet. Improving our schools and learning outcomes for our students have been a national focus over the past several decades. But, we have made very little progress in applying personalization to learning.

For example, in most assessments, a teacher is only able to see an overall performance score, and at best, a breakdown of which questions were answered correctly. However, there are other factors–engagement, behavioral issues, and learning styles–that affect a student’s performance, and should be considered when trying to remediate instruction. In our activity, we will view all of the different signals that can be tracked in a blended learning lesson, and identify the most important factors to highlight. Does a teacher need to know a student’s score relative to the rest of the class? Or the number of behavioral issues a student has received in the past week?

Together, we will explore what information a teacher needs to know from student data. We will introduce Gooru (www.goorulearning.org), a search engine for learning, that allows teachers to personalize instruction for their students. We will look at the different aspects of how data is logged, processed and reported to support the diverse needs of teachers. Finally, we will investigate the three elements of personalization: developing a user profile, maintaining a dynamic learning catalog, and developing tools for teachers to personalize learning for their students.

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