based on your reading from this week and your personal/professional experiences, discuss the following:
- Suggest two methods you might use to gather and analyze data about students in a classroom
- Discuss how learner analytics is meaningful to diverse students in the inclusive K-12 classroom
- Readings
1. Baker, R. (2016). Using learning analytics in personalized learning. In M. Murphy, S. Redding, & J. Twyman (Eds.), Handbook on personalized learning for states, districts, and schools (pp. 165–174). Temple University, Center on Innovations in Learning. https://learninganalytics.upenn.edu/ryanbaker/ED568173.pdf
- This chapter discusses learning analytics applied in the context of how it affects and can guide personalized learning. Some examples of success are shared, as well as occasions failures could occur. Action principles for education agencies of all levels is introduced and explained.
2. Lawrence, K.S. (2016). Identifying data-driven instructional systems. Research to Practice Brief. SWIFT Center. https://files.eric.ed.gov/fulltext/ED571845.pdf
- The resource is a brief overview of the study “Creating data-driven instructional systems in school: The new instructional leadership,” by Halverson, Grigg, Pritchett, and Thomas in 2015. The study reviewed investigated student outcome improvements linked to analyzing data in K-12 classrooms.
3. Mims, W.M., & Lockley, J. (2017, January). Action research and differentiating reading instruction in Mississippi: Fourth-grade students’ reading success. https://files.eric.ed.gov/fulltext/ED571755.pdf
- A fourth-grade teacher applied research to analyze learner data in her fourth-grade classroom to make data-driven decisions about reading instruction and interventions to apply to improve reading skills for students. Inclusive instructional methods were applied using learner data analytics with inclusive reading instruction.
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4. Patrick, S., Worthen, M., Frost, D., & Gentz, S. (2016). Promising state policies for personalized learning. International Association for K-12 Online Learning. https://files.eric.ed.gov/fulltext/ED567893.pdf
- This resource is a publication by the International Association for K–12 Online Learning (iNACOL). This report is written for state educational policymakers regarding policies that support personalized learning. Reading should be contained through pages 5-9 and 42.
5. Possi, M.K., & Milinga, J.R. (2017). Learner diversity in inclusive classrooms: The interplay of language of instruction, gender and disability. Malaysian Online Journal of Educational Sciences, 5(3), 28-45. https://files.eric.ed.gov/fulltext/EJ1150435.pdf
- This research resource reviewed the impact of data analytics in relation to student diversity in inclusive classrooms that focused on “the language of instruction, gender and disability issues, and their implications for educational practices.”
6. Rijumol, K.C., Thangarajathi, S., & Ananthasayanam, R. (2011). Factor analytic study of cognitive processing and self-perception of learning disabilities among the elementary inclusive school children. Journal of Educational Psychology, 5(1), 32-38. https://files.eric.ed.gov/fulltext/EJ1102286.pdf
- This resource explored cognitive processing and self-perception of learning disabilities in elementary inclusive school children was reviewed.
7. Shum, S. B., & Crick, R.D. (2016). Learning analytics for 21st-century competencies. Journal of Learning Analytics, 3(2), 6-21. https://files.eric.ed.gov/fulltext/EJ1126768.pdf
- This special section brings together a diverse range of learning analytics tools and techniques that can be deployed in the service of building 21st-century competencies. We introduce the research and development challenges and introduce the research and practitioner papers accepted to this section, before concluding with some brief reflections on the collection and relevance of a complex systems perspective for framing this topic.