Privacy and Student Agency in BL/PL Classrooms

1. Privacy and Student Agency in BL/PL Classrooms Heather Greenhalgh-Spencer, PhD. Heather.greenhalgh-spencer@ttu.edu #iNACOL17

2. Using Student Data Data-Driven Instruction Efficacy of Outcomes Student Privacy Student Agency Student Ownership #iNACOL17

3. TTU Program in Blended and Personalized Learning https://www.depts.ttu.edu/education/graduate/blpl/ #iNACOL17

4. Case Studies of Privacy, Agency, and Using Student Data • FERPA and Data Sharing • Data Walls and Data Comparison • Predictive Software and Data Inference #iNACOL17

5. FERPA and Data Sharing: Case Study You are the head of the data analytics team for your school district. You have partnered with a local university which is helping you analyze your data. This university wants to help you correlate student performance on tests with demographic data (gender, race, nationality, first-language, Free and Reduced Lunch) and family data (homelessness, divorce, recent move, military). The university plans to help you analyze the data, but then will also publish their analysis as a case study in an academic journal. Can you provide this information to the university? How can you work with your partner and also protect your students’ privacy? #iNACOL17

6. FERPA and Data Sharing: WYNtK • FERPA = Family Educational Rights and Privacy Act • Parents/Students have the right to: • Inspect records • Request that records are changed/corrected • Protect the release of information #iNACOL17

7. FERPA and Data Sharing: WYNtK Schools are required to get “Consent” of parent/student to disclose non “directory” information, “except in cases of: • School officials with legitimate educational interest; • Other schools to which a student is transferring; • Specified officials for audit or evaluation purposes; • Appropriate parties in connection with financial aid to a student; • Organizations conducting certain studies for or on behalf of the school; • Accrediting organizations; • To comply with a judicial order or lawfully issued subpoena; • Appropriate officials in cases of health and safety emergencies; and • State and local authorities, within a juvenile justice system, pursuant to specific State law.” #iNACOL17

8. FERPA and Data Sharing: WYNtK “Schools may disclose, without consent, ‘directory’ information such as a student's name, address, telephone number, date and place of birth, honors and awards, and dates of attendance. However, schools must tell parents and eligible students about directory information and allow parents and eligible students a reasonable amount of time to request that the school not disclose directory information about them. Schools must notify parents and eligible students annually of their rights under FERPA. The actual means of notification (special letter, inclusion in a PTA bulletin, student handbook, or newspaper article) is left to the discretion of each school.” #iNACOL17

9. FERPA and Data Sharing: Case Study You are the head of the data analytics team for your school district. You have partnered with a local university which is helping you analyze your data. This university wants to help you correlate student performance on tests with demographic data (gender, race, nationality, first-language, Free and Reduced Lunch) and family data (homelessness, divorce, recent move, military). The university plans to help you analyze the data, but then will also publish their analysis as a case study in an academic journal. Can you provide this information to the university? How can you work with your partner and also protect your students’ privacy? #iNACOL17

10. FERPA and Data Sharing: Case Study What are your solutions? #iNACOL17

11. FERPA and Data Sharing: Case Study Possible Solutions • IRB / RRB • De-Identified Data • Non-Published Research • Consent/Assent • Public Records Data as Separate Path #iNACOL17

12. Data Walls and Data Comparison: Case Study As a 4th grade ELAR teacher, you have a ‘data wall’ where you have posted the scores of everyone in your class on the last 4 minor assessments as well as the last 2 major assessments. The students’ names are not on the board, just their scores and an identification number that stands in for the students’ names. While the names are not on the board, the students tend to know who is who. One student approaches you after class—crying—and asks you to not put his information on the data wall. He is tired of always being seen by his classmates as ‘stupid’. He feels that you are making fun of him by posting his scores on the ‘data wall’. What should you do? #iNACOL17

13. Data Walls #iNACOL17

14. Data Walls and Data Comparison: WYNtK Data walls can help students by: • Allowing students to track their goals and progress over time • Creating a visual representation of the progress of the class • Creating positive peer pressure • Allowing for differentiated instruction • Reminding teachers about targets for instruction • Creating a visual comparison of how students compare to the class norm #iNACOL17

15. Data Walls and Data Comparison: WYNtK Data walls can harm students because: • Lack of privacy = lack of student agency and real student choice, voice, ownership • Humiliation = lack of engagement • Some students are motivated by competition; other students are paralyzed by it • Lack of self-efficacy = poor learning outcomes • Intrinsic motivation vs. extrinsic motivation #iNACOL17

16. Data Walls and Data Comparison: Case Study As a 4th grade ELAR teacher, you have a ‘data wall’ where you have posted the scores of everyone in your class on the last 4 minor assessments as well as the last 2 major assessments. The students’ names are not on the board, just their scores and an identification number that stands in for the students’ names. While the names are not on the board, the students tend to know who is who. One student approaches you after class—crying—and asks you to not put his information on the data wall. He is tired of always being seen by his classmates as ‘stupid’. He feels that you are making fun of him by posting his scores on the ‘data wall’. What should you do? #iNACOL17

17. Data Walls and Data Comparison: Case Study What are your solutions? #iNACOL17

18. Data Walls and Data Comparison: Possible Solutions • Don’t have a data wall • Do have a data dashboard that is only for the teacher • Do share individual results with each individual student • Track class progress as a whole rather than as an aggregate of the scores of each individual • Create a culture where humiliation and coercion doesn’t happen* • *Not sure if this is possible, but I’ve heard that it is #iNACOL17

19. Predictive Software and Data Inference: Case Study Your school uses an assessment software package for all of their math classes. This software evaluates students on their content mastery by asking questions, and then tracking right/wrong answers, as well as time-taken to come to an answer. Students are scored both on their answers, and the time taken to submit an answer. It is possible to get a low score on these assessments—even if you have all of the correct answers—if you took a long time to answer each question. Today, you are supposed to have your students take a math test using this software package. However, you know that your student—Emma—has been having a hard time today because her father just got deployed. How do you make sense of the data generated by Emma’s math test? #iNACOL17

20. Predictive Software and Data Inference: WYNtK • Predictive analytics can help teachers off-load tasks • Predictive analytics can aggregate and analyze data, in real-time, and provide suggestions for change • Predictive analytics are created through algorithms that may/may not be ‘trained’ using ‘truth data’ • Predictive analytics are created through the coding of assumptions • Teachers and students should understand the models and assumptions that undergird the predictive analytics #iNACOL17

21. Predictive Software and Data Inference: Case Study Your school uses an assessment software package for all of their math classes. This software evaluates students on their content mastery by asking questions, and then tracking right/wrong answers, as well as time-taken to come to an answer. Students are scored both on their answers, and the time taken to submit an answer. It is possible to get a low score on these assessments—even if you have all of the correct answers—if you took a long time to answer each question. Today, you are supposed to have your students take a math test using this software package. However, you know that your student—Emma—has been having a hard time today because her father just got deployed. How do you make sense of the data generated by Emma’s math test? #iNACOL17

22. Predictive Software and Data Inference: Case Study What are your solutions? #iNACOL17

23. Predictive Software and Data Inference: Possible Solutions • Data literacy for teachers and students • Knowledge of the assumptions behind the analytic model • Fuller picture of what counts as ‘data’ • Data analytics in context • Emma’s score on this one test as one data point among many #iNACOL17

24. Any Questions? Heather Greenhalgh-Spencer, PhD. Heather.greenhalgh-spencer@ttu.edu #iNACOL17

25. Image References • http://declineofscarcity.com/?p=3341 • http://www.personalizelearning.com/2016/01/continuum-of-voice-what-it-means-for.html • http://www.thebluediamondgallery.com/wooden-tile/a/agenda.html • https://tctechcrunch2011.files.wordpress.com/2012/03/johnny_automatic_scales_of_justice.png • https://www.workinsports.com/blog/how-to-ask-questions-that-will-always-inspire-thoughtful-answers/ • http://qualitycommunityschools.weebly.com/data-walls.html • https://www.pinterest.com/explore/classroom-data-wall/ #iNACOL17

26. Citation References • de Freitas, S., Gibson, D., Alvarez, V., Irving, L., Star, K., Charleer, S., & Verbert, K. (2017, April). How to use Gamified Dashboards and Learning Analytics for Providing Immediate Student Feedback and Performance Tracking in Higher Education. In Proceedings of the 26th International Conference on World Wide Web Companion (pp. 429-434). International World Wide Web Conferences Steering Committee. • Leme, M. I. D. S., Dell'Agli, B. A. V., & Caetano, L. M. (2016, October). Views of parents and teachers on the moral development of children and adolescents. In Association for Moral Education Conference Proceedings (Vol. 41, No. 1). • Westphal, K. R. (2017). How Kant Justifies Freedom of Agency. • Prinsloo, P., & Slade, S. (2016). Student vulnerability and agency in networked, digital learning. European Journal of Open, Distance and E-learning, 19(2). • Zirkel, S., & Pollack, T. M. (2016). “Just Let the Worst Students Go” A Critical Case Analysis of Public Discourse About Race, Merit, and Worth. American Educational Research Journal, 53(6), 1522-1555. • Vallerand, Robert J. "Toward a hierarchical model of intrinsic and extrinsic motivation." Advances in experimental social psychology 29 (1997): 271-360. • Aro, M., & Lyytinen, H. (2016). Training Reading Skills in Finnish: From Reading Acquisition to Fluency and Comprehension. In Reading Fluency (pp. 125-140). Springer International Publishing. • Liu, T. Y. (2016). Using educational games and simulation software in a computer science course: learning achievements and student flow experiences. Interactive Learning Environments, 24(4), 724-744. • Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & education, 104, 18-33. • Beecham, S., Bowes, D., & Stol, K. J. (2017). Introduction to the EASE 2016 Special Section: Evidence-Based Software Engineering: Past, Present, and Future. • Williamson, B. (2016). Digital education governance: data visualization, predictive analytics, and ‘real-time’policy instruments. Journal of Education Policy, 31(2), 123-141. • O'Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books. #iNACOL17