Books & Resources




Rankin, J. G. (2017). Data system-embedded analysis support’s implications for Latino students and diverse classrooms. In C. Curran & A. Petersen (Eds.), Handbook of Research on Classroom Diversity and Inclusive Education Practice, (pp. TBD-TBD). Hershey, PA: IGI Global. doi:TB

Rankin, J. G. (2017). First aid for teacher burnout: How you can find peace and success. New York, NY: Routledge/Taylor & Francis.

Rankin, J. G. (2016). Engaging & challenging gifted students: Tips for supporting extraordinary minds in your classroom. Alexandra, VA: ASCD.

Rankin, J. G. (2016). Standards for reporting data to educators: What educational leaders should know and demand. New York, NY: Routledge/Taylor & Francis.

Rankin, J. G. (2016). How to make data work: A guide for educational leaders. New York, NY: Routledge. ISBN 978-1-315-66586-3 [Details:]

Rankin, J. G. (2016). Designing data reports that work: A guide for creating data systems in schools and districts. New York, NY: Routledge. ISBN 978-1-315-66584-9 [Details:]

Rankin, J. G. (2015). Data system-embedded guidance significantly improves data analyses by making data ‘over-the-counter’ for users. In J. T. Martins & A. Molnar (Eds.), Handbook of Research on Innovations in Information Retrieval, Analysis, and Management. Hershey, PA: IGI Global. doi:10.4018/978-1-4666-8833-9  (

Rankin, J. G. (2013). Over-the-counter data’s impact on educators’ data analysis accuracy. ProQuest Dissertations and Theses, 3575082. Retrieved from




Rankin, J. G. (2017, May 9). Build data products to be “over-the-counter”. TIBCO. Retrieved from

Rankin, J. G. (2017, May 6). Overstimulation and the teaching mind. Psychology Today. Retrieved from

Rankin, J. G. (2017, April 10). Teacher burnout & a well organized class. Showbie. Retrieved from

Rankin, J. G. (2017, January 30). The teacher burnout epidemic (Part 2 of 2). Psychology Today. Retrieved from

Rankin, J. G. (2017, January 7). Response from Jenny Grant Rankin. In L. Ferlazzo (Ed.), Response: Challenging moments in teaching. Education Week. Retrieved from

Rankin, J. G. (2016, November 16). The Teacher Burnout Epidemic (Part 1 of 2). Psychology Today. Retrieved from

Rankin, J. (2016). Data systems and reports as active participants in data interpretation. Universal Journal of Educational Research, 4(11), 2493 - 2501. doi: 10.13189/ujer.2016.04110

Rankin, J. G. (2016, August 10). Gifted programs’ embarrassing secret: Changes are needed to achieve fair inclusion. Psychology Today. Retrieved from

Rankin, J. G. (2016, August). Over-the-counter data: The heroics of creating easily interpreted information. Mensa Bulletin: The Magazine of American Mensa, 597(2016), 22-26.

Rankin, J. G. (2016, August). GATE’s Embarrassing Secret. Mensa Oracle, 46(2), 6.

Rankin, J. G. (2015, December 15). Using data to avoid gender disparity in English-Language Arts classroom. In K. Macro (Ed.), GALA Journal: A Journal of the Gender in Literacy and Life Assembly, 21(2015), 43-49.

Rankin, J. G. (2015, November). Data report design makes or breaks data use: Evaluate your data system’s ability to communicate effectively. Educational Leadership, 73(3), 5.

Rankin, J. G. (2015). Data helps educators target gender issues if the data is poised to be understood. In K. Macro (Ed.), The Women in Literacy and Life Assembly (WILLA) of the National Council of Teachers of English (NCTE) Journal. National Council of Teachers of English (NCTE).

Rankin, J., Johnson, M., & Dennis, R. (2015, March 2). Research on implementing big data: Technology, people, & processes. In M. Searson & M. Ochoa (Eds.), Proceedings of Society for Information Technology & Teacher Education (SITE) International Conference 2015(1). Chesapeake, VA: AACE.

Rankin, J. G. (2015, May 4). Who is really responsible for bad data use? EdCircuit. Retrieved from

Rankin, J. G. (2014, August 24). Three ways data helps educators prepare for the first day of school. Ed-Fi Alliance Blog. Retrieved from

Rankin, J. G. (2014, July 31). Make the most of student data. International Society for Technology in Education (ISTE) EdTechHub. Retrieved from

Rankin, J. G. (2014, Summer). Universal design for educators, too. OnCUE Journal, (36)2, 22-23.

Rankin, J. G. (2014, June 28). When data systems actively support data analysis. EdSurge. Retrieved from

Rankin, J. G. (2014, May 22). Over-the-counter data: Don’t swallow anything without a label. EdTech Review. Retrieved from

Rankin, J. (2014, March 17). Reporting data with “over-the-counter” data analysis supports improves educators’ data analyses. In M. Searson & M. Ochoa (Eds.), Proceedings of Society for Information Technology & Teacher Education (SITE) International Conference 2014(1), 900-911. Chesapeake, VA: AACE. [Also available at]

Rankin, J. G. (2014, January 7). Better data use requires better data systems. eSchool News. Retrieved from

Rankin, J. G. (2013, December/Winter). Remedying educators’ data analysis errors with over-the-counter data. CCNews: Newsletter of the California Council on Teacher Education, 24(4), 14-21. San Francisco, CA: Caddo Gap Press. [Also available at]

Rankin, J. G. (2013, October 25). Pushing edtech’s responsibility to communicate feedback effectively. Edtech Women. Retrieved from

Rankin, J. G. (2013, June 3). Featured article: What data reporting systems can learn from medicine labeling. EdSurge. Retrieved from

Rankin, J. G. (2013, May 2). Over-the-counter data is the next frontier for data in edtech. Edukwest. Retrieved from


Daily Updates on Twitter


New research and other expert sources related to OTCD are shared nearly every day on Twitter at @OTCData. Keep an eye on the Presentations and Blog pages of this site for other updates. To avoid missing anything new, use the Subscribe page to automatically receive each new blog entry in your email inbox. 


OTCD Standards and Research Paper


Data systems/reports should adhere to OTCD standards, designed to increase the accuracy with which educators analyze the data being displayed.




OTCD Standards involve  5 components, each with its own OTCD standards:




Templates for creating reference sheets and reference guides, as well as "real life" samples, are provided here so you can make your own report-specific supplemental documentation:

Results from a quantitative study on OTCD components indicates increases of data analysis accuracy by up to 436% when educators use supplemental documentation while analyzing data.


Data & Questions to Support


The data types educators use, as well as the questions they seek to answer, are somewhat infinite. Data systems should provide reporting capabilities to support all key data types, as well as reports allowing educators to answer key questions related to this data. Examples of key data and questions can help:


Data Use Standards


To better understand what data knowledge and skills educators should possess, refer to SLDS Data Use Standards: Knowledge, Skills, and Professional Behaviors for Effective Data Use (via the Statewide Longitudinal Data Systems Grant Program, 2015), available at These standards serve as a comprehensive resource in many other areas of data use, as well, and are worth examining.



National standards have been available over the last 2 decades to offer guidance concerning the best way to communicate test results (standards applicable to data systems & data system reports should be balanced with an understanding of data display research):

National Council on Measurement in Education: Code of Professional Responsibilities in Educational Measurement Responsibilities of Those Who Interpret, Use, and Communicate Assessment Results (National Council on Measurement in Education, 1995):

American Educational Research Association (AERA) Standards for Educational & Psychological Testing (AERA, American Psychological Association, & National Council on Measurement in Education, 2014 update on 1999 version):

Code of Fair Testing Practices in Education Reporting & Interpreting Test Results (Joint Committee on Testing Practices, 2004):


 Data Visualization Experts Not Exclusive to Education


Experts who study and write about data visualization as it applies to all fields (e.g., graphing data outside of Education, which includes many aspects that can be applied to - and/or modified for - OTCD) include: