DATA LITERACY IN PHYSICAL EDUCATIONGLORIA NAPPER-OWEN

COLLEGE OF EDUCATION, UNIVERSITY OF NEW MEXICO

Purpose

• Session objectives:• Attendees will analyze the difference in data literacy and assessment literacy

• Attendees will evaluate the importance of data literacy to make informed decisions on curriculum and instruction.

• Session outcomes:• Attendees will identify data that is available through school records or district instructional

services that will inform their instructional plan.

• Attendees will formulate an action plan for gathering necessary data beyond assessment data that will inform their instructional strategies.

What Is Data Literacy?

• Data literacy is

• transforming information into actionable instructional knowledge and practices

• collecting, analyzing, and interpreting all types of data (assessment, school climate, behavioral, snapshot, longitudinal, moment-to-moment, etc.) to help determine instructional steps. (Mandinach & Gummer, 2016)

Are Data Literacy And Assessment Literacy The Same?

Adapted from Mandinach, 2016

Educators must use diverse sources of data to inform their practice. e.g. performance tasks, portfolios, affective data, health data, behavior data, attendance data, IEP data, family data…… and on.

Why The Attention To Data Literacy?

• Essential for educators to be data literate• School accountability• Teacher accountability for student achievement• Instructional practices & teacher reflectivity

• “can assist teachers in moving from an intuitive, disorganized, undocumented, “in the head” process of assessing their students to a systematic, consistent way of monitoring student progress.” (Reeves & Honig, 2015)

• The focus on evidence-based practice requires that educators use data effectively and responsibly

• Secretary of Education Arne Duncan (2009): “Data gives us the roadmap to reform. It tells us where we are, where we need to go, and who is most at risk.”

What Does The National Board Say?

• Standard 1 Knowledge of Students: “attain knowledge of their students’ unique qualities and characteristics”

• Data on ethnicity, religious background, body image, sexual orientation, family life, SES, ability level, primary language, health and wellness issues

• Standard IV Wellness within Physical Education: “motivate students by using technology to record and analyze their daily activity and revise their personal fitness plans based on the data gathered.”

• Standard VII Teaching Practices: “use the results of preassessment to design lessons for the class that challenge each student”

• Standard VIII Assessment: “reflect on all aspects of the assessments they use within their classrooms, from the careful selection and responsible administration of valid, reliable assessments to the benefits of assessment data for students, families, and physical education programs.”

• Standard IX Reflective Practice: “if the majority of students in a class do not reach their target heart-rate zone while playing soccer, an accomplished teacher may modify the game or rules to increase active participation.”

• (Physical Education Standards, 2nd Edition, 2014)

Teacher Education InTASC Standards

• Use of data to support learning

• Standard 5 Application of Content

• Standard 6 Assessment

• Standard 7 Planning for Instruction

• Standard 8 Instructional Strategies

• Standard 9 Professional Learning and Ethical Practices

• Standard10 Leadership and Collaboration

• (2013 InTASC Model Core Teacher Standards)

The Conceptual Framework For Data Literacy For Teaching

From Mandinach, 2016

Using Data For TeachingFrom Mandinach, 2016

Current Research In Education

• Data literacy:

• Has a positive impact on teachers’ instructional practices (Gambell, 2004)

• Allows teachers to focus on specific instructional goals, refine assessments and increase feedback to students (Gambell & Hunter, 2004; Gearhart & Osmundson, 2009)

• However,

• Teachers often struggle with examining multiple data points and identifying patterns within data (Means et al., 2009)

• Teachers report low levels of confidence in their data-related knowledge/skills (Means et al., 2009)

Research In Physical Education

• Often focuses on data physical activity or physical fitness

• Dauenhauer (2014) dissertation• Physical activity and fitness data were collected at the expense of data from other related

teaching domains

• Teachers rarely transformed the data to actionable knowledge

• Teachers were collecting data because of requirements, saw no value in the data

• Data collection was time-consuming and posed challenges; lack of information management systems

• Professional development wasn’t provided to help teachers use data for effective teaching

QPE & Standards-based Physical Education

What data will provide evidence of:• National Standards?• Appropriate Practices?• Opportunity to Learn?

These documents go beyond a narrow focus of fitness and physical activity data

What Data Might Be Collected To Inform Instruction?

• Student characteristics: ESL, IEP, Health data

• Knowledge, skills, and attitudes/values of students in classes

• Student outcome data (is there an achievement gap in physical education?)

• Values and support of community stakeholders re: curriculum

• Resources / Equipment available for instruction

• Time allotment, Time on task

• Class size, facilities

Your Turn

From Mandinach, 2016

Articulate a problemFrame a questionDetermine data to be collected

a. What data are needed?b. From where will it be obtained?c. What is your action plan to obtain data?

Come back tomorrow for Part II! Glenn Hushman at 1:40 pmAnalyze the data to provide informationData-driven decision

Concluding Thoughts

• Teachers often indicate they “just know ‘IT’ when they see it”

• (‘IT’ may be student learning, good teaching, changes in motor skills, improved fitness)

• Current educational climate is on evidence-based practices

• Data literacy may be foreign; professional development is needed

• Data coaches may be needed to help educators become more data literate

• May help through challenges

• May help to rethink the questions and sources of data

References

• Council of Chief State School Officers. (2013,April). Interstate Teacher Assessment and Support Consortium InTASC Model Core Teacher Standards and Learning Progressions for Teachers 1.0: A Resource for Ongoing Teacher Development. Washington, DC: Author.

• Duncan, A. (2009). Federal leadership to support state longitudinal data systems. In Leveraging the Power of Data to Improve Education. Panel discussion at the Data Quality Campaign Conference, Washington DC.

• Dauenhauer, B. (2014). Data-Driven Decision Making in Physical Education: A Case Study. (Published doctoral dissertation). https://repositories.lib.utexas.edu/handle/2152/24745. University of Texas, Austin.

• Gambell,T. (2004). Teachers working around large-scale assessment: reconstructing professionalism and professional development. English Teaching: Practice and Critique, 3(2), 48-73.

• Gambell, T., & Hunter, D. (2004). Teacher scoring of large-scale assessment: professional development of debilitation? Journal of Curriculum Studies, 36(6), 697-724.

• Gearhart, M., & Osmundson, E. (2009). Assessment portfolios as opportunities for teacher learning. Educational Assessment, 14(1), 1-24.

• Mandinach, E. (2016). Data Literacy for Teachers. CAEP Pre-conference Workshop, San Diego, CA

• Mandinach, E. & Gummer, E. (2016). Data Literacy for Educators. Teachers College Press, New York, New York.

• Means, B., Padilla, C., DeBarger, , A., & Bakia, M. (2009). Implementing data-informed decision making in schools: Teacher access, supports and use. Washington, DC: U.S. Department of Education.

• National Board for Professional Teaching Standards (2014). Physical Education Standards for teachers of students ages 3-18+. 2nd Edition.

• Reeves, T., & Honig, S. (2015). A classroom data literacy intervention for pre-service teachers. Teaching and Teacher Education, 50, 90-101.