{"id":44833,"date":"2017-10-26T12:50:00","date_gmt":"2017-10-26T16:50:00","guid":{"rendered":"https:\/\/ced.ncsu.edu\/news\/?page_id=44833"},"modified":"2021-07-12T16:32:38","modified_gmt":"2021-07-12T20:32:38","slug":"data-alone-is-not-power-data-plus-thinking-is-power","status":"publish","type":"page","link":"https:\/\/ced.ncsu.edu\/news\/data-alone-is-not-power-data-plus-thinking-is-power\/","title":{"rendered":"“Data Alone is Not Power. Data Plus Thinking is Power.”"},"content":{"rendered":"\n
\"Data\"<\/a><\/figure>\n\n\n\n

The 21st century is an increasingly complex, data-driven world, where statistical literacy has become an essential skill for everyday life. Citizens must be able to deal with data in order to navigate modern society, whether that means explaining election results, making healthcare decisions or choosing how to invest in the stock market. Knowledge of statistics can also provide a competitive advantage in the job market, where positions as statisticians and data scientists are among the top-ranked careers.<\/p>\n\n\n\n

In addition, standards for teaching mathematics have placed a strong emphasis on statistics, particularly at the middle and high school levels. However, meeting these more rigorous standards can be challenging for teachers, who often lack the proficiency and resources needed to effectively teach the subject.<\/p>\n\n\n\n

Because of the increased importance of statistics, a team of researchers at NC State led by Hollylynne Lee, Ph.D.<\/a>, is working to improve the ways in which statistics in taught in the classroom.<\/p>\n\n\n\n

The Research<\/h2>\n\n\n\n
\"Hollylynne
Hollylynne Lee<\/figcaption><\/figure><\/div>\n\n\n\n

Lee, professor of mathematics and statistics education, has spent the last 18 years designing learning opportunities to help secondary teachers bring statistics and probability to life in the classroom. Recently, she has explored the use of online courses and professional development modules as a platform to reach more teachers and students across geographic boundaries.<\/p>\n\n\n\n

In Spring 2015, Lee and her team launched a massive open online course (MOOC) called \u201cTeaching Statistics Through Data Investigations\u201d through the NC State College of Education\u2019s Friday Institute for Educational Innovation<\/a>. Her course has reached far: to date, educators from 50 U.S. states and 84 countries have participated in the course.<\/p>\n\n\n\n

One of the goals of the course is to change teachers\u2019 beliefs and perspectives about statistics in order to change their teaching practices. Statistics is about more than just calculating a median or a mean, or being able to chart a graph, Lee says. It is about harnessing data to answer complex questions and gain a deeper understanding of the world. As such, the course presents a framework for statistical investigation that mirrors the scientific process, progressing through four phases (pose, collect, analyze, interpret).<\/p>\n\n\n\n

In surveys of participants six months after taking the course, 83% of survey respondents reported they gained important skills knowledge and resources; of those participants, 63% said they have changed their practice. One of the most often cited change to practice is to use real world messy data with students and engage them in more than merely number crunching the data.<\/p>\n\n\n

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\u201cI had a \u2018lightbulb moment.\u2019 Although I have been teaching HS math for 24 years, I have never actually taught \u201cstatistics\u201d as defined by the members of the expert panel. I have taught units that I THOUGHT were statistics, but I was merely providing students with a few mathematical tools that statisticians [sic] can use (e.g. finding a mean, making a histogram, calculating a standard deviation, etc.).\u201d <\/p>\n

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\u2014<\/strong> Participant post on MOOC discussion forum<\/strong><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/blockquote>\n\n\n\n

The Real World<\/h2>\n\n\n\n

According to Lee, creating scenarios where teachers and students play with real-world datasets \u2014 like census data or climate data \u2013 is an effective way to demonstrate the value of statistical thinking. In one scenario, for example, Lee has given students a dataset from the last hundred years on rollercoasters in the United States. Working with the data, the students become naturally curious about wanting to discover the story of rollercoasters. Did they all get bigger and faster? How do wood rollercoasters compare to steel rollercoasters? Does it matter if they invert you or not?<\/p>\n\n\n\n

\u201cData alone is not power. Data plus thinking is power,\u201d says Lee. \u201cThe data doesn\u2019t tell you the story. You have to understand which pieces of data are relevant and explanatory and which pieces are not.\u201d<\/p>\n\n\n\n

Statistical thinking develops along a continuum, and it takes time. Lee has found that breaking down the content of MOOCs into easily digestible pieces \u2013 two-page briefs, five-minute videos, accessible on mobile devices at all hours of the day \u2013 reduces barriers to learning.<\/p>\n\n\n

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4 BASIC PRINCIPLES OF SUCCESSFUL MOOCS<\/h2>\n <\/div>\n
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Professional development must be\u00a0connected to practice<\/strong>. Provide choices and variety (i.e., videos, tasks, experiences) so that teachers can see how they can use the information in their practice.<\/p>\n\t \t \n <\/div>\n\n <\/div>\n\n\n

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Discussion forums allow critical space for\u00a0peer support<\/strong>. Teachers can reinforce their learning and build networks through the exchange of ideas on various topics (i.e., task design, pedagogy).<\/p>\n\t \t \n <\/div>\n\n <\/div>\n\n\n

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People learn best when they hear from\u00a0multiple voices<\/strong>. In addition to messages delivered by the lead instructor, include other sources of information like journal articles, taped classroom interactions, interviews with students, and expert panel discussions.<\/p>\n\t \t \n <\/div>\n\n <\/div>\n\n\n

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Courses should be designed to promote\u00a0self-directed learning<\/strong>. Provide flexibility, through differentiated activities and resources, with personalized options, to make it easier for busy teachers to complete units, on their time.<\/p>\n\t \t \n <\/div>\n\n <\/div>\n\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n\n

What’s Next<\/h2>\n\n\n\n

This fall, with funding from the William and Flora Hewlett Foundation, Lee and her team at the Friday Institute launched a second MOOC, called \u201cTeaching Statistics Through Inferential Reasoning,\u201d to meet the demand for more course offerings.<\/p>\n\n\n\n

She is also using the knowledge she has gleaned from developing these MOOCs to enhance the training of the next generation of teachers, who she has found are ill-prepared to teach statistics. Lee\u2019s NSF-funded project for Enhancing Statistics Teacher Education through E-Modules (ESTEEM) will create online resources for statistics preservice teacher education, design modules and approaches for using these resources, and implement resources and modules in undergraduate mathematics teacher education programs.<\/p>\n\n\n\n

What are Lee\u2019s hopes for the future of statistics education? \u201cI hope people start loving it,\u201d says Lee. \u201cMy hope is that we as a society learn to value statistics and data, and that we develop future citizens who have the right education and the right mindset when it comes to how statistics can be applied in everyday life.\u201d<\/p>\n\n\n\n

– Written by Marla Broadfoot<\/em><\/p>\n\n\n\n