doi: 10.3934/fods.2021035
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Addressing confirmation bias in middle school data science education

1. 

Associate Professor, Department of Mathematical Sciences, College of Arts & Sciences, University of Cincinnati, USA

2. 

Gifted Intervention Specialist, Indian Hill Middle School, Indian Hill Ohio, USA

Received  July 2021 Revised  October 2021 Early access January 2022

More research is needed involving middle school students' engagement in the statistical problem-solving process, particularly the beginning process steps: formulate a question and make a plan to collect data/consider the data. Further, the increased availability of large-scale electronically accessible data sets is an untapped area of study. This interpretive study examined middle school students' understanding of statistical concepts involved in making a plan to collect data to answer a statistical question within a social issue context using data available on the internet. Student artifacts, researcher notes, and audio and video recordings from nine groups of 20 seventh-grade students in two gifted education pull-out classes at a suburban middle school were used to answer the study research questions. Data were analyzed using a priori codes from previously developed frameworks and by using an inductive approach to find themes.

Three themes that emerged from data related to confirmation bias. Some middle school students held preconceptions about the social issues they chose to study that biased their statistical questions. This in turn influenced the sources of data students used to answer their questions. Confirmation bias is a serious issue that is exacerbated due to endless sources of data electronically available. We argue that this type of bias should be addressed early in students' educational experiences. Based on the findings from this study, we offer recommendations for future research and implications for statistics and data science education.

Citation: Sarai Hedges, Kim Given. Addressing confirmation bias in middle school data science education. Foundations of Data Science, doi: 10.3934/fods.2021035
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M. Lenker, Motivated reasoning, political information, and information literacy education, Portal: Libraries and the Academy, 16 (2016), 511-528.  doi: 10.1353/pla.2016.0030.  Google Scholar

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J. Ridgway, Implications of the data revolution for statistics education, International Statistical Review, 84 (2016), 528-549.  doi: 10.1111/insr.12110.  Google Scholar

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M. B. Roscoe, A vehicle for bivariate data analysis, Mathematics Teaching in the Middle School, 21 (2016), 348-356.  doi: 10.5951/mathteacmiddscho.21.6.0348.  Google Scholar

[40]

A. Savard and D. Manuel, Teaching statistics: Creating an intersection for intra and interdisciplinarity, Statistics Education Research Journal, 15 (2016), 239-256.  doi: 10.52041/serj.v15i2.250.  Google Scholar

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P. Stapleton, Cognitive biases: Promoting good decision making in research methods courses, Teaching in Higher Education, 24 (2019), 578-586.  doi: 10.1080/13562517.2018.1557137.  Google Scholar

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D. E. Varberg, The development of modern statistics, The Mathematics Teacher, 56 (1963), 252-257.   Google Scholar

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J. Watson, K. Beswick, N. Brown, R. Callingham, T. Muir and S. Wright, Digging into Australian Data with TinkerPlots, Objective Learning Materials, Melbourne, 2011. Google Scholar

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J. M. Watson and J. B. Moritz, Development of sampling for statistical literacy, Journal of Mathematical Behavior, 19 (2000), 109-136.   Google Scholar

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C. H. Wild and M. Pfannkuch, Statistical thinking in empirical enquiry, International Statistical Review, (1999), 223–248. Available from http://www.jstor.org/stable/1403699. Google Scholar

[47]

M. H. Wilkerson and V. Laina, Middle school students' reasoning about data and context through storytelling with repurposed local data, ZDM - Mathematics Education, 50 (2018).  doi: 10.1007/s11858-018-0974-9.  Google Scholar

[48]

L. Zapata-Cardona, Students' construction and use of statistical models: A socio-critical perspective, ZDM - Mathematics Education, 50 (2018), 1213-1222.  doi: 10.1007/s11858-018-0967-8.  Google Scholar

show all references

References:
[1]

A. Agresti, C. Franklin and B. Klingenberg, Statistics: The Art and Science of Learning from Data, 4th edition, Pearson, Boston, 2017. Google Scholar

[2]

J. Ainley and D. Pratt, Computational modelling and children's expressions of signal and noise, Statistics Education Research Journal, 16 (2017), 15-37.  doi: 10.52041/serj.v16i2.183.  Google Scholar

[3]

The Annie E. Casey Foundation Kids Count Data Center, About Kids Count Data Center, (n.d.)., Available from https://datacenter.kidscount.org/about. Google Scholar

[4]

K. Aridor and D. Ben-Zvi, Statistical modeling to promote students' aggregate reasoning with sample and sampling, ZDM, 50 (2018), 1165-1181.  doi: 10.1007/s11858-018-0994-5.  Google Scholar

[5]

P. M. Arnold, Statistical Investigative Questions: An Enquiry into Posing and Answering Investigative Questions from Existing Data, Ph.D thesis, 2013. Available from http://researchspace.auckland.ac.nz. Google Scholar

[6]

A. Bargagliotti, C. Franklin, P. Arnold, R. Gould, S. Johnson, L. Perez and D. A. Spangler, Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education, American Statistical Association, Alexandria, 2020. Available from https://www.amstat.org/asa/education/Guidelines-for-Assessment-and-Instruction-in-Statistics-Education-Reports.aspx. Google Scholar

[7]

L. Bershidsky, No, big data didn't win the U.S. election, in Bloomberg View, (2016, December 8). Available from https://www.bloomberg.com/view/articles/2016-12-08/no-big-data-didn-t-win-the-u-s-election. Google Scholar

[8]

C. Büscher and S. Schnell, Students' emergent modelling of statistical measures – a case study, Statistics Education Research Journal, 16 (2017), 144-162.   Google Scholar

[9]

S. B. BushK. S. KarpJ. Albanese and F. Dillon, The oldest person you've known, Mathematics Teaching in the Middle School, 20 (2015), 278-285.  doi: 10.5951/mathteacmiddscho.20.5.0278.  Google Scholar

[10]

B. J. Casad, Confirmation bias, in Encyclopedia Britannica, 2009. Available from https://www.britannica.com/science/confirmation-bias. Google Scholar

[11]

J. W. Creswell, Qualitative Inquiry and Research Design: Choosing Among Five Approaches, 3rd edition, Sage, Thousand Oaks, 2013. Google Scholar

[12]

The Concord Consortium, Common Online Data Analysis Platform (CODAP), 2018. Available from https://codap.concord.org/. Google Scholar

[13]

M. Dawkins, B. Gentile-Henning, K. Given, M. Burgess, E. Leppert and M. Stewart, Information for Parents and Educators Working With Gifted Students, 2017. Available from https://www.indianhillschools.org/Downloads/Indian%20Hill%20Gifted%20Handbook%20November%202019.pdf. Google Scholar

[14]

C. Duhigg, How companies learn your secrets, in The New York Times Magazine, (2012, February 16). Available from http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?_r=1&pagewanted=all. doi: 10.7312/star16075-025.  Google Scholar

[15]

T. G. EdwardsA. Özgün-Koca and J. Barr, Interpretations of boxplots: Helping middle school students to think outside the box, Journal of Statistics Education, 25 (2017), 21-28.  doi: 10.1080/10691898.2017.1288556.  Google Scholar

[16]

Facing the Future, Facing the Future, 2018. Available from https://www.facingthefuture.org/. Google Scholar

[17]

C. Franklin, G. Kader, A. Bargagliotti, D. Mewborn, J. Moreno, R. Peck, M. Perry and R. Scheaffer, Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: A PreK-12 Curriculum Framework, American Statistical Association, Alexandria, 2007. Available from http://www.amstat.org/ASA/Education/Guidelines-for-Assessment-and-Instruction-in-Statistics-Education-Reports.aspx. Google Scholar

[18]

R. Gould, Statistics and the modern student, International Statistical Review, 78 (2010), 297-315.  doi: 10.1111/j.1751-5823.2010.00117.x.  Google Scholar

[19]

R. Gould, A. Bargagliotti and T. Johnson, An analysis of secondary teachers' reasoning with participatory sensing data, Statistics Education Research Journal, 16 (2017), 305–334. Available from http://iase-web.org/Publications.php?p=SERJ doi: 10.52041/serj.v16i2.194.  Google Scholar

[20]

R. E. GrothM. Jones and M. Knaub, Working with noise in bivariate data, Mathematics Teaching in the Middle School, 23 (2017), 82-89.  doi: 10.5951/mathteacmiddscho.23.2.0082.  Google Scholar

[21]

I. Hernandez and J. L. Preston, Disfluency disrupts the confirmation bias, Journal of Experimental Social Psychology, 49 (2013), 178-182.   Google Scholar

[22]

R. Hurworth, Interpretivism, in Encyclopedia of Evaluation (ed. S. Mathison), (2005), 209–210. Google Scholar

[23]

S. KazakD. Pratt and R. Gökce, Sixth grade students' emerging practices of data modelling, ZDM - Mathematics Education, 50 (2018), 1151-1163.  doi: 10.1007/s11858-018-0988-3.  Google Scholar

[24]

N. C. Lavigne and S. P. Lajoie, Statistical reasoning of middle school children engaging in survey inquiry, Contemporary Educational Psychology, 32 (2007), 630-666.   Google Scholar

[25]

M. Lenker, Motivated reasoning, political information, and information literacy education, Portal: Libraries and the Academy, 16 (2016), 511-528.  doi: 10.1353/pla.2016.0030.  Google Scholar

[26]

Y. S. Lincoln and E. G. Guba, Naturalistic Inquiry, Sage, Newbury Park, 1985. Google Scholar

[27]

Magnified Giving, Magnified Giving, (n.d.). Available from http://www.magnifiedgiving.org/. Google Scholar

[28]

J. H. Michalski, The sociological determinants of scientific bias, Journal of Moral Education, (2020).  doi: 10.1080/03057240.2020.1787962.  Google Scholar

[29]

M. B. Miles, A. M. Huberman and J. Saldaña, Qualitative Data Analysis: A Methods Sourcebook, 3rd edition, Sage, Thousand Oaks, 2014. Google Scholar

[30]

W. L. Neuman, Social Research Methods: Qualitative and Quantitative Approaches, 7th edition, Pearson Education, Boston, 2011. Google Scholar

[31]

C. Nolan and S. Herbert, Introducing linear functions: An alternative statistical approach, Mathematics Education Research Journal, 27 (2015), 401-421.  doi: 10.1007/s13394-015-0147-x.  Google Scholar

[32]

Ohio Department of Education, Gifted Screening and Identification, 2018. Available from https://education.ohio.gov/Topics/Other-Resources/Gifted-Education/Gifted-Screening-and-Identification. Google Scholar

[33]

C. O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Crown, New York, 2016.  Google Scholar

[34]

A. Patel and M. Pfannkuch, Developing a statistical modeling framework to characterize year 7 students' reasoning, ZDM, 50 (2018), 1197-1212.  doi: 10.1007/s11858-018-0960-2.  Google Scholar

[35]

Public School Review., 2018. Available from https://www.publicschoolreview.com/indian-hill-middle-school-profile. Google Scholar

[36]

J. Reid and C. Carmichael, A taste of Asia with statistics and technology, Australian Primary Mathematics Classroom, 20 (2015), 10-14.   Google Scholar

[37]

Represent, in Merriam-Webster.com, (n.d.). Available from https://www.merriam-webster.com/dictionary/represent. Google Scholar

[38]

J. Ridgway, Implications of the data revolution for statistics education, International Statistical Review, 84 (2016), 528-549.  doi: 10.1111/insr.12110.  Google Scholar

[39]

M. B. Roscoe, A vehicle for bivariate data analysis, Mathematics Teaching in the Middle School, 21 (2016), 348-356.  doi: 10.5951/mathteacmiddscho.21.6.0348.  Google Scholar

[40]

A. Savard and D. Manuel, Teaching statistics: Creating an intersection for intra and interdisciplinarity, Statistics Education Research Journal, 15 (2016), 239-256.  doi: 10.52041/serj.v15i2.250.  Google Scholar

[41]

P. Stapleton, Cognitive biases: Promoting good decision making in research methods courses, Teaching in Higher Education, 24 (2019), 578-586.  doi: 10.1080/13562517.2018.1557137.  Google Scholar

[42]

D. E. Varberg, The development of modern statistics, The Mathematics Teacher, 56 (1963), 252-257.   Google Scholar

[43]

J. Watson, K. Beswick, N. Brown, R. Callingham, T. Muir and S. Wright, Digging into Australian Data with TinkerPlots, Objective Learning Materials, Melbourne, 2011. Google Scholar

[44]

J. M. Watson and J. B. Moritz, Development of sampling for statistical literacy, Journal of Mathematical Behavior, 19 (2000), 109-136.   Google Scholar

[45]

H. G. Wells, Mankind in the Making, Chapman and Hall, London, 1903. Available from http://www.gutenberg.org/ebooks/7058. Google Scholar

[46]

C. H. Wild and M. Pfannkuch, Statistical thinking in empirical enquiry, International Statistical Review, (1999), 223–248. Available from http://www.jstor.org/stable/1403699. Google Scholar

[47]

M. H. Wilkerson and V. Laina, Middle school students' reasoning about data and context through storytelling with repurposed local data, ZDM - Mathematics Education, 50 (2018).  doi: 10.1007/s11858-018-0974-9.  Google Scholar

[48]

L. Zapata-Cardona, Students' construction and use of statistical models: A socio-critical perspective, ZDM - Mathematics Education, 50 (2018), 1213-1222.  doi: 10.1007/s11858-018-0967-8.  Google Scholar

Table 1.  Groups' Question and Study Design Development
Group (Size) Question Versions Initial Question Final Question Study Design for Final Question
1 (2) 3 How does overpopulation affect quality of life? How does overpopulation affect mortality rate? Most and least populated country in each continent in 2016 from World Bank, Statistics Times, and Worldometers
2 (2) 10 What affects poverty? [Do] race and ethnicity of people affect the average yearly income they make? Race/ethnicity and highest and lowest income categories in 2016 from U.S. Census
3 (2) 8 How many people are living in poverty? Is the rate of children being born into poverty decreasing in the United States of America? Child poverty in U.S. in 2000 – 2016 from KidsCount
4 (2) 4 How many people are eating based on MyPlate regulations in the United States of America per day? What percentage of students get a free or reduced lunches [sic] per county (in Ohio)? Six largest counties in Ohio in 2017 from CDC website
5 (3) 3 How many cats and dogs are abused each year in the USA? What percent of the animal abuse cases in New York City are neglected? Animal abuse cases reported in 2017-2108 from NYC Open Data
6 (2) 6 What are the ages of people in poverty? What percent of people in the U.S. are in poverty per state? Poverty per state in 2016 from KidsCount
7 (2) 11 How are animals affected by climate change? How has average precipitation within the U.S.A. changed over the course of 17 years? (2000-2017)? Average precipitation in 2000–2017 from National Centers for Environmental Information (NCEI)
8 (3) 6 What is the current poverty rate in the United States? What is the income to poverty ratio for people of different age groups? Income to poverty ratios by age group in 2016 from U.S. Census
9 (2) 4 What was the most dangerous modern war? What is the total number of civilian deaths in Afghanistan that were [sic] killed by ISAF from 2010 to 2013? Subset of CIVCAS database provided in Science Magazine
Group (Size) Question Versions Initial Question Final Question Study Design for Final Question
1 (2) 3 How does overpopulation affect quality of life? How does overpopulation affect mortality rate? Most and least populated country in each continent in 2016 from World Bank, Statistics Times, and Worldometers
2 (2) 10 What affects poverty? [Do] race and ethnicity of people affect the average yearly income they make? Race/ethnicity and highest and lowest income categories in 2016 from U.S. Census
3 (2) 8 How many people are living in poverty? Is the rate of children being born into poverty decreasing in the United States of America? Child poverty in U.S. in 2000 – 2016 from KidsCount
4 (2) 4 How many people are eating based on MyPlate regulations in the United States of America per day? What percentage of students get a free or reduced lunches [sic] per county (in Ohio)? Six largest counties in Ohio in 2017 from CDC website
5 (3) 3 How many cats and dogs are abused each year in the USA? What percent of the animal abuse cases in New York City are neglected? Animal abuse cases reported in 2017-2108 from NYC Open Data
6 (2) 6 What are the ages of people in poverty? What percent of people in the U.S. are in poverty per state? Poverty per state in 2016 from KidsCount
7 (2) 11 How are animals affected by climate change? How has average precipitation within the U.S.A. changed over the course of 17 years? (2000-2017)? Average precipitation in 2000–2017 from National Centers for Environmental Information (NCEI)
8 (3) 6 What is the current poverty rate in the United States? What is the income to poverty ratio for people of different age groups? Income to poverty ratios by age group in 2016 from U.S. Census
9 (2) 4 What was the most dangerous modern war? What is the total number of civilian deaths in Afghanistan that were [sic] killed by ISAF from 2010 to 2013? Subset of CIVCAS database provided in Science Magazine
Table 2.  Pertinent Data Codes and Themes
Type Code/Theme
In vivo "reliable data"
Emergent Sampling from the extremes Preconceptions
Type Code/Theme
In vivo "reliable data"
Emergent Sampling from the extremes Preconceptions
Table 3.  Students' Meanings of the Term Reliable Data Source
Reliable data come from... Count (Groups)
Government organization or from the teacher 4 (1, 6, 7, 9)
Varied/multiple sources 4 (2, 3, 7, 9)
No explanation given 1 (8)
Note: Groups 7 and 9 gave different definitions at different times.
Reliable data come from... Count (Groups)
Government organization or from the teacher 4 (1, 6, 7, 9)
Varied/multiple sources 4 (2, 3, 7, 9)
No explanation given 1 (8)
Note: Groups 7 and 9 gave different definitions at different times.
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