اهمیت و ضرورت آموزش علم داده در مدارس

نوع مقاله : مقاله ترجمه ای

نویسنده

گروه ریاضی- دانشگاه پیام نور- تهران- ایران

چکیده

پاندمی جهانی کووید-19 شرایطی ایجاد کرده است که همه افراد به درکی از داده‌های مربوط به گسترش بیماری در جامعه، سطوح خطر و کارآیی واکسن دست یابند. با این حال، تحقیقات نشان می‌دهد که توانایی دانش‌آموزان در سواد داده کافی نیست. تانیا لامار و جو بولر استدلال می کنند که آموزش علم داده فرصتی را برای رسیدگی به این مشکل و درعین حال فرصتی را برای یک بازنگری ضروری در برنامه درسی کنونی ریاضی، فراهم می کند. ادغام علم داده می تواند مسیر ریاضیات عادلانه تری نسبت به مسیر متمرکز بر حساب دیفرانسیل و انتگرال فراهم کند که اکثر دانش آموزان را از آینده ای در ریاضیات محروم کرده است. از طریق علم داده، دانش‌آموزان می‌توانند بیاموزند که به سؤالات مرتبط با زندگی و جامعه خود پاسخ دهند، مصرف‌کنندگان منتقدی برای داده‌هایی باشند که هر روز آنها را احاطه می‌کنند و از تجزیه و تحلیل داده‌ها به خوبی استفاده کنند.

کلیدواژه‌ها

موضوعات


[1] S. Ahmed, Data scientists are growing faster than demand,is it true?,https://thinkml.ai/
is-supply-of-data-scientists-growing-faster-than-demand/, (2019).
[2] R. Q. Berry III and M. R. Larson, The need to catalyze change in high school mathematics, Phi Delta Kappan, 100 (2019) 39–44.
[3] J. Boaler, Experiencing school mathematics: Traditional and reform approaches to teaching and their impact on student learning, Routledge, 2002.
[4] J. Boaler, Mathematical mindsets: Unleashing students’ potential through creative math, inspiring messages, and innovative teaching, John Wiley and Sons, 2015.
[5] J. Boaler, M. Cordero and J. Dieckmann, Pursuing gender equity in mathematics competitions: A case of mathemat-ical freedom, Mathematics Association of America MAA Focus, 39 (2019) 18–20.
[6] J. Boaler and S. D. Levitt, Opinion: Modern high school math should be about data science – not Algebra 2, The Los Angeles Times, 2019.
[7] J. Boaler and M. Staples, Creating mathematical futures through an equitable teaching approach: The case of Railside School, Teachers College Record, 110 (2008) 608–645.
[8] D. Bressoud, (Ed.), The role of calculus in the transition from high school to college mathematics, Mathematical Association of America and National Council of Teachers of Mathematics, 2017.
[9] S. H. Cha, Exploring disparities in taking high-level math courses in public high schools, KEDI Journal of Educa-tional Policy, 12 (2015).
[10] College Board, Program summary report 2020, Advanced Placement program participation, https://research.collegeboard.org/programs/ap/data/participation/ap-2020, 2020.
[11] P. Daro and H. Asturias, Branching out: Designing high school math pathways for equity, Just Equations, 2019.
[12] A. B. Diekman, E. R. Brown, A. M. Johnston and E. K. Clark, Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers, Psychological Science, 21 (2010) 1051–1057.
[13] A. B. Diekman, E. K. Clark, A. M. Johnston, E. R. Brown and M. Steinberg, Malleability in communal goals and beliefs influences attraction to STEM careers: Evidence for a goal congruity perspective, Journal of Personality and Social Psychology, 101 (2011) 902–918.
[14] J. Engel, Statistical literacy for active citizenship: A call for data science education. Statistics Education Research Journal, 16 (2017) 44–49.
[15] T. Erickson, M. Wilkerson, W. Finzer and F. Reichsman, Data moves. Technology Innovations in Statistics Education, 12 (2019).
[16] C. D. Evans and A. B. Diekman, On motivated role selection: Gender beliefs, distant goals, and career interest, Psychology of Women Quarterly, 33 (2009) 235–249.
[17] P. J. Fleming and J. J. Wallace, How not to lie with statistics: the correct way to summarize benchmark results. Communications of the ACM, 29 (1986) 218–221.
[18] L. Fries, J. Y. Son, K. B. Givvin and J. W. Stigler, Practicing connections: A framework to guide instructional design for developing understanding in complex domains, Educational Psychology Review, 33 (2021) 739–762.
[19] R. Gould, S. Machado, C. Ong, T. Johnson, J. Molyneux, S. Nolen, . . . .and L. Zanontian, Teaching data science to secondary students: The mobilize introduction to data science curriculum, In. J. Engel (Ed.), Proceedings of the Roundtable Conference of the International Association of Statistics Education, Berlin, (2016).
[20] D. Huff, How to lie with statistics, WWNorton and Company, 1993.
[21] S. Johnson, University of California expands list of courses that meet math requirement for admission, EdSource, (2020).
[22] J. Kahne and B. Bowyer, Educating for democracy in a partisan age: Confronting the challenges of motivated rea-soning and misinformation, American Educational Research Journal, 54 (2017) 3–34.
[23] S. Kesar, Closing the STEM gap: Why STEM classes and careers still lack girls and what we can do about it,
Microsoft Philanthropies, 2017.
[24] G. King, How not to lie with statistics: Avoiding common mistakes in quantitative political science, American Journal
of Political Science, 30 (1986) 666–687.
[25] Y. Koh, The movement to modernize math class, Wall Street Journal, (2020).
[26] C. Konold, T. Higgins, S. J. Russell and K. Khalil, Data seen through different lenses, Educational Studies in Math-ematics, 88 (2015) 305–325.
[27] Lawyers’ Committee for Civil Rights of the San Francisco Bay Area, Held back: Addressing misplacement of 9th grade students in Bay Area school math classes, Author, 2013.
[28] Messy Data Coalition. Catalyzing K − 12 data education: A coalition statement, https://messydata.org/statement.pdf, 2020.
[29] M. Niederle and L. Vesterlund, Explaining the gender gap in math test scores: The role of competition, Journal of Economic Perspectives, 24 (2010) 129–44.
[30] S. U. Noble, Algorithms of oppression: How search engines reinforce racism, NYU Press, 2018.
[31] C. O’Neil, Weapons of math destruction: How big data increases inequality and threatens democracy, Broadway Books, 2016.
[32] Program for International Student Assessment, PISA 2021 Mathematics Framework, Organization for Economic Co-operation and Development, 2020.
[33] A. Rubin, Learning to reason with data: How did we get here and what do we know? Journal of the Learning Sciences, (2019) 1–11.
[34] C. Spector, Bringing math class into the data age. Research Stories, Stanford Graduate School of Education, 2020.
[35] S. Wineburg, S. McGrew, J. Breakstone and T. Ortega, Evaluating information: The cornerstone of civic online reasoning, Stanford Digital Repository, 2016.
[36] C. Wolfram, The math(s) fix: An education blueprint for the AI age. Wolfram Media, Inc, 2020.
[37] A. Zucker, P. Noyce and A. McCullough, JUST SAY NO! Teaching students to resist scientific misinformation, The Science Teacher, 87 (2020) 24–29.