연구동향 분석
박사

Assessment of Informal Statistical Inference : 비형식적 통계적 추리의 평가

박민선 2015년

논문상세정보
인용/피인용
' Assessment of Informal Statistical Inference : 비형식적 통계적 추리의 평가' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • abduction
  • argumentation
  • assessment model
  • induction
  • informal statistical inference
  • integration of instruction and assessment
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
158 0

0.0%

' Assessment of Informal Statistical Inference : 비형식적 통계적 추리의 평가' 의 참고문헌

  • Woo, J. -H., Chong, Y. -O., Park, K. -M., Lee, K. -H., Kim, N. -H., Na, G. -S., & Yim, J. -H. (2006). Research Methods in Mathematics Education. Seoul: Kyung Moon Sa. (in Korean)
  • Woo, J. -H. (2000). An exploration of the reform direction of teaching statistics. Journal of the Korea Society of Educational Studies in Mathematics: School Mathematics, 2(1), 1-27. (in Korean)
  • Wilson, M., & Carstensen, C. (2007). Assessment to improve learning in mathematics: the BEAR assessment system. In A. Schoenfeld (Ed.), Assessing Mathematical Proficiency (pp. 311-332). New York, NY: Cambridge University Press.
  • Wiliam, D., & Thompson, M. (2008). Integrating assessment with learning: what will it take to make it work? In C. A. Dwyer (Ed.), The Future of Assessment: Shaping Teaching and Learning (pp. 53-82). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Wiliam, D. (2007). Keeping learning on track: Classroom assessment and the regulation of learning. In F. Lester (Ed.), Second Handbook of Research on Mathematics Teaching and Learning (pp. 1053-1098). Charlotte, NC: Information Age Publishing.
  • Webb, N. (1992). Assessment of students' knowledge of mathematics: steps toward a theory. In D. Grouws (Ed.), Handbook of Research on Mathematics Teaching and Learning (pp. 334-368). New York, NY: Macmillan.
  • Watson, J., Callingham, R., & Kelly, B. (2007). Students' appreciation of expectation and variation as a foundation for statistical understanding. Mathematical Thinking and Learning, 9(2), 83-130.
  • Watson, J., & Moritz, J. (1999). The beginning of statistical inference: comparing two data sets. Educational Studies in Mathematics, 37(2), 145-168.
  • Watson, J. (2008). Exploring beginning inference with novice grade 7 students. Statistics Education Research Journal, 7(2), 59-82.
  • Watson, J. (2006). Statistical Literacy at School: growth and goals. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Watson, A. (2007). The nature of participation afforded by tasks, questions and prompts in mathematics classrooms. Research in Mathematics Education, 9(1), 111-126.
  • Watson, A. (2000). Mathematics teachers acting as informal assessors: practices, problems and recommendations. Educational Studies in Mathematics, 41, 69- 91.
  • Van den Heuvel-Panhuizen, M., & Becker, J. (2003). Towards a didactic model for assessment design in mathematics education. In A. J. Bishop, M. A. Clements, C. Keitel, J. Kilpatrick & F. K. S. Leung (Eds.), Second International Handbook of Mathematics Education (pp. 689-716). Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Uchiyama, M. (2004). Teachers' Use of Formative Assessment in Middle School Reform-Based Mathematics Classrooms (Unpublished doctoral dissertation). University of Colorado, Colorado.
  • Toulmin, S. (1958). The Uses of Argument. Cambridge: Cambridge University Press.
  • Torrance, H., & Pryor, J. (2001). Developing formative assessment in the classroom: using action research to explore and modify theory. British Educational Research Journal, 27(5), 615-631.
  • Swaffield, S. (2011). Getting to the heart of authentic assessment for learning. Assessment in Education: Principles, Policy, & Practice, 18(4), 433-449.
  • Stigler, S. M. (1986). The history of statistics: The measurement of uncertainty before 1900. Cambridge, MA: Harvard University Press.
  • Shin, B. -M. (2012). A study on a didactic transposition method and students’ understanding for the normal distribution. Journal of Educational Research in Mathematics, 22(2), 117-136. (in Korean)
  • Shepard, L. (2000). The role of assessment in a learning culture. Educational Researcher, 29(7), 4-14.
  • Shaughnessy, J. M., Ciancetta, M., Best, K., & Canada, D. (2004). Students' attention to variability when comparing distribution. Paper presented at the 82nd Annual Meeting of the National Council of Teachers of Mathematics. Philadelphia, PA.
  • Sfard, A., (2008). Thinking as Communicating: Human Development, the Growth of Discourse, and Mathematizing. Cambridge: Cambridge University Press.
  • Sedlmeier, P., & Gigerenzer, G. (1997). Intuitions about sample size: The empirical law of large numbers. Journal of Behavioral Decision Making, 10(1), 33-51.
  • Salsburg, D. (2001). The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. New York, NY: W. H. Freeman.
  • Salmon, W. C. (1967). The Foundations of Scientific Inference. Pittsburgh, PA: University of Pittsburgh Press.
  • Saldanha, L., & Thompson, P. (2002). Conceptions of sample and their relationship to statistical inference. Educational Studies in Mathematics, 51, 257-270.
  • Rubin, A., Hammerman, J., & Konold, C. (2006). Exploring informal inference with interactive visualization software. In Proceedings of the Seventh International Conference on Teaching Statistics (ICOTS-7). Salvador, Bahia, Brazil.
  • Rubin, A., Bruce, B., & Tenney, Y. (1991). Learning about sampling: Trouble at the core of statistics. In D. Vere-Jones (Ed.), Proceedings of the Third International Conference on Teaching Statistics. Vol. 1. School and General Issues (pp. 314-319). Voorburg, The Netherlands: International Statistical Institute.
  • Rossman, A. (2008). Reasoning about informal statistical inference: One statistician's view. Statistics Education Research Journal, 7(2), 5-19.
  • Reading, C., & Shaughnessy, M. (2004). Reasoning about variation. In D. Ben-Zvi & J. Garfield (Eds.), The Challenge of Developing Statistical Literacy, Reasoning, and Thinking (pp. 201-226). Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Reading, C., & Reid, J. (2006). An emerging hierarchy of reasoning about distribution: From a variation perspective. Statistics Education Research Journal, 5(2), 46- 68.
  • Rao, C. R. (1997). Statistics and Truth: Putting Chance to Word (2nd Ed.). Singapore: World Scientific.
  • Ramaprasad, A. (1983). On the definition of feedback. Behavioral Science, 28(1), 4- 13.
  • Pfannkuch, M. (2011). The role of context in developing informal statistical inferential reasoning: A classroom study. Mathematical Thinking and learning, 13, 27-46.
  • Pfannkuch, M. (2008). Building sampling concepts for statistical inference: A case study. In Proceedings of the 11th International Congress on Mathematics Education (ICME-11). Monterrey, Mexico.
  • Pfannkuch, M. (2007). Year 11 students' informal inferential reasoning: a case study about the interpretation of box plots. International Electronic Journal of Mathematics Education, 2(3), 149-167.
  • Pfannkuch, M. (2006). Comparing box plot distributions: A teacher's reasoning. Statistics Education Research Journal, 5(2), 27-45.
  • Pfannkuch, M. (2005). Probability and statistical inference: how can teachers enable learners to make the connection? In G. Jones (Ed.), Exploring Probability in School: Challenges for Teaching and Learning (pp. 267-294). New York, NY: Springer.
  • Pellegrino, J., Baxter, G., & Glaser, R. (1999). Addressing the “two disciplines” problem: Linking theories of cognition and learning with assessment and instructional practice. Review of Research in Education, 24, 307-353.
  • Peirce, C. S. (1878). Deduction, induction, and hypothesis. Popular Science Monthly, 13, 470-482.
  • Park, J. (2012). Developing and Validating an Instrument to Measure College Students’ Inferential Reasoning in Statistics: An Argument-Based Approach to Validation (Unpublished doctoral dissertation). University of Minnesota, Minnesota.
  • Paparistodemou, E., & Meletiou-Movrotheris, M. (2008). Developing young students’ informal inference skills in data analysis. Statistics Education Research Journal, 7(2), 83-106.
  • NationalCouncilofTeachersofMathematics(2000).Principlesandstandardsforschool mathematics. Reston, VA: National Council of Teachers ofMathematics.
  • National Research Council. (2001). Knowing What Students Know: The Science and Design of Educational Assessment. Washington, D. C.: National Academy Press.
  • Morgan, C., & Watson, A. (2002). The interpretative nature of teachers' assessment of students' mathematics: issues for equity. Journal for Research in Mathematics Education, 33(2), 78-110.
  • Ministry of Education and Science Technology. (2011). Mathematics Curriculum. Seoul: Author. (in Korean)
  • Makar, K., Bakker, A., & Ben-Zvi, D. (2011). The reasoning behind informal statistical inference. Mathematical Thinking and Learning, 13(1-2), 152-173.
  • Makar, K., & Rubin, A. (2009). A framework for thinking about informal statistical inference. Statistics Education Research Journal, 8(1), 82-105.
  • Makar, K., & Rubin, A. (2007). Beyond the bar graph: Teaching informal inference in primary school. In D. Pratt & J. Ainley (Eds.), Proceedings of the Fifth International Research Forum on Statistical Reasoning, Thinking, and Literacy (SRTL-5). University of Warwick, UK.
  • Makar, K., & Confrey, J. (2002). Comparing two distributions: investigating secondary teachers' statistical thinking. In Proceedings of the Sixth International Conference on Teaching Statistics (ICOTS-6). Cape Town, South Africa.
  • Lyon, E. (2011). Beliefs, practices, and reflection: exploring a science teacher's classroom assessment through the assessment triangle model. Journal of Science Teacher Education, 22, 417-435.
  • Lipson, K. (2002). The role of computer based technology in developing understanding of the concept of sampling distribution. In Proceedings of the Sixth International Conference on Teaching Statistics (ICOTS-6). Cape Town, South Africa.
  • Lee, Y. -H., & Shin, S. -Y. (2011). Features of sample concepts in the probability and statistics chapters of Korean mathematics textbooks of grades 1-12. Journal of Educational Research in Mathematics, 21(4), 327-344. (in Korean)
  • Lee, Y. -H., & Nam, J. -H. (2005). An epistemological inquiry on the development of statistical concepts. Journal of Korea Society of Mathematics Education Series A: The Mathematics Education, 44(3), 457-475. (in Korean)
  • Lee, Y. -H., & Lee, E. -H. (2010). The design and implementation to teach sampling distributions with the statistical inferences. Journal of Korea Society of Educational Studies in Mathematics: School Mathematics, 12(3), 273-299. (in Korean)
  • Lee, O. -S., Lim, Y. -B., Seong, N. -G., & So, B. -S. (2000). Introduction to Statistics (2nd Ed.). Seoul: Kyung Moon Sa. (in Korean)
  • Lee, M. -S., & Park, Y. -H. (2006). A case study on understanding of the concept of sampling and data analysis by elementary 6th graders. Journal of Korea Society of Educational Studies in Mathematics: School Mathematics, 8(4), 441-463. (in Korean)
  • Lee, K. -H., & Ji, E. -J. (2005). Pedagogical significance and students’ informal knowledge of sample and sampling. Journal of Educational Research in Mathematics, 15(2), 177-196. (in Korean)
  • Lee, K. -H. (1996). A Study on the Didactic Transposition of the Concept of Probability (Unpublished doctoral dissertation). Seoul National University, Seoul. (in Korean)
  • Lee, J. -M. (1988). Logic of experiment: scientific explanation and inference. In Korean Psychological Association (Ed.), Introduction to Methodologies of Experimental Psychology: Hypothesis generating, Experimental Designs, and Analysis (pp. 73-116). Seoul: Seung Won Sa. (in Korean)
  • Kwon, C. -E., (1996). Inductive inference of Aristotle. In H. Yeo (Ed.), Logic and Truth (pp. 211-224). Seoul: Chulhak goa Hyunsilsa. (in Korean)
  • Krummheuer, G. (1995). The ethnography of argumentation. In P. Cobb & H. Bauersfeld (Eds.), The Emergence of Mathematical Meaning: Interaction in Classroom Cultures (pp. 229-269). Hillsdale, NJ: Lawrence Erlbaum.
  • Ko, E. -S., & Lee, K. -H. (2011). Pre-service teachers’ understanding of statistical sampling. Journal of Educational Research in Mathematics, 21(1), 17-32. (in Korean)
  • Ko, E. -S. (2012). A comparison of mathematically talented students and non-talented students’ level of statistical thinking: statistical modeling and sampling distribution understanding. Journal of Gifted/Talented Education, 22(3), 503- 525. (in Korean)
  • Klenowski, V. (2009). Editorial: Assessment for learning revisited: An Asia-Pacific perspective. Assessment in Education: Principles, Policy, & Practice, 16(3), 263-268.
  • Kim, Y. -H. (2006). A study of using the terminology of sampling error and sampling distribution. Journal of the Korean School Mathematics Society, 9(3), 309- 316. (in Korean)
  • Kim, W. -C., Kim, J. -J., Park, B. -U., Park, S. -H., Song, M. -S., Lee, S. -Y., ... Cho, S. S. (2006). General Statistics (1st Ed.). Seoul: Youngchi Moonhwa Sa. (in Korean)
  • Kim, S. -H., & Lee, C. -H. (2002). Abduction as a mathematical reasoning. Journal of Educational Research in Mathematics, 12(2), 275-290. (in Korean)
  • Kim, K. -S. (2007). Logic and Critical Thinking. Seoul: Chulhak goa Hyunsilsa. (in Korean)
  • Kim, H. -K. (2001). Statistical Inference. Seoul: Acanet. (in Korean)
  • Kaplan, J., Fisher, D., & Rogness, N. (2010). Lexical ambiguity in statistics: how students use and define the words: association, average, confidence, random and spread. Journal of Statistics Education, 18(2). 1-22. Available online: http://www.amstat.org/publications/jse/v18n2/kaplan.pdf
  • Kaplan, J., Fisher, D., & Rogness, N. (2009). Lexical ambiguity in statistics: what do students know about the words association, average, confidence, random and spread? Journal of Statistics Education, 17(3). 1-19. Available online: http://www.amstat.org/publications/jse/v17n3/kaplan.html
  • Kalinowski, P., Fidler, F., & Cumming, G. (2008). Overcoming the inverse probability fallacy: A comparison of two teaching interventions. Experimental Psychology, 4(4), 152-158.
  • Jeon, Y. -S. (2013). When Can We Make an Inductive Leap? Seoul: Acanet. (in Korean)
  • Jeon, Y. -S. (2000). Inductive Logic of Carnap. In C. Lee (Ed.), Inductive Logic and Philosophy of Science (pp. 79-105). Seoul: Chulhak goa Hyunsilsa. (in Korean)
  • Hwang, S. -G., & Yoon, J. -H. (2011). A study on teaching continuous probability distribution in terms of mathematical connection. Journal of Korea Society of Educational Studies in Mathematics: School Mathematics, 13(3), 423-446. (in Korean)
  • Holland, J., Holyoak, K., Nisbett, R., & Thagard, P. (1989). Induction:Processes of inference, learning, and discovery. Cambridge, MA:MIT Press.
  • Holcomb, J., Chance, B., Rossman, A., & Cobb, G. (2010). Assessing student learning about statistical inference. In C. Reading (Ed.), Proceedings of the Eighth International Conference on Teaching Statistics (ICOTS-8). Ljubljana, Slovenia.
  • Ho, Y. C. (1994). Abduction? Deduction? Induction? Is there a logic of exploratory data analysis? Paper presented at the Annual Meeting of American Educational Research Association. New Orleans, Louisiana.
  • Herman, J. (2013). Formative assessment for next generation science standards: a proposed model. Paper presented at Invitational Research Symposium on Science Assessment. Washington, D. C.
  • Heritage, M. (2010). Formative assessment and next-generation assessment systems: are we losing an opportunity? Paper prepared for the Council of Chief State School Officers. Washington, D. C.
  • Harlen, W. (2006). On the relationship between assessment for formative and summative purposes. In J. Gardner (Ed.), Assessment and Learning (pp. 103- 117). London: Sage Publications.
  • Haller, H., & Krauss, S. (2002). Misinterpretations of significance: A problem students share with their teachers? Methods of Psychological Research Online, 7(1), 1-20.
  • Hacking, I. (2012). The Taming of Chance. (H. Geong, Trans.). Seoul: Badabooks Publications. (Original work Published 1990) (in Korean)
  • Hacking, I. (1990). The Taming of Chance. Cambridge: Cambridge University Press.
  • Hacking, I. (1980). The theory of probable inference: Neyman, Peirce, and Braithwaite. In D. H. Mellor (Ed.), Science, Belief, and Behaviour: Essays in honour of R. B. Braithwaite (pp. 141-160). Cambridge: Cambridge University Press.
  • Gower, B. (1997). Scientific Method: A Historical and Philosophical Introduction. London: Routledge.
  • Gigerenzer, G., Krauss, S., & Vitouch, O. (2004). The null ritual what you always wanted to know about significance testing but were afraid to ask. In D. Kaplan (Ed.), The Sage Handbook of Quantitative Methodology for the Social Sciences (pp. 391-408). Thousand Oaks, CA: Sage Publications.
  • Garfield, J., delMas, R., & Chance, B. (2002). Assessment Resource Tools for Improving Statistical Thinking. Retrieved from https://apps3.cehd.umn.edu/artist/index.html
  • Garfield, J., & Ben-Zvi, D. (2009). Helping students develop statistical reasoning: implementing a statistical reasoning learning environment. Teaching Statistics, 31(3), 72-77.
  • Garfield, J., & Ben-Zvi, D. (2008). Developing Students' Statistical Reasoning: Connecting Research and Teaching Practice. Dordrecht, The Netherlands: Springer.
  • Gal, I., & Garfield, J. (1997). Curricular goals and assessment challenges in statistics education. In I. Gal & J. Garfield (Eds.), The Assessment Challenge in Statistics Education (pp. 1-13). Amsterdam, The Netherlands: IOS Press.
  • Gal, I. (2004). Statistical literacy: Meanings, components, responsibilities. In D. Ben- Zvi & J. Garfield (Eds.), The Challenge of Developing Statistical Literacy, Reasoning, and Thinking (pp. 47-78). Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Ekmekci, A. (2013). Mathematical Literacy Assessment Design: A Dimensionality Analysis of Programme for International Student Assessment (PISA) Mathematics Framework (Unpublished doctoral dissertation). The University of Texas at Austin, Texas.
  • Curcio, F. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education, 18(5), 382-393.
  • Cowie, B., & Bell, B. (1999). A model of formative assessment in science education. Assessment in Education: Principles, Policy & Practice, 6(1), 101-116.
  • Cobb, P., Stephan, M., McClain, K., & Gravemeijer, K. (2001). Participating in classroom mathematical practices. The Journal of the Learning Sciences, 10(1&2), 113-163.
  • Cobb, P., & McClain, K. (2004). Principles of instructional design for supporting the development of students' statistical reasoning. In D. Ben-Zvi & J. Garfield (Eds.), The Challenge of Developing Statistical Literacy, Reasoning, and Thinking (pp. 375-395). Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Cobb, G., & Moore, D. (1997). Mathematics, statistics, and teaching. The American Mathematical Monthly, 104(9), 801-823.
  • Ciancetta, M. A. (2007). Statistics Students Reasoning when Comparing Distributions of Data (Unpublished doctoral dissertation). Portland State University, Portland.
  • Choi, J. -S., Yun, Y. -S., & Hwang, H. -J. (2014). A study on pre-service teachers’ understanding of random variable. Journal of Korea Society of Educational Studies in Mathematics: School Mathematics, 16(1), 19-37. (in Korean)
  • Chance, B., delMas, R., & Garfield, J. (2004). Reasoning about sampling distribution. In D. Ben-Zvi & J. Garfield (Eds.), The Challenge of Developing Statistical Literacy, Reasoning, and Thinking (pp. 295-323). Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Burrill, G. (2007). The role of formative assessment in teaching and learning statistics. In Proceedings of the IASE/ISI Satellite Conference on Assessing Student Learning in Statistics. Guimaraes, Portugal.
  • Brookhart, S. (2003). Developing measurement theory for classroom assessment purposes and uses. Educational Measurement: Issues and Practice, 22(4), 5- 12.
  • Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation, and Accountability, 21, 5-31.
  • Black, P., & Wiliam, D. (2006). Developing a theory of formative assessment. In J. Gardner (Ed.), Assessment and Learning (pp. 81-100). London: Sage Publications.
  • Bennett, R. (2011). Formative assessment: a critical review. Assessment in Education: Principles, Policy, & Practice, 18(1), 5-25.
  • Ben-Zvi, D., Makar, K., Bakker, A., & Aridor, K. (2011). Children's emergent inferential reasoning about samples in an inquiry-based environment. In M. Pytlak, T. Rowland, & E. Swoboda (Eds.), Proceedings of the Seventh Congress of the European Society for Research in Mathematics Education. University of Rzeszow, Poland.
  • Ben-Zvi, D., Makar, K., & Bakker, A. (2009). Towards a framework for understanding students’ informal statistical inference and argumentation. In K. Makar (Ed.), Proceedings of the Sixth International Research Forum on Statistical Reasoning, Thinking, and Literacy (SRTL-6). University of Queensland, Brisbane, Australia.
  • Ben-Zvi, D., Gil, E., & Apel, N. (2007). What is hidden beyond the data? Helping young students to reason and argue about some wider universe. In D. Pratt & J. Ainley (Eds.), Proceedings of the Fifth International Research Forum on Statistical Reasoning, Thinking, and Literacy (SRTL-5). University of Warwick, UK.
  • Ben-Zvi, D., Aridor, K., Maker, K., & Bakker, A. (2012). Students’ emergent articulations of uncertainty while making informal statistical inferences. ZDM the International Journal on Mathematics Education, 44(7), 913-925.
  • Ben-Zvi, D., & Sfard, A. (2007). Ariadne's thread, daedalus' wings, and the learner's autonomy. Education & Didactique, 1(3), 123-142.
  • Ben-Zvi, D., & Gil, E. (2010). The role of context in the development of students’ informal inferential reasoning. In C. Reading (Ed.), Proceedings of the Eighth International Conference on Teaching Statistics (ICOTS-8). Ljubljana, Slovenia.
  • Ben-Zvi, D., & Aridor, K. (2012). Children's wonder how to wander between data and context. In Proceedings of the 12th International Congress on Mathematics Education (ICME-12). Seoul, Korea.
  • Ben-Zvi, D. (2006). Scaffolding students' informal inference and argumentation. In A. Rossman and B. Chance (Eds.), Proceedings of the Seventh International Conference on Teaching Statistics (ICOTS-7). Salvador, Bahia, Brazil.
  • Batanero, C. (2000). Controversies around the role of statistical tests in experimental research. Mathematical Thinking and Learning, 2(1&2), 75-97.
  • Bakker, A., Kent, P., Derry, J., Noss, R., & Hoyles, C. (2008). Statistical inference at work: statistical process control as an example. Statistics Education Research Journal, 7(2), 130-145.
  • Bakker, A., & Gravemeijer, K. (2004). Learning to reason about distribution. In D. Ben-Zvi & J. Garfield (Eds.), The Challenge of Developing Statistical Literacy, Reasoning, and Thinking (pp. 147-168). Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Bakker, A., & Derry, J. (2011). Lessons from inferentialism for statistics education. Mathematical Thinking and Learning, 13, 5-26.
  • Bakker, A. (2004). Design Research in Statistics Education: On Symbolizing and Computer Tools. Utrecht, The Netherlands: CD Beta Press.
  • American Statistical Association. (2005). Guidelines for Assessment and Instruction in Statistical Education: College Report. Alexandria, VA: Author.
  • Allen, K., Stone, A., Reed-Rhoads, T., & Murphy, T. J. (2004). The statistics concept inventory: Developing a valid and reliable instrument. In Proceedings of the American Society for Engineering Education Annual Conference and Exposition. Salt Lake City.
  • Albert, L. (2000). Outside-in, inside-out: seventh-grade students’ mathematical thought processes. Educational Studies in Mathematics, 41, 109-141.
  • ?Abelson, R. P. (1995). Argumentation as Principled Argument. Hillsdale, NJ: Lawrence Erlbaum.