P1-08: The Benefits of an Inquiry Based Science Education (IBSE) Assessment in an Undergraduate-Level Statistics Course

By Brenna Butler & Jennifer Ann Morrow (University of Tennessee - Knoxville)


A recent shift in statistics education has placed an emphasis upon students’ understanding of statistical concepts in applied fields rather than statistical theory (Marshall, 2019). This poster will introduce and describe an assessment tool (e.g., midterm, final exam) developed from an inquiry-based science education (IBSE) framework for an undergraduate-level intermediate statistics course that measures students’ applied statistics higher-order thinking skills. An IBSE focuses on developing three authentic science skills in students: developing hypotheses, designing experiments, and evaluating evidence (van Uum et al., 2016) to overall develop students’ scientific critical thinking strategies and bolster students’ self-questioning of the appropriateness of such strategies (Klahr & Nigam, 2004).

Modeled off of the IBSE framework, this statistics assessment consists of four main sections: an exploratory phase of a real data set provided to students by the instructor, students’ own development of statistical hypotheses and appropriate statistical methods to utilize, analysis and interpretation of test results, and student self-evaluation of the appropriateness of the statistical process utilized. Each section requires students to write approximately 2-4 paragraphs and provide supporting statistical software output for their answers. Through emphasizing student-led decision making, evaluation of statistical methods, using real-life data, and holistic understanding of statistical concepts as opposed to memorization of statistic procedures (Franklin & Garfield, 2006; Marshall, 2019), this assessment could help foster students’ mastery of statistical procedures in context of realistic situations (McMillan, 2018). Audience members will be provided tips on how to utilize this assessment tool in their courses in order for their students to practice applied statistical methodology to prepare them for statistics-related careers.


  • Franklin, C., & Garfield, J. (2006). The guidelines for assessment and instruction in statistics education (GAISE). In G.F. Burrill, (Ed.), Thinking and Reasoning about Data and Chance: Sixty-eighth NCTM Yearbook (pp. 345-375). Reston, VA: National Council of Teachers of Mathematics.
  • Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15(10), 661-667. doi:10.1111/j.0956-7976.2004.00737.x
  • Marshall, E. (2019). Embedding and assessing project based statistics. MSOR Connections, 17(2), 75-82.
  • McMillan, J. H. (2018). Classroom assessment: Principles and practice that enhance student learning and motivation. Upper Saddle River, NJ: Pearson.
  • van Uum, M., Verhoeff, R., & Peeters, M. (2016). Inquiry-based science education: Towards a pedagogical framework for primary school teachers. International Journal of Science Education, 38(3), 450-469. doi:10.1080/09500693.2016.1147660