September 10, 2013Teaching & Learning Webinar

Dennis Pearl, OSU and CAUSE

Phase I: Creating a Shared Vision Phase II: Planning & Initial ActionsPhase III: Support for ResearchersPhase IV: Support for Programs/Institutions

Motivation◦ Need to develop and promote high-quality

interdisciplinary graduate programs in statistics education

◦ Address issues common in emerging fields such as support for statistics education faculty, building infrastructure, defining a core curriculum

Phase I workshop October 24/25, 2008 Final Report at

www.CAUSEweb.org/research/programs

Summer Institutes (phase II focus) Statistics Education Research Retreats (phase III) National Mentoring Program Statistics Education On-line Opportunities Leveraging Connections to other STEM Ed

Programs Phase II workshop July 30/31, 2009

Phase III Final Report at www.CAUSEweb.org/research/guidelines

Phase IV workshop September 28/29, 2012

Report arising from: research retreat (June 14-15, 2010) and writing team meeting (May 21-22, 2011).

To foster productivity and coherence in statistics education research by providing guidance on important priorities in the field, and

To provide the impetus for development and wide use of instruments needed to address fundamental questions in statistics education research.

Broad descriptions of issues, Gateways to the literature, Research priorities & example questions, Measurement/assessment needs Why questions are important and the

implications of addressing them.

Cognitive outcomes Affective constructs Curriculum Teaching Practice Teacher Development Technology

+ Bonus chapter on General Statistics Education Assessment Issues

Research Priority 1: How do different written, intended, and enacted curricula support or impede students’ development of learning and/or affective outcomes for different purposes and groups of students?

Research Priority 1: How do different written, intended, and enacted curricula support or impede students’ development of learning and/or affective outcomes for different purposes and groups of students?

o What curricular sequences and approaches are effective for long-term retention?

o What curricular sequences and approaches are effective to support learning progressions/trajectories for particular statistical ideas?

o How large is the effect size on the student outcomes when comparing two curricula?

o What can be left out of the curriculum, and what should be added, given factors such as advances in the field and technology, changes in the K-12 curriculum, or the changing needs of client disciplines?

… (eight example research questions provided)

Research Priority 2: What conditions support or impede students’ cognitive and/or affective outcomes development?

o Under what conditions (e.g. class size, student demographic) is the curriculum most effective and why?

o What are good methods for evaluating the effect of a curriculum on student outcomes, or comparing curricula?

o What is the impact of implementation fidelity on the effect of a curriculum? What are good methods for determining this impact?

o What are good methods (e.g. teaching experiments) for developing high quality, research-based curricula?

o Which enactments of the curriculum produce better long-term results (e.g. higher attainment in future courses or higher self-efficacy)?

If we knew the answers to these questions then:

o Instructors would know the impact of different components and sequencing on long-term retention for students of different levels and backgrounds and from different client disciplines, helping them to make decisions in the intended curriculum.

o Instructors would be able to make informed decisions in choosing curricula and textbooks for their courses from the available set of written curricula.

o Instructors would have a guide to the best way to change the written college level curricula over time to best incorporate changes in K-12 curricula and plan for changes in the field and client disciplines.

o Instructors would know ideal options for including probability within statistics in the intended and enacted curriculum. For instance, this might include new ways of teaching probability such as through simulation rather than relying upon formal rules/formulas.

… 10 implications provide

Measurement/Assessment needs

o Written curriculumo Intended curriculumo Enacted curriculumo Achievement of student learning objectives

Cognitive outcomes ◦ (2 priorities; 8 research questions; and 3 implications)

Affective constructs ◦ (4 priorities; 11 research questions; and 4 implications)

Curriculum◦ (2 priorities; 13 research questions; and 10 implications)

Teaching Practice ◦ (2 priorities; 9 research questions; and 6 implications)

Teacher Development ◦ (3 priorities; 11 research questions; and 2 implications)

Technology ◦ (6 priorities; 39 research questions; and 12 implications)

Build a virtual community for statistics education graduate programs

Create shared virtual course(s) ASA/NCTM Joint Committee charged with

recommendations for training K-12 teachers Develop guides and assistance for a

research practicum Create an international young scholars

visitor program