Image Credit: A flickr photo by pshab

AIMS Project

The NSF-funded project Adapting and Implementing Innovative Material in Statistics (AIMS) developed lesson plans and activities based on innovative materials that have been produced for introductory statistics courses.

The AIMS materials are available on Github at https://github.com/zief0002/aims

The case for substantial change in statistics instruction is built on strong synergies between content, pedagogy, and technology.

David Moore

Initially written in 2005–2006, the AIMS lesson plans and student activity guides were developed to help transform an introductory statistics course into one that is aligned with the Guidelines for Assessment and Instruction in Statistics Education (GAISE) for teaching introductory statistics courses. The lessons, which build on implications from educational research, involve students in small and large group discussion, computer explorations, and hands-on activities. The lessons are described in full detail along with the research foundations for the lessons, in Garfield and Ben-Zvi’s book Developing Students’ Statistical Reasoning: Connecting Research and Teaching Practice.

AIMS & GAISE

The AIMS materials have been aligned with the 2005 Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report. Each of the six recommendations from this report is listed below along with an indication of how the AIMS materials have been aligned with it.

  • Emphasize statistical literacy and develop statistical thinking: The AIMS materials help build students’ statistical literacy by having them read their text and complete study questions, and respond to literacy oriented assessment items.
  • Use real data: The AIMS materials use data gathered from students (student survey data, body data) and analyze other real data sets of interest (e.g., college admissions data)
  • Stress conceptual understanding rather than mere knowledge of procedures: The AIMS materials focus on the nine big ideas (data, model, distribution, center, variability, comparing groups, samples, inference, and covariation), and develop them throughout the course. Students first encounter these ideas informally, and then as they revisit them, the materials aid in moving them to more formal understanding and reasoning.
  • Foster active learning in the classroom: The AIMS lesson plans suggest how teachers may guide students through activities where they are actively engaged in making and testing conjectures, working in small groups, explaining their reasoning, and learning together. The materials also incorporate versions of many innovative student activities.
  • Use technology for developing conceptual understanding and analyzing data: The AIMS materials use Fathom™ for data analysis and exploration, Tinkerplots™ to help students understand and reason about graphs, Sampling SIM to simulate data to make informal inferences, and several applets to illustrate abstract concepts.
  • Use assessments to improve and evaluate student learning: Three free resources can be used to assess students’ statistical reasoning: The ARTIST website, the ARTIST Item Database, and the ARTIST Topic Tests and CAOS tests. These are all located at the ARTIST website.


People

There were many people involved in the AIMS Project. Many of them are listed below.

AIMS Principal Investigators

  • Joan Garfield (University of Minnesota)
  • Robert delMas (University of Minnesota)
  • Andrew Zieffler (University of Minnesota)

AIMS Contributing Author

  • Dani Ben-Zvi (University of Haifa)

AIMS Staff

  • Michelle Everson (University of Minnesota)
  • Jared Dixon (University of Minnesota, Graduate Research Assistant)
  • Beng Chang (University of Minnesota, Graduate Research Assistant)

AIMS Evaluator

  • Robert Gould (University of California, Los Angeles)

AIMS Advisory Board

  • Beth Chance (California Polytechnic State University)
  • George Cobb (Mount Holyoke College)
  • Bill Finzer (KCP Technologies)
  • Cliff Konold (University of Massachusetts Amherst)
  • Robin Lock (St. Lawrence University)
  • Dennis Pearl (The Ohio State University)
  • Allan Rossman (California Polytechnic State University)
  • Richard Scheaffer (University of Florida)