Who Should Take This Course?The Teaching Statistics Through Inferential Reasoning MOOC-Ed is applicable to anyone interested in strengthening their approaches to teaching statistics through data investigations. The statistical content and strategies are appropriate for implementation with middle school through early college learners. Thus, teachers of statistics in grades 6–12 and in post-secondary contexts are the primary audience. This course may also be of interest to elementary teachers, teacher educators, and teachers of other disciplines that use data-based explorations extensively to make claims and inferences (e.g., science, social science). There is no cost for participating in the Teaching Statistics Through Inferential Reasoning MOOC-Ed.
- Strengthen your understanding of how to engage students in a statistical investigation process for the purpose of making inferences or claims;
- Explore a framework for guiding your teaching of statistical investigations to promote inferential reasoning for your students;
- Use rich data sources and dynamic graphing tools to support data exploration for investigative questions that give students opportunities to make inferences about contexts and issues of interest them;
- Examine the ways students reason with data to make inferences or claims;
- Apply a framework for inferential reasoning to your educational practices;
- Collaborate with colleagues near and far to gain different perspectives and to build a library of teaching resources.
Unit 0: Orientation and Review of SASI Framework
In Orientation, you are introduced to the course and colleagues, and can review essential background material related to a framework for supporting Students’ Approaches to Statistical Investigations (SASI). This material is part of the Teaching Statistics Through Data Investigations course. If you need a review, or if you have not yet had the opportunity, please engage with these materials before you work on Unit 1.
Unit 1: What is Inferential Reasoning?
In this unit, you learn core aspects of inferential reasoning, why it is important in statistics, and how it develops, from informal approaches with early learners to more formal approaches as learners get more sophisticated, as described in the SASI framework. In the Posing Questions phase of an investigation, you will learn three general types of questions that can provide opportunities for students to build inferential reasoning skills. Each type of question will be the focus of the next three units.
Unit 2: Inferential Reasoning with Comparing Groups
In this unit, we take a deep dive into questions that provide opportunities for learners to compare two or more groups. When learners have a need to find similarities or difference among distributions, their understanding of key characteristics of distributions becomes an essential aspect of making comparative statements and generalizing beyond the data at hand. You will get to experience investigating a comparing groups question, see samples of students work, and consider other tasks for their potential to promote inferential reasoning.
Unit 3: Inferential Reasoning Between Samples and Population
Generalizing from a sample to a population is often considered the quintessential way to make inferences in statistics. In Unit 3, we consider questions that engage learners in considering what is likely true about a population. You will get to experience a task that includes reasoning about a sample to make claims about a population, view students’ work, and consider different ways to support inferential reasoning.
Unit 4: Inferential Reasoning with Competing Models
Unit 4 focuses on how learners can engage with questions that focus on making decisions about which model is the most plausible for describing a population. Within tasks that engage learners to compare competing models, you will consider how learners can use different approaches and levels of sophistication for supporting claims.
Unit 5: Making Inferential Reasoning Essential in Your Practice
This unit will assist you in making plans to change teaching practices that can really engage students in inferential reasoning. You will reflect on, assess, and share what you have learned throughout the course.
A certificate of completion for 20 hours of professional development will be provided to participants who do the following in each unit:
- Access and engage with all materials on the Engage with Essentials page.
- Complete the Explore and Discuss; this includes engaging in activities and participating in the discussion forum.
- Post at least one discussion or comment in the Discuss with Colleagues.
- Complete the End-of-Course Survey.
You can submit the certificate to your local agency with a request for Continuing Education Units (CEUs). Granting of CEUs will be subject to the policies and procedures of your state and local agency.
There are also opportunities to participate in performance assessments to demonstrate your competency with ideas presented in the course and apply them to your educational practices. These performance assessments, called micro-credentials, can allow you to earn additional professional development hours. Our Teaching Statistics micro-credentials are portable and stackable. Once you demonstrate a competency and earn a micro-credential, you will receive a certificate and a virtual badge recognizing your accomplishment. We have created two stacks of micro-credentials that are purposefully stacked to help support you as you deepen your knowledge and competence in specific areas of teaching statistics. Each micro-credential can help you earn 5–7.5 professional development hours. Note that you can earn hours by successfully completing any of the six Teaching Statistics micro-credentials, even if you choose to not complete the requirements for the 20-hour certificate.
- Self-directed learning, through personalizing your experience by identifying your own goals, selecting among a rich array of resources, and deciding whether, when, and how to engage in discussions and activities to further your own learning and meet your goals.
- Peer-supported learning, through engaging in online discussions, reviewing your colleagues' projects, rating posted ideas, recommending resources, crowdsourcing lessons learned, and participating in twitter chats and other exchanges appropriate to the individual course.
- Job-embedded learning, through the use of case studies, classroom and school related projects; developing action plans; and other activities that center your work on critical problems of practice and data-informed decision-making in your own classrooms, schools or districts.
- Multiple voices, through learning about the perspectives of other teachers and administrators along with those of students, researchers and experts in the field. Our courses are purposefully not designed around one or two experts who present online lectures. They provide exposure to a rich set of perspectives presented within the context of course elements that reflect these core principles.
You will see these design principles implemented in our courses through the following instructional elements:
- Conceptual Frameworks
- Resource Collections
- Asynchronous Discussions and Twitter Chats
- Student Scenarios
- Expert Panels
- Participant Projects and Peer Feedback
- Professional Learning Community (PLC) Guides
Grade 6-12 Teachers
Hollylynne Lee, Ph.D.
Gemma Mojica, Ph.D.
|Certificate Requirements Due||
Teaching Statistics Through Data Investigations
Micro-credentials (Learn More)
|Teaching Statistics: Understanding the SASI Framework|
|Teaching Statistics: Statistics Task Design|