Webinars

Abstract: A homework/e-textbook prototype (lecturebook.com) is being used in a course with >1,500 students. This prototype makes the e-textbook a supplement to the homework. Results show an increase in average grades and an increase of buy-in of the e-textbook option as students appreciate the integration of textbook with tailored homework questions. Description: Students are accustomed to accessing information immediately. So we develop ways to enhance the teaching and incorporate technological methods into all aspects of the students' learning environment. This presentation will share a new online tool (www.lecturebook.com, a new component of www.lecturetools.com), that facilitates creation and grading of homework linked to an electronic version of the course textbook. The idea is to make the e-textbook a supplement to the homework questions. This homework/e-textbook prototype has been used in an introductory statistics course with semester enrollments of over 1500 students since the Fall of 2010. A bank of customized questions has been created and linked directly to e-textbook content. The solutions can be enhanced by the instructor to go beyond just providing the correct answer. Problems are selected and assigned weekly to match content presented in lectures and lab. Students work through the weekly homework online, with direct links to the e-textbook material if questions or a review is needed. The submission of the paperless homework is automatic and set for one common time for all students (no more 'I lost my homework' or 'I forgot to turn in my homework'). Grading is completed online with the ability to provide tailored feedback quickly. Students receive the solutions immediately after submission and their scores with tailored feedback a few days later. Students have all homework assignments with their answers and feedback in one place for future reference. We have seen an increase in average grades and an increase of the buy-in of the e-textbook option as students appreciate the integration of textbook with tailored homework questions. Future plans include embedding mini video hints, tagged to specific homework questions. This tool allows students to build connections between the material they encounter to see the bigger picture. This session will demonstrate how homework assignments are set up, submitted, and graded when using the Lecturebook tool. There will also be some sharing of feedback from students and GSIs who have used this tool.

The Journal of Statistics Education (JSE) is a leading journal for the dissemination of knowledge for the improvement of statistics education at all levels, including elementary, secondary, post-secondary, post-graduate, continuing, and workplace education. Current JSE Editor John Gabrosek will discuss how JSE handles submissions. Discussion will include guidelines and tips for writing papers for JSE and for reviewing papers as a JSE referee or Associate Editor.

This webinar provides an overview of the research foundations of a radically different introductory statistics course: the CATALST course. This course teaches students the skills they need in order to truly cook with statistics, not just the procedures they need in order to follow a statistical recipe. In addition to the research foundations of the course, we will describe unique aspects of this course as well as details of a one-year teaching experiment to learn how this course can be taught and its impact on student learning.

Forty-three states have signed on to the mathematics part of the Common Core State Standards (CC). Statistics and Probability play a prominent part in CC grades 6-11 for all students. How may Stats 101 have to change to accommodate potentially better prepared quantitatively literate students?

The role that data collection plays in causal inference is of fundamental importance in introductory statistics, and yet is outside the comfort zone for many of us. In this webinar, I'll discuss why causal inference is important and also fun, and give some advice for teaching this topic.

Generally in learning statistical inference, students reason backwards from data to the (usually invisible) process that produced them. I will demonstrate an alternative approach in which students begin at the process end, designing their own "data factories." Based on their output, students modify their factories such that, for example, a collection of cats produced by a cat factory has features that look more like real cats. This work is part of the NSF-funded "Model Chance" project. In this project, we have been adding probability modeling to the existing data-visualization capabilities of TinkerPlots and, using that environment, exploring how data and chance might be better integrated in our instruction beginning in the middle school.

Developmental education in America's community colleges has been a burial ground for the aspirations of our students seeking to improve their lives through education. Under the leadership for the Carnegie Foundation for the Advancement of Teaching and the Charles A. Dana Center, nineteen community colleges and systems are building accelerated pathways to and through developmental education with the goal of helping students with low levels of mathematical preparation complete a college credit bearing, transferable statistics course within one year. Uri will describe the work to date, the challenges the initiative faces, and the underlying ideas of improvement science that are driving its development.

Since formal hypothesis testing and inference methods can be a challenging topic for students to tackle, introducing informal inference early in a course is a useful way of helping students understand the concept of a null distribution and how to make decisions about whether to reject it. We will present two computer labs, both using Fathom, that illustrate these concepts using permutation in a setting where students will be answering interesting investigative questions with real data.

When instructors have their students implement "real-world" projects in statistics, a number of questions arise: Where can students locate real data to analyze? What kinds of meaningful research questions can we help students to formulate? What aspects of statistical research can be covered in a project? What are reasonable methods for evaluating the student's work? The presenters will share resources developed during an NSF-funded study to develop and test curriculum materials for student projects in statistics, using linear regression and t-test scenarios.

Some students in statistics classes exhibit behaviors that share characteristics with the established construct of learned helplessness. This webinar will discuss this phenomenon, and detail an instrument recently developed which measures this (HILS: Helplessness in Learning Statistics).