In this NSF-funded research project, we developed an electronic assessment model with vocabulary, clicker, and homework assessment questions for a broad range of undergraduate introductory statistics courses. Along with the assessment questions, we developed a report software package (ePort) incorporating the current state of data visualization research to automatically generate reports for students, course instructors, and supervisors, enabling rapid feedback on learning and instruction to all parties. This project extends previous work on course level student learning outcomes (GAISE College Report) to the topic level and develops formative assessments to measure these outcomes. These assessments can be used to find the relative difficulties students have in learning course outcomes, to obtain the most common patterns exhibited by students in their understanding of course learning outcomes, and to identify the most common problems students have in learning particular outcomes.
Our electronic assessment model divides the material in the introductory statistics course into 27 different topic areas covering content in descriptive statistics, data collection, probability and statistical inference. Coverage of each topic is self-contained, so instructors can choose only the topics covered in their specific course in any order. Topics are not structured around any textbook, but include a broad range of topics covered in many different general introductory statistics courses.
For each topic, we wrote a set of student learning outcomes - statements describing what we want students to know or to be able to do after learning the topic. There are 218 learning outcomes across our 27 topics. These learning outcomes form the structure of our electronic assessment model. Student understanding of the learning outcomes is assessed through the use of the online homework question database and the clicker question database.Learn more
To assess student understanding of learning outcomes, we wrote online homework assignments for each of our 27 topics. Questions are coded by topic, learning outcome and question set and include multiple choice, matching, true/false, fill in the blank, and drop down menu question formats. The complete database consists of over 1200 questions across the 27 topics and is currently available in two formats: MS Word and Respondus archives for upload to the CMS Blackboard.Learn more
To assess student understanding of learning outcomes, we wrote clicker questions for each of our 27 topics. Clickers, or personal response systems, can be used during class to quickly assess student learning as it occurs, providing immediate feedback to both students and instructors. The complete database consists of 400 questions across the 27 topics and is currently available in PowerPoint slide format with Turning Point 5.0 software.Learn more
For each topic, we identified a set of vocabulary words – terminology necessary for students to become statistically literate and to learn concepts in introductory statistics. There are 369 vocabulary words across our 27 topics. Student understanding of vocabulary is assessed through the use of the vocabulary question database.Learn more
To assess student understanding of the vocabulary words, we wrote short vocabulary quizzes for each topic, ranging from 3 to 13 questions each. The database includes a total of 195 questions with multiple choice, true/false, matching, and drop down menu question formats. The complete question database is currently available in two formats: MS Word files and Respondus archives for upload to the CMS Blackboard.Learn more
The electronic format of our online homework and vocabulary questions gives instructors and course supervisors access to student responses across topics, learning outcomes, question sets, and questions. To analyze these responses, we developed an R package called ePort. ePort takes student responses downloaded from Blackboard and compiles them into reports. Six levels of reports are available: short and long reports for each course section for a single topic, short and long reports across multiple course sections for a single topic, and reports across multiple topics for a single course section and for multiple course sections. Analysis of student performance through these reports allows instructors and course supervisors to flag struggling students and to flag topics, learning outcomes, question sets, and questions with low performance.Learn more
To request a copy of the materials described here, please complete our request form. You may also choose to become a project collaborator, which enables you to contribute to the further development of the project materials through a private GitHub repository. For instructions on how to set up a GitHub account, click here.