With Samantha Robinson (University of Arkansas, Fayetteville)
Increasing student success rates, student learning outcomes, and student perceptions of statistics courses while simultaneously expanding content coverage and incorporating technology for data-based projects appears to be the direct result of an implementation of purposeful content resequencing in a lower-division (i.e. 200 level), non-calculus based, undergraduate, service course on introductory statistics at a major state university where between 600 and 800 students are taught in 16 to 20 sections (both face-to-face and online) by a coordinated team of approximately ten instructors and senior graduate teaching assistants each semester.
Purposeful content resequencing involves sequencing topics to facilitate the early introduction and constant revisiting of concepts which the target group of students at your specific university have struggled with historically. In addition, coordination with other academic programs is necessary to create an introductory course that benefits non-STEM programs while also communicating the need for statistical rigor to both students and programs. While our revised content sequence is similar to the Winona State University model (Malone, Gabrosek, Curtiss & Race, 2010), it also differs slightly as it was devised analyzing past student data on course retention, examination items, and student evaluations at our university. Using the historic data from our own students, the desired outcomes from other programs, the goals of our own department, and some knowledge of the agreements in the topic sequencing debate (Chance & Rossman, 2001), a backward design with a mixture of learning-related and concept-related sequencing schemes was applied. Once a sequence of topics was determined, course materials including notes, homeworks, quizzes, in-class activities, data-based projects, and common examinations were created. All instructors were provided with these materials and participated in pre-semester training and weekly supervision with a course coordinator. In addition, these instructors were observed and were provided feedback by the course coordinator each semester. While the training and supervision models utilized during implementation were not new and had been commonplace prior to resequencing, the smooth implementation of the resequenced course was due in part to these models already being in place.
This quasi-experimental, pre-post, mixed methods study with non-equivalent comparison groups explores the potential for purposeful resequencing of course content to impact student success rates, student learning outcomes, and student perceptions of the course. Using both quantitative and qualitative analytical techniques, it appears that the implementation of purposeful resequencing not only expands content coverage but also increases student success rates, student learning, and student perceptions of the course. These findings point to purposeful resequencing as one possible curriculum design that might improve student outcomes and further the continued investigation of content sequencing in statistics and related disciplines.