Evaluating the Effectiveness of a Statics Recitation Course

Brian Lani, Charlotte De Vries

Research output: Contribution to journalConference articlepeer-review


In order for a student to complete an engineering program, they must first be able to successfully build their fundamental skills during their introductory engineering courses. Those students who struggle may not be able to graduate on time and as a result these courses end up impacting retention. This paper describes the development and effectiveness of a Statics recitation course designed to improve the passing rate in a fundamental class. Learning data such as grades and self-reported information from surveys were analyzed through binomial logistical regression analysis to determine their ability to predict student success in Statics. The goal was to develop a method of identifying students who would be at risk of failing the course based on historically predictive indicators of student learning and invite “at-risk” students to join the recitation course early in the semester. The impact of recitation was then determined by comparing the passing rate of at-risk students who registered for recitation with those who did not take the recitation course. Early results showed midterm 1 exam scores were the best predictor for student success and those who scored below 70% would be defined as at-risk. The resulting data shows passing rates of these students were higher when they were enrolled in the recitation course. Binomial logistical regression supports the idea that recitation plays a role in predicting student success. This paper discusses the motivation for intervening with Statics, the recitation course pedagogy, the statistical methods used to predict student performance, and the effectiveness of recitation.

Original languageEnglish (US)
JournalASEE Annual Conference and Exposition, Conference Proceedings
StatePublished - Aug 23 2022
Event129th ASEE Annual Conference and Exposition: Excellence Through Diversity, ASEE 2022 - Minneapolis, United States
Duration: Jun 26 2022Jun 29 2022

All Science Journal Classification (ASJC) codes

  • General Engineering


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