Date of Award
Level of Access Assigned by Author
Master of Science (MS)
Second Committee Member
Third Committee Member
This study seeks to analyze the effects of targeted interventions on students with varying statistics background knowledge. These interventions include remediating assessment homework assignments, and Maine Learning Assistant (MLA) led statistics boot camps. These interventions were completed in an undergraduate Lean Six Sigma course, where students initially had a wide variety of prior statistics experience. This large dispersion of background knowledge levels is paralleled in many STEM entry-level courses. Data collected about student participation in these interventions and their later success on exams in the course were analyzed using General Linear Model protocol to determine if any intervention created a statistically significant change in student success measures. Several models were run, each concentrating on a particular statistics background knowledge concept addressed in the boot camps and essential for course success. Examination success rates were found to have increased significantly from the cohort without these interventions (2016) to the cohort with these interventions (2018). This improvement was maintained with the second cohort with the interventions in 2019. The statistics backgrounds of the 2016, 2018 and 2019 cohorts were not found to be significantly different from each other after analyzing their reported backgrounds and major demographic. However, no strong singular effect was found on student success through General Linear Model Analysis. Further data collection of student participation and success measures is encouraged in subsequent course offerings, to enhance the chance of detecting subtler intervention effects on student success. Qualitative data from student and MLA interviews may also be beneficial to see how perceptions of statistics and teaching are influenced by the interventions.
Willis, Justin C., "Use of Statistics Boot Camps to Encourage Success of Students with Diverse Background Knowledge" (2020). Electronic Theses and Dissertations. 3340.