BackgroundLarge introductory STEM courses historically have high failure rates, and failing such courses often leads students to change majors or even drop out of college. Instructional innovations such as the Learning Assistant model can influence this trend by changing institutional norms. In collaboration with faculty who teach large-enrollment introductory STEM courses, undergraduate learning assistants (LAs) use research-based instructional strategies designed to encourage active student engagement and elicit student thinking. These instructional innovations help students master the types of skills necessary for college success such as critical thinking and defending ideas. In this study, we use logistic regression with pre-existing institutional data to investigate the relationship between exposure to LA support in large introductory STEM courses and general failure rates in these same and other introductory courses at University of Colorado Boulder.ResultsOur results indicate that exposure to LA support in any STEM gateway course is associated with a 63% reduction in odds of failure for males and a 55% reduction in odds of failure for females in subsequent STEM gateway courses.ConclusionsThe LA program appears related to lower course failure rates in introductory STEM courses, but each department involved in this study implements the LA program in different ways. We hypothesize that these differences may influence student experiences in ways that are not apparent in the current analysis, but more work is necessary to support this hypothesis. Despite this potential limitation, we see that the LA program is consistently associated with lower failure rates in introductory STEM courses. These results extend the research base regarding the relationship between the LA program and positive student outcomes.

No abstract

Large introductory science, technology, engineering, and mathematics (STEM) courses historically have high failure rates, and failing such courses often leads students to change majors or even drop out of college. Institutional change models such as the Learning Assistant (LA) model can influence this trend by changing institutional norms. In collaboration with faculty who teach large-enrollment introductory courses, undergraduate learning assistants (LAs) use research-based instructional strategies designed to encourage active student engagement and elicit student thinking. In this study, we use logistic regression to investigate the relationship between exposure to LA support in these large introductory courses generally and failure rates in Physics I and II specifically at University of Colorado Boulder. We find that exposure to LA support is associated with lower failure rates in introductory physics courses and that the magnitude of the relationship is larger for female and first-generation college students.

Observation protocol scores are commonly used as status measures to support inferences about teacher practices. When multiple observations are collected for the same teacher over the course of a year, some portion of a teacher’s score on each occasion may be attributable to the rater, lesson, and the time of year of the observation. All three of these are facets that can threaten the generalizability of teacher scores, but the role of time is easiest to overlook. A generalizability theory framework is used in this study to illustrate the concept of a hidden facet of measurement. When there are many temporally spaced observation occasions, it may be possible to support inferences about the growth in teaching practices over time as an alternative (or complement) to making inferences about status at a single point in time. This study uses longitudinal observation scores from the Measures of Effective Teaching project to estimate the reliability of teacher-level growth parameters for designs that vary in the number and spacing of observation occasions over a 2-year span. On the basis of a subsample of teachers scored using the Danielson Framework for Teaching, we show that at least eight observations over 2 years are needed before it would be possible to make distinctions in growth with a reliability coefficients of .39.