Reading Versus Doing: Methods of Teaching Problem-Solving in Introductory Statistics

Abstract

Practice problems and worked examples are both well-established teaching techniques. Research in math and physics suggests that having students study worked examples during their first contact with new material, instead of solving practice problems, can be beneficial to their subsequent performance, possibly due to the reduced cognitive load required to study examples compared to generating solutions. However, there is minimal research directly comparing these teaching methods in introductory statistics. In this study, we chose six pairs of introductory statistics topics of approximately equal difficulty from throughout the semester. After an initial mini-lecture, one topic from each pair was taught using practice problems; the other was taught by having students read worked examples. Using Bayesian and frequentist analyses, we find that student performance is better after reading worked examples. This may be due to worked examples slowing the process of forgetting. Surprisingly, there is also strong evidence from in-class surveys that students experience greater frustration when reading worked examples. This could indicate that frustration is not an effective proxy for cognitive load. Alternatively, it could indicate that classroom supports during in-class problem-solving were effective in reducing the cognitive load of practice problems below that of interpreting written statistical explanations.

Learning from errors? The impact of erroneous example elaboration on learning outcomes of medical statistics in Chinese medical students

“…Cognitive psychology provides evidence that using worked-out examples and delaying actual problem-solving practice benefits novice learners [7]. For instance, in an introductory statistics class, students who studied worked-out problems demonstrated better academic performance on different statistical concepts [8]. Additionally, learners' active self-explanations of worked-out examples lead not only to enhanced near transfer but also to better far transfer [9].…”

Educación Estadística: tendencias para su enseñanza y aprendizaje en educación secundaria y terciaria

“…Asimismo, las metodologías de investigación se clasificaron de acuerdo con el tipo de análisis de la información realizado en cada documento. Una parte de los documentos utilizó, como medio para apoyar sus conclusiones, técnicas de análisis cuantitativo, como regresión lineal múltiple, ANOVAS, modelos jerárquicos, validación de escalas o análisis descriptivos (Brisbin y Maranhao, 2019;Huey et al, 2018;Kulkarni et al, 2019;Nielsen y Kreiner, 2018); o bien técnicas cualitativas, como el estudio de casos, la codificación axial, análisis de discurso y entrevistas a profundidad (Moreira y Samá, 2014;Sánchez y Gómez, 2015;Strayer et al, 2019). Sin embargo, una considerable cantidad de artículos ejecutan propuestas didácticas mediante actividades de aula, o bien, plantean propuestas para ser adaptadas a diferentes contextos, en las cuales destacan: aprendizaje basado en proyectos y desarrollo de aplicaciones para comprender conceptos estadísticos (Brown, 2019;Liu, 2017;Rodríguez y Sánchez, 2016;Ruggieri, 2019).…”

Conceptualizing a Framework for Analysing College Statistics Textbooks in Terms of Text Accessibility

“…Another potential teaching approach in college statistics course is reading, given that learning statistics through reading is associated with better performance when proper reading materials and supports are provided (Brisbin & Maranhao do Nascimento, 2019). Hence, the components of the accessibility framework could be taken as the basis for designing proper reading materials when reading is applied as a teaching pedagogy.…”