Adapting Statistics Education to a Cognitively Heterogeneous Student Population

Abstract

Historically, the introductory course in statistics at the Norwegian University of Life Sciences (NMBU), has taken a traditional, lecture-based form. A previous study at NMBU concluded that the course structure appeared to disfavor certain cognitive or personality types, extraverts in particular. Therefore, in 2016, as an experiment, the course was restructured into a student active learning course following a flipped classroom approach. At the same time, students were encouraged to do an online screening test for cognitive preferences, personality, work interest and preferred learning style. The main outcome in the present study was exam scores.Despite the new course structure, we still found significant differences in exam scores between students with a contextual preference, compared with a digital preference, and those with a feelingbased rather than a thinking-based personality characteristic. However, in contrast to the previous study, no significant difference in exam scores was found between the extraverts and the introverts, also after adjusting for other covariates. In the present article we outline these results and other findings that indicate that additional adaptations should be made in the course, in order to reach an even wider group of the heterogeneous student mass, helping individuals to better reach their learning potential.

Facilitation of Statistical Courses for the Growing and Heterogeneous Student Population

“…Project-based teaching was introduced primarily as part of a larger pedagogical project on how to adapt statistics training to the larger and more heterogenous student population that enrolls in our university today (Saebø et al, 2015;Vinje et al, 2021). At the same time, such a way of working is useful and relevant to how students might encounter statistics throughout their degree programs and in their future professions.…”

“…To assess the cognitive diversity of the student population, all students were first offered a short version of a STEM-education profiler, the Education Test (Brovold, 2020). It is a self-assessment questionnaire containing four sections: (a) a short personality trait profiler section based on the dichotomy types of Jung (1921) and Lowen (1982) (see, e.g., Vinje et al, 2021 for a closer description of the traits); (b) a section assessing work interests; (c) a section assessing preferred learning instruction methods; and (d) a section assessing interest level in six areas of STEM education. The test output gives an indication of how a student learns best.…”

“…Saebø et al (2015) showed that a traditional lecture style may (dis)favor certain cognitive types. Vinje et al (2021) described how changing from a traditional lecture based introductory statistics course to a flipped classroom introductory course promoting more active cooperative learning turned out to be more favorable for extraverted students. Although the course was taught in a student-active manner, the course structure was admittedly quite inflexible, with weekly assignments and a compulsory attendance requirement.…”

Integrating computational thinking into a longitudinal data analysis course for public health students

“…To achieve that goal, the instructor must cope effectively with diverse student backgrounds by decoupling conceptual knowledge from higher mathematics. This has been a dominant theme in research on the teaching of introductory statistics courses [39], but the problem is accentuated in a follow-on course by the considerably more challenging subject matter, as exemplified by Cai & Wang [5] and by Zheng [48]. The following is a representative cross section of students who participated in my past longitudinal data analysis classes, presented here to help the reader appreciate the rationale behind the proposed pedagogy.…”

How to Calculate Statistics for Significant Difference Test Using SPSS: Understanding Students Comprehension on the Concept of Steam Engines as Power Plant

“…Different test statistics have wide applications in various scientific disciplines, from social sciences, and economics, to health sciences and natural sciences (Mishra et al, 2019). Research and statistical analysis of difference tests can provide valuable insight into the phenomena we observe, such as the effect of medical intervention on a group of patients, a comparison of educational outcomes between two schools, or the impact of a marketing strategy on product sales (Vinje et al, 2021).…”