Educational Tool and Active-Learning Class Activity for Teaching Agglomerative Hierarchical Clustering

Abstract

To incorporate active learning and cooperative teamwork in statistics classroom, this article introduces a creative three-dimensional educational tool and an in-class activity designed for introducing the topic of agglomerative hierarchical clustering. The educational tool consists of a simple bulletin board and color pushpins (it can also be realized with a less expensive alternative) based on which students work collaboratively in small groups of 3-5 to complete the task of agglomerative hierarchical clustering: they start with n singleton clusters, each corresponding to a pushpin of a unique color on the board, and work step by step to merge all pushpins into one single cluster using the single linkage, complete linkage, or group average linkage criteria. We present a detailed lesson plan that accompanies the designed activity and also provide a real data example in the supplementary materials. Supplementary materials for this article are available online.

Construction of Alumni Information Analysis Model Based on Big Data

“…e improved condensed hierarchical clustering algorithm proposed in this paper is utilized to cluster text information, and the central point of each cluster is figured out. e calculation formula is shown in formula (22) [23][24][25][26][27]:…”

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.…”

“…In contrast, few comparable systematic attempts have been made to explore effective ways of teaching to the same audience follow-on biostatistics courses that focus on a particular branch of statistics, e.g., categorical data analysis, although recent years saw encouraging attempts to find innovative ways to teach to non-statistics majors advanced topics that were normally covered by follow-on statistics courses. Among such advanced topics are mixed-effects model [12], principal component analysis [11], and cluster analysis [5]. Public health students enroll in follow-on biostatistics courses either to fulfil their degree requirements or solely to increase their research competency by taking follow-on courses as electives.…”

“…However, in practice, the instructor inevitably faces challenges posed by the often far more complex subject matter. The recent work of Cai & Wang [5] illustrates the nature of such a challenge. These researchers successfully avoided formal mathematical formulation in teaching the basic principle undergirding a clustering algorithm.…”

Online Repository for Facilitating Teaching and Learning of Undergraduate Statistical Modeling Tools

“…It is also crucial to connect each concept with realworld applications through concrete yet interesting examples. Some recent pedagogical efforts on this front often involve active learning (Gelman and Nolan, 2017;Green et al, 2018;Cai and Wang, 2020) and interactive web applications (Tintle et al, 2020). Although the use of those applications saves students from undertaking the hard coding of simulations, there are only a small number of such online applications available for teaching or learning statistical modeling at the undergraduate level.…”