Teaching and Learning Statistics: Concept Maps and Patterns that Connect

Abstract

For many students in graduate programs of study, an introductory statistics course is often required but can bring anxiety and disconnection, as well as a perception that the discipline is abstract and challenging for students to integrate with their own academic knowledge. To address these concerns, this paper considers an approach to teaching and learning statistics, namely, collaborative concept maps. Drawing upon scholarly literature in statistical education, concept mapping, and complexity theory, this paper argues for a view of statistics classrooms as complex systems that self-organize and emerge through interaction, a diversity of ideas, and shared meaning-making, resulting in “patterns that connect.” To facilitate productive and meaningful connections, small randomized groups of students are invited to collaborate on concept maps as productive pedagogical structures to foster connections among conceptual concepts, the course text, other learning materials, and the learners themselves. To this end, this paper offers a theoretically- and conceptually-informed account of a pedagogical design that illustrates how collaborative concept mapping can function as a generative learning condition, supporting conceptual coherence, reducing conceptual disconnection, and promoting shared understanding. To end, implications for instructors interested in designing productive learning environments that emphasize connection, emergence, and shared meaning-making are offered.

Keywords:

• concept maps,
• statistics education,
• teaching and learning