A Network Science Perspective to Personalized Learning

TL;DR

This paper proposes a personalized learning framework using network science to tailor educational content and engagement based on individual skills and goals, supported by a prototype platform called CHUNK Learning.

Contribution

It introduces a novel network science-based approach to personalized education focusing on learning experiences and presents a prototype platform implementing this framework.

Findings

Framework emphasizes learning experiences over teaching experiences

Supports self-paced learning with content choices and multiple modalities

Prototype platform CHUNK Learning demonstrates practical implementation

Abstract

The modern educational ecosystem is not one-size fits all. Scholars are accustomed to personalization in their everyday life and expect the same from education systems. Additionally, the COVID-19 pandemic placed us all in an acute teaching and learning laboratory experimentation which now creates expectations of self-paced learning and interactions with focused educational materials. Consequently, we examine how learning objectives can be achieved through a learning platform that offers content choices and multiple modalities of engagement to support self-paced learning, and propose an approach to personalized education based on network science. This framework brings the attention to learning experiences, rather than teaching experiences, by providing the learner engagement and content choices supported by a network of knowledge, based on and driven by individual skills and goals. We conclude with a discussion of a prototype of such a learning platform, called CHUNK Learning.