In Unit 2, we learn about five basic steps in a supervised machine learning process in addition to some other components of a learning analytics workflow. For example, to help prepare for analysis, we'll first take a step back and think about how we want to use machine learning, and predicting is a key word. Many scholars have focused on predicting students who are at-risk: of dropping a course or not succeeding in it. In this introductory machine learning case study, we will cover the following workflow processes from @krumm2018 as we attempt to develop our own model for predicting student drop-out

For Unit 1, we will focus on online science classes provided through a state-wide online virtual school and conduct an analysis that help predict students' performance in these online courses. This case study is guided by a foundational study in Learning Analytics that illustrates how analyses like these can be used develop an early warning system for educators to identify students at risk of failing and intervene before that happens.

Our primary aim for this case study is to gain some hands-on experience with essential R packages and functions for SNA. We learn how to preparing network data for analysis and creating a simple network sociogram to help describe visually what our network “looks like.” Specifically, this case study will cover the following topics pertaining to each data-intensive workflow process (Krumm, Means, and Bienkowski 2018).