Lifelong learning supports social participation, cognitive health, and well being in later life. Online learning platforms often celebrate lifelong learning, yet older learners rarely appear as a focus in learning analytics work. This project uses an EdX course catalog snapshot to study how often courses explicitly reference aging, older adults, or later life. The analysis addresses three research questions.

Data sources such as digital learning environments and administrative data systems, as well as data produced by social media websites and the mass digitization of academic and practitioner publications, hold enormous potential to address a range of pressing problems in education, but collecting and analyzing text-based data also presents unique challenges. This week, our case study is guided by Josh Rosenberg's study, Advancing new methods for understanding public sentiment about educational reforms: The case of Twitter and the Next Generation Science Standards. We will focus on conducting a very simplistic "replication study" by comparing the sentiment of tweets about the Next Generation Science Standards (NGSS) and Common Core State Standards (CCSS) in order to better understand public reaction to these two curriculum reform efforts. Specifically, our Unit 3 case study will cover the following topics

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).