Week 3: Tools & Methods

Week 3: Tools and methods of learning analytics

Introduction:

During week 1 & 2, we spent time defining LA and looking at examples of how they have been used in different settings to improve retention and student learning. This week is a practical week - we finally get a chance to dive into tools. The range of LA tools is significant and growing almost daily. The readings this week focus on two specific areas of analytics: technical (algorithmic, models) and applications.

Readings:

Baker & Yacef list five primary areas Links to an external site. (pdf) of Educational Data Mining:

• Prediction
• Clustering
• Relationship mining
• Distillation of data for human judgment
• Discovery with models

 Bienkowski, Feng, and Means offer five areas of LA/EDM application Links to an external site. (.pdf):

• Modeling user knowledge, behavior, and experience
• Creating profiles of users
• Modeling knowledge domains
• Trend analysis
• Personalization and adaptation

From these two models of analytics tools, we can see  technical and application (see image 1 below) as well as the roots or historical influences of those approaches.

Image 1: LA Techniques and Application

These five elements of LA (user modeling, relationship mining, knowledge domain modeling, trend analysis & prediction, and personalization and adaptation) are summarized with possible tools/approaches:

Learning Activities:

The learning activities this week include:

1. Get started with R, Tableau or Evidence Hub:  Analytics Project

2. Participate in the Week 3 Discussion Forum

3. Attend weekly lectures (Chuck Severance and Ryan S.J.d. Baker). See schedule and access info here: Live Sessions & Guest Speakers