Competencies

Global Course Competencies

Competency 0.1: Describe and navigate the distributed structure of DALMOOC, different ways to engage with peers and various technologies to manage and share personal learning.

Week 1

Competency 1.1: Identify proprietary and open source tools commonly used in learning analytics
Competency 1.2: Define learning analytics and detail types of insight they can provide to educators and learners

Week 2

Competency 2.1: Describe the learning analytics data cycle
Competency 2.2: Download, install, and conduct basic analytics using Tableau software
Competency 2.3: Evaluate the impact of policy and strategic planning on systems-level deployment of learning analytics

Week 3

Competency 3.1: Define social network analysis and its main analysis methods
Competency 3.2: Perform social network analysis and visualize analysis results in Gephi

Week 4

Competency 4.1: Describe and critically reflect on approaches to the use of social network analysis for the study of learning
Competency 4.2: Describe and interpret the results of social network analysis for the study of learning

Week 5

Competency 5.1: Learn to conduct prediction modeling effectively and appropriately
Competency 5.2: Understand core uses of prediction modeling in education

Week 6

Competency 6.1: Learn how to engineer both features and training labels
Competency 6.2: Learn about key diagnostic metrics and their uses

Week 7

Competency 7.1: Describe prominent areas of text mining
Competency 7.2: Detail subareas of text mining such as collaborative learning process analysis
Competency 7.3: Use tools such as LightSIDE in a very simple way to run a text classification experiment
Competency 7.4: Describe how models might be used in Learning Analytics research, specifically for the problem of assessing some reasons for attrition along the way in MOOCs

Week 8

Competency 8.1: Prepare data for use in LightSIDE and use LightSIDE to extract a wide range of feature types
Competency 8.2: Build and evaluate models using alternative feature spaces
Competency 8.3: Compare the performance of different models
Competency 8.4: Inspect models and interpret the weights assigned to different features as well as to reason about what these weights signify and whether they make sense
Competency 8.5: Examine texts from different categories and notice characteristics they might want to include in feature space for models and then use this reasoning to start to make tentative decisions about what kinds of features to include in their models

Week 9

Competency 9.1: Identify and describe professional and research organizations that are prominent in developing learning analytics as a domain
Competency 9.2: Integrate various course concepts through creation of a graphical representation (concept map) of the relationships between prominent course topics
Competency 9.3: Self-assess your engagement in DALMOOC and personal learning lessons in a blog post or EdX discussion forum thread