Week 1: Introduction to Learning Analytics
Instructor: George Siemens
Competency 1.2: Identify proprietary and open source tools commonly used in learning analytics
Competency 1.3: Define learning analytics and detail types of insight they can provide to educators and learners
Hangout: Week 1 Live Session
Video: Introduction to DALMOOC Topics
Video: Introduction to Learning Analytics
Video: Getting Started With Data Analytics Tools
Activity: Installing Tableau
Activity: Download Learning Analytics Tools Worksheet
Assignment: Learning Analytics: Tool Matrix
Bazaar Assignment: Discuss Week 1
WEEK 2: starting with data
Instructor: George Siemens
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
Hangout: Tony Hirst on “Data Wrangling”
Video: Week 2 Introduction
Video: The Data / Analytics Cycle
Video: Visualization & Dashboards
Video: Systems Level Considerations
Activity and Videos: Tableau Installation and Basics
Bazaar Activity: Week 2 Reflection
Activity: Experimenting With Educational Data
Hangout: Week 2 Live Session
WEEK 3: Basics of Social Network Analysis
Instructor: Dragan Gasevic
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
Video: Introduction in Social Network Analysis
Video: Network Structure and Data Sources
Activity: Download the Data Set and Familiarize With the Data
Video: Centrality Measures
Video: Modularity
Discussion Activity: Post personal goals for social network analysis unit on discussion forum and Pro Solo status wall.
Video: Gephi – A Brief Tour
Activity: Install Gephi
Activity: Gephi Tutorial Quick Start
Activity: Hands-on to Seek Relevant Resources
Activity: Hands-on to import the data set into Gephi and calculate the SNA measures
Activity: Gephi Tutorial Visualization
Activity: Hands-on to Seek Relevant Resources
Activity: Hands-on to Visualize the Network Based on the Computed Measures
Bazaar Activity: Collaborative reflection on the visualization of the networks based on the computed measures in Gephi
Assignment: Describe the results of the SNA peformed in Hands-on Sessions
Hangout: Week 3 Live Session
WEEK 4: Sensemaking of Social Network Analysis for Learning
Instructor: Dragan Gasevic
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
Video: Introduction
Video: Social Network Analysis and Learning Design
Video: Social Network Analysis and Social Presence
Video: Social Network Analysis and Sense of Community
Video: Social Network Analysis and Creativity
Video: Social Network Analysis and Academic Peformance
Video: Social Network Analysis and Learning Networks Dynamics
Bazaar Activity: Collaborative reflection on the different types of learning indicators and SNA
Discussion Activity: Reflect on SNA sensemaking options in relation to the personal goal
Assignment: Provide interpretation of social network measures in relation to a selected interpretation introduced.
Assignment: Provide interpretation of the network dynamics and the roles different network actors occupied over time.
Hangout: Week 4 Live Session
WEEK 5: Predictive Modeling
Instructor: Ryan Baker
Competency 5.1: Learn to conduct prediction modeling effectively and appropriately
Competency 5.2: Understand core uses of prediction modeling in education
Video: Regressors
Video: Classifiers (part 1)
Video: Classifiers (part 2)
Activity: RapidMiner Walkthrough
Video: Case Study in Classification
Video: Cross-Validation and Over-Fitting
Assignment: Classification
Assignment: CTAT
Hangout: Week 5 Live Session
WEEK 6: Behavior Detection and Model Assessment
Instructor: Ryan Baker
Competency 6.1: Learn how to engineer both features and training labels
Competency 6.2: Learn about key diagnostic metrics and their uses
Video: Behavior Detection Introduction
Video: Ground Truth
Video: Feature Engineering
Video: Diagnostic Metrics: Kappa and Accuracy
Video: Diagnostic Metrics: ROC and A’
Video: Diagnostic Metrics: Correlation and RMSE
Video: Knowledge Engineering and Data Mining
Video: Over Validity Considerations
Assignment: Metric Assessment
Assignment: CTAT
Hangout: Week 6 Live Session
WEEK 7: Text Mining Introduction
Instructor: Carolyn Rosé
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
Video: Text Unit Introduction
Video: Exploration of Collaborative Learning Process Analysis (research highlight)
Discussion Activity: Post Personal Goals for Text Mining Unit on Discussion Forum
Video: Text Mining Conceptual Overview of Techniques
Video: Tools and Resources
Video: LightSIDE: A Quick Tour
Activity: Hands on LightSIDE Walk Through
Assignment: Training and Evaluating a Simple Predictive Model
Video: Exploration of Student Attitudes in MOOCs (research highlight)
Bazaar Activity: Collaborative reflection on the MOOC video
Hangout: Week 7 Live Session
WEEK 8: Text Mining Nuts and Bolts
Instructor: Carolyn Rosé
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
Video: Data Preparation
Video: Getting a Sense of Your Data
Activity: Textual Data Preprocessing and Informal Analysis
Video: Exploring Basic Text Feature Extraction
Video: Interpreting Feature Weights
Video: Comparing Performance of Alternative Models
Activity: Hands on Work With Text Feature Extraction
Assignment: Compare Two Feature Spaces and Explain Performance Differences
Video: Advanced Feature Extraction
Bazaar Activity: Hands on Experimentation With Advanced Feature Extraction
Hangout: Week 8 Live Session
WEEK 9: Wrap Up
Competency 9.1: Identify and describe professional and research organizations that are prominent in developing learning analytics as a domain
Competency 9.2: Describe the role of privacy and ethics in learning analytics activity
Competency 9.3: Evaluate the impact of emerging trends, including learning profile develop, adaptive learning, and openness, on the field of learning analytics
Video: Integration of 8 Weeks
Video: Open Learning Analytics
Video: Getting Deeper: What To Do Next
Video: Introduction to LA / EDM Organizations
Video: PKG and Adaptivite Learning
Hangout: Week 8 Live Session