Learning Analytics Tools

Week 1:

Lecture 1:Intro To Data Analytics

Lecture 2:What is LA! Definition

Lecture 3:Academic Analytics, and Educational Data Mining

Lecture 4:Four Levels of Analytics

Lecture 5:Descriptive, Diagnostic, Predictive and Prescriptive Analytics

Week 2:

Lecture 1:Data Collection from Different learning environment

Lecture 2:Technology Enhanced Learning, Classroom and MOOC environment

Lecture 3:Preprocessing Lecture 4:Ethics in Learning Analytics, Student Privacy

Week 3:

Lecture 1:Intro to Machine Learning

Lecture 2:Supervised and Unsupervised learning

Lecture 3:Regression, Clustering and Classification

Lecture 4:Metrics for ML algorithms –Recall, Precision, Accuracy, F-Score and Kappa

Lecture 5:Demo of ML algorithms using Orange

Week 4:

Lecture 1:Descriptive Analytics

Lecture 2:Data Visualization

Lecture 3:Data visualization using Excel

Lecture 4:Dashboard Analytics

Lecture 5:Dashboard of Youtube, MOOC 

Week 5:

Lecture 1:Intro to iSAT

Lecture 2:iSAT Demo with example

Lecture 3:Diagnostic Analysis

Lecture 4:Correlation

Week 6:

Lecture 1:Sequential Pattern Mining

Lecture 2:SPM tool Demo

Lecture 3:Process Mining

Lecture 4:ProM Tool Demo

Week 7:

Lecture 1:Predictive Analytics

Lecture 2:Modeling – Feature Selection

Lecture 3:Linear Regression

Lecture 4:Demo of Linear Regression using Weka

Week 8:

Lecture 1:Decision Tree

Lecture 2:Demo of Decision Tree using Orange

Lecture 3:Naïve Bayes algorithm

Lecture 4:Demo of Naïve Bayes

Week 9:

Lecture 1:Clustering in predictive algorithm

Lecture 2:K-Means clustering

Lecture 3:Demo of K-Means clustering

Week 10:

Lecture 1:Text analytics

Lecture 2:Words, Token, Stem and lemma

Lecture 3:Minimum edit distance

Lecture 4:Develop algorithm to automatically grade subjective answers

Lecture 5:Demo of Word embedding

Week 11:

Lecture 1:Intro Multimodal Learning Analytics

Lecture 2:Eye-gaze data collection

Lecture 3:Affective computing

Lecture 4:Aligning and analyzing data from Multiple sensors

Week 12:

Lecture 1:Advanced topics in LA

Lecture 2:How to apply LA in our class

Lecture 3:Data repos, Research papers to read, and where to present your work