Learning Analytics Tools

Por: Swayam . en: , ,

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 AnalyticsWeek 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 PrivacyWeek 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 OrangeWeek 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:CorrelationWeek 6:Lecture 1:Sequential Pattern Mining Lecture 2:SPM tool Demo Lecture 3:Process Mining Lecture 4:ProM Tool DemoWeek 7:Lecture 1:Predictive Analytics Lecture 2:Modeling – Feature Selection Lecture 3:Linear Regression Lecture 4:Demo of Linear Regression using WekaWeek 8:Lecture 1:Decision Tree Lecture 2:Demo of Decision Tree using Orange Lecture 3:Naïve Bayes algorithm Lecture 4:Demo of Naïve BayesWeek 9:Lecture 1:Clustering in predictive algorithm Lecture 2:K-Means clustering Lecture 3:Demo of K-Means clusteringWeek 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 embeddingWeek 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 sensorsWeek 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