NPTEL : NOC:Learning Analytics Tools (Computer Science and Engineering)

Co-ordinators : Prof. Ramkumar Rajendran


Lecture 1 - Intro to Data Analytics. What is Learning Analytics?

Lecture 2 - Academic Analytics, and Educational Data Mining

Lecture 3 - Four Levels of Analytics

Lecture 4 - Four Levels of Learning Analytics Overview - II

Lecture 5 - Data Collection from Different learning environment

Lecture 6 - Data collection in TELE

Lecture 7 - Data Preprocessing

Lecture 8 - Ethics in Learning Analytics, Student Privacy

Lecture 9 - Demo of Weka

Lecture 10 - Introduction to Machine Learning - Part 1

Lecture 11 - Introduction to Machine Learning - Part 2

Lecture 12 - Training and testing data

Lecture 13 - Performance Metrics - I

Lecture 14 - Performance Metrics - II

Lecture 15 - Performance Metrics - III

Lecture 16 - Demo of Orange

Lecture 17 - Descriptive Analytics - I

Lecture 18 - Descriptive Analytics - II

Lecture 19 - Charts - I

Lecture 20 - Charts - II

Lecture 21 - Charts - III

Lecture 22 - Comparing Charts

Lecture 23 - Descriptive Analytics – Example I

Lecture 24 - Descriptive Analytics – Example II

Lecture 25 - Excel tool

Lecture 26 - Diagnostics Analytics

Lecture 27 - Correlation

Lecture 28 - Correlation Matrix

Lecture 29 - Spearman’s Rank Correlation

Lecture 30 - Data Mining

Lecture 31 - iSAT

Lecture 32 - Diagnostic Analytics - SPM

Lecture 33 - Sequential pattern mining (SPM-II)

Lecture 34 - Differential Sequence Mining (DSM)

Lecture 35 - Process Mining

Lecture 36 - Diagnostic Analytics - Clustering

Lecture 37 - K-means Clustering

Lecture 38 - Hierarchical Clustering

Lecture 39 - Clustering - Examples

Lecture 40 - Predictive Analytics

Lecture 41 - Linear Regression

Lecture 42 - Multiple Regression

Lecture 43 - Logistic Regression

Lecture 44 - Linear Regression - Example

Lecture 45 - Predictive Analytics - II

Lecture 46 - Naive Bayes Classifier

Lecture 47 - Decision Tree

Lecture 48 - Decision Tree Classifier

Lecture 49 - DT, NB - Examples

Lecture 50 - Text Analytics

Lecture 51 - Introduction to NLP

Lecture 52 - NLP-II

Lecture 53 - NLP-Tools

Lecture 54 - NLP-Examples

Lecture 55 - Intro Multimodal Learning Analytics

Lecture 56 - Affective Computing - 1

Lecture 57 - Affective Computing - 2

Lecture 58 - Eye Tracking

Lecture 59 - Revision of Learning Analytics tools course

Lecture 60 - Source of Data collection and Research Community

Lecture 61 - Machine Learning tools used in industry