Course10610122465 lectures
Learning Analytics Tools
Prof. Ramkumar Rajendran · IIT Bombay
About
Learning analytics is a method to collect, measure, analysis and reporting of data about learners and their interactions with a learning environment. Learning analytics is applying analytics on educational data to infer the student learning process and to provide support. Learning analytics is important course in the data era and it will help the learner to apply analytics on data from education domain and help the students to learn.
INTENDED AUDIENCE : Any interested learners
Lectures
Week 1
- 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
Week 2
- 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 : Introduction to Data Preprocessing
- Lecture 10 : Major Tasks in Data Preprocessing
- Lecture 11 : Missing Data Handling
- Lecture 12 : Outliers, Categorical Data Encoding and Data Transformation
- Lecture 13 : Demo of Weka
Week 3
- Lecture 14 : Introduction to Machine Learning
- Lecture 15 : Introduction to Machine Learning Part 2
- Lecture 16 : Training and testing data
- Lecture 17 : Performance Metrics-I
- Lecture 18 : Performance Metrics-II
- Lecture 19 : Performance Metrics-III
- Lecture 20 : Demo of Orange
Week 4
- Lecture 21 : Descriptive Analytics
- Lecture 22 : Descriptive Analytics-II
- Lecture 23 : Charts
- Lecture 24 : Charts-II
- Lecture 25 : Charts-II
- Lecture 26 : Comparing Charts
- Lecture 27 : Descriptive Analytics – Example I
- Lecture 28 : Descriptive Analytics – Example II
- Lecture 29 : Excel tool
Week 5
- Lecture 30 : Diagnostics Analytics
- Lecture 31 : Correlation
- Lecture 32 : Correlation Matrix
- Lecture 33 : Spearman’s Rank Correlation
- Lecture 34 : Data Mining
- Lecture 35 : iSAT
Week 6
- Lecture 36 : Diagnostic Analytics - SPM
- Lecture 37 : Sequential pattern mining (SPM-II)
- Lecture 38 : Differential Sequence Mining (DSM)
- Lecture 39 : Process Mining
Week 7
- Lecture 40 : Diagnostic Analytics - Clustering
- Lecture 41 : K-means Clustering
- Lecture 42 : Hierarchical Clustering
- Lecture 43 : Clustering - Examples
Week 8
- Lecture 44 : Predictive Analytics
- Lecture 45 : Linear Regression
- Lecture 46 : Multiple Regression
- Lecture 47 : Logistic Regression
- Lecture 48 : Linear Regression - Example
Week 9
- Lecture 49 : Predictive Analytics-II
- Lecture 50 : Naive Bayes Classifier
- Lecture 51 : Decision Tree
- Lecture 52 : Decision Tree Classifier
- Lecture 53 : DT, NB - Examples
Week 10
- Lecture 54 : Text Analytics
- Lecture 55 : Introduction to NLP
- Lecture 56 : NLP-II
- Lecture 57 : NLP-Tools
- Lecture 58 : NLP-Examples
Week 11
- Lecture 59 : Intro Multimodal Learning Analytics
- Lecture 60 : Affective Computing -1
- Lecture 61 : Affective Computing -2
- Lecture 62 : Eye Tracking
Week 12
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Bombay
- Department
- Multidisciplinary
- Teacher
- Prof. Ramkumar Rajendran
- Level
- Undergraduate
- Language
- English
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed