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Swayam Central

Learning Analytics Tools

By Prof. Ramkumar Rajendran   |   IIT Bombay
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
PREREQUISITES None
INDUSTRIES  SUPPORT     : None

Learners enrolled: 1139

SUMMARY

Course Status : Upcoming
Course Type : Elective
Duration : 12 weeks
Start Date : 20 Jul 2020
End Date : 09 Oct 2020
Exam Date : 18 Oct 2020
Enrollment Ends : 27 Jul 2020
Category :
  • Multidisciplinary
  • Data Science
  • Faculty Domain for Newly Joined
  • Faculty Domain for Experienced
  • Level : Undergraduate/Postgraduate
    This is an AICTE approved FDP course

    COURSE LAYOUT

    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

    BOOKS AND REFERENCES

    The Handbook of Learning Analytics, 1st edition, Charles Lang, George Siemens, Alyssa Wise, Dragan Gašević

    INSTRUCTOR BIO

    Prof. Ramkumar Rajendran

    IIT Bombay
    Ramkumar Rajendran is an Assistant Professor in IDP in Educational Technology at Indian Institute of Technology Bombay, Mumbai. He obtained his Ph.D. in Computer Science and Engineering from IITB-Monash Research Academy, IIT Bombay and Postdoctoral training at Vanderbilt University, USA and NEC Central Research Laboratories, Japan

    COURSE CERTIFICATE

    •The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
    •The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
    Date and Time of Exams: 18 October 2020, Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm. 
    •Registration url: Announcements will be made when the registration form is open for registrations.
    • The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
    •Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

    CRITERIA TO GET A CERTIFICATE:
    • Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course. 
    • Exam score = 75% of the proctored certification exam score out of 100
    •Final score = Average assignment score + Exam score

    YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. 
    •If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.
    • Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Bombay. It will be e-verifiable at nptel.ac.in/noc.
    •Only the e-certificate will be made available. Hard copies will not be dispatched.

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