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Model Institute of
Engineering & Technology
Big Data-Use Cases in Higher
Education
SAHIL SAWHNEY
MGR. STRATEGIC INITIATIVES,
MIET
Key Business Challenges and Opportunities
•Acquire the most talented students
•Maximize students performance
•Retain students throughout their academic careers
•Understand and promote most effective instructors
•Shorten time consuming research and produce
accurate and impactful outcomes
•Identify at risk students
Case1-Predictive Analytics
 Colleges are using Big Data to predict which
students will do well—Before They Accept Them
 It is also being used to predict the
 Success of a research project early.
 Continuation of project
 Stop the project
WSU's recruitment model had 96% accuracy
identifying "high-yield" application prospects
Case 2-Next Gen. Analytical Framework
 Measurement, monitoring and tracking of
institution’s, faculty and student performance
 Institutional Benchmarking
 Real time alumni tracking through LinkedIn
 Automated Guidance and Counseling
 Real time research reports though Google
Scholar
Case 3 Social Media Reputation Index
SMRI
Key
Conversations?
Threats
Community
Building and
Communication
Positive and
Negative Trends
•Automated Sentimental
Analysis
•Real Time Social
Monitoring
•Popularity Index
•Competitor Analysis
Case 4-Global Academic Cloud
 Create Universally Unique Identifiers for each
student
 Track student progression, academic
performance, achievements, extra/co-
curricular activities across institutions and
geographies
 Authentic records with documentary evidence.
 Quick and on-demand verification.
 facilitate global migration of students
State of Big Data Implementation in
Education in India
 Missing Thread
Status at MIET
 Next generation analytical tool-PI360
Performance Tracking
Predictive Analytics(In process)
Real time alumni tracking
 Dedicated Social Media Cell
 Conceptualization of Global Academic Cloud
Power of Analytics
Analytics
Insights
Intelligence
Strategic
Interventio
n
Competitiv
e
Advantage
Thank You

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Big Data-Use Cases in Higher Education

  • 1. Model Institute of Engineering & Technology Big Data-Use Cases in Higher Education SAHIL SAWHNEY MGR. STRATEGIC INITIATIVES, MIET
  • 2. Key Business Challenges and Opportunities •Acquire the most talented students •Maximize students performance •Retain students throughout their academic careers •Understand and promote most effective instructors •Shorten time consuming research and produce accurate and impactful outcomes •Identify at risk students
  • 3. Case1-Predictive Analytics  Colleges are using Big Data to predict which students will do well—Before They Accept Them  It is also being used to predict the  Success of a research project early.  Continuation of project  Stop the project WSU's recruitment model had 96% accuracy identifying "high-yield" application prospects
  • 4. Case 2-Next Gen. Analytical Framework  Measurement, monitoring and tracking of institution’s, faculty and student performance  Institutional Benchmarking  Real time alumni tracking through LinkedIn  Automated Guidance and Counseling  Real time research reports though Google Scholar
  • 5. Case 3 Social Media Reputation Index SMRI Key Conversations? Threats Community Building and Communication Positive and Negative Trends •Automated Sentimental Analysis •Real Time Social Monitoring •Popularity Index •Competitor Analysis
  • 6. Case 4-Global Academic Cloud  Create Universally Unique Identifiers for each student  Track student progression, academic performance, achievements, extra/co- curricular activities across institutions and geographies  Authentic records with documentary evidence.  Quick and on-demand verification.  facilitate global migration of students
  • 7. State of Big Data Implementation in Education in India  Missing Thread
  • 8. Status at MIET  Next generation analytical tool-PI360 Performance Tracking Predictive Analytics(In process) Real time alumni tracking  Dedicated Social Media Cell  Conceptualization of Global Academic Cloud

Editor's Notes

  1. Presentation slide for courses, classes, lectures et al.
  2. Personalised Learning experience Adaptive learning process Loyal alumni
  3. To predict the aptitude training requirements based on the demographic and social profile of the students To predict the
  4. Performance and Skill level of individual students.
  5. Where the key conversations are taking place? What positive and negative trends are being discussed by key influencers? Where threats to your brand’s reputation exist and how they can be managed quickly? What techniques and content you might need to build positive momentum for your brand in social spaces? How you can quantify the value of social media marketing?