How will Big Data help educational institutions?

• Students’ voluminous data are easily captured in all its volumes on an ongoing basis.
•  Data can be studied for the shortest time frame. Even intra-day analysis is possible. This shortens time to action. Prompt, proactive action is possible.  
• Study patterns of students can be documented and examined on a historic basis.
•  Predicting Student Performance. Rating them - Which students are slow in tasks assigned? Who are low performers and least performers?
• Drawing up a Risk Matrix for Cohorts.
• LP (Low Performing) student needing help some online help or offsite. 
• A student in classroom session or on online learning:  Is there disinterestedness, boredom visible?  Is there frustration creeping in?
• How often and in what manner do students use educational software? (Blackboard, EBSCO, Turn-it-in, any internal intranet?) When do they really submit; the pattern of submissions.
• What is the inventory of faculty skills?
• Predicting student progression. 
• What courses and pedagogical modes attract students?
• Which courses and pedagogical modes deter students?
• Patterns in enrollment.
• Deciphering patterns in student progression.
• Analysis of student dropout ratios and causative factors.
• Student retention trends.  
• Predictive models using this type of unconventional data to assess teaching risk. 
• Knowing and monitoring the opinion and attitude of the students and stakeholders as opinions, feelings and attitudes about the EI, as discussed on the world wide web.
• Developing a sentiment analysis tool to monitor student reflections.
• Monitoring social platforms and social media websites. Reconnaissance of the social sphere, including social networks, blogs, Facebook and twitter and other relevant sites.  
•  Leveraging on valuable feedback and insights to improve offerings and services.
• Student profiling to suit personalization.
• Data would reveal the student interests.
• Using student data for cluster analysis
• Student’s propensity towards a certain subject can be measured.
• Data from online usage- from cookies, URL and software metrics – could to identify which online channels the students are using and what they are using them for.