Know How Education Transformed With Data Analytics

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Know How Education Transformed With Data
Analytics
In 2017, the data analytics team from
Northeastern University hit the nail on the head
when they employed a "predictive model" for
identifying the best-fit admission applications.
Metrics defining a student's high-school
background, email proactiveness/responsiveness,
and other postsecondary enrollments formed the
basis of their data analysis powering the
University to the 40th rank in the 2017 U.S.
News World Report rankings from 115th in
2006. A similar turn of events was reported by
Kennesaw State University in 2018 when they used
data-driven analytics to design cost-effective
and customized learning experiences. Data was
generated across each touchpoint of the
web-based portal and leveraged to gather student
and teacher feedback, tailor learning
experiences, and equip educators with the tools
to act accordingly in real time. Such predictive
modeling saw a reduction of 48 in the dropout
rate for the course Foundations for Healthy
Living (WELL 1000). Likewise, from the University
of Maryland to the University of California to
even Purdue University, a host of grade-A
institutions worldwide are inclining towards the
use of data analytics to transform the education
system and bolster student outcomes. The use of
predictive analytics and machine learning
algorithms is quickly becoming the latest trend
in higher education and has already led to
several successful upshots. Data Literacy and
Enhanced Learning - A Relationship Made in
Heaven Data literacy has been heralded to be the
biggest force that will propel the world toward
a smart, connected space. With data being aptly
utilized, there is no limit to what can be
accomplished. And if you are in the education
sector, it's no secret that data is, indeed,
powerful.
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The big question, however, is - What type of data
do we need, and how do we utilize it for
enhanced learning and overall ed-space
transformation? Although the answer lies in
analyzing the data generated across all
touchpoints of student engagement, accomplishing
the same and putting the consolidated
information to use isn't as simple as it sounds.
For one, it is crucial to keep in check the data
idiosyncrasies, decide on the best fit models and
algorithms, and utilize the same in real
time. Indeed, an all-inclusive data analytics
process seems to be the answer. But what does it
entail?

• Employing a Central Data Analytics Team
• When it comes to identifying core data points,
  leveraging data for enhanced learning, and
  making the most of the very same, it is important
  to employ a data analytics team that can do all
  that and more. The involved players can take up
  any of the following roles
• Data analysts who work towards aggregating,
  cleansing, and modeling the data across various
  sources for business intelligence
• Data scientists who are in charge of analyzing
  information across touchpoints (registration
  forms, online surveys, class participation, etc.)

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• DataOps professionals who oversee the entire
  data analytics process from top to bottom.
• Krawitz et al. from McKinsey Company says that
  "higher-education leaders must explicitly
  allocate the requisite financial and human
  resources to establish a central department or
  function to oversee and manage the use of
  analytics across the institution."
• Exploring Data Sources, Identifying
  Vulnerabilities, and Setting Standards
• With a team in place, the next step is to look at
  the various data sources across the institution
  and identify where data errors can be found. It
  is important to confirm whether there are
  erroneous entry points, incomplete reporting
  exercises, improper reporting standards,
  erroneous metadata, and wasted effort in the use
  of analytics platforms.
• For instance, one of the most common errors found
  when gathering data is when students dont fill
  in the "Religion" field. An appropriate change in
  such a scenario would be to add a checkbox with
  "None" as an option instead of leaving the field
  blank.
• Similar changes can be made across all
  give-and-take points, which can help eliminate
  ambiguity and enable more accurate reporting in
  the long run. A call-to-action over email, for
  example, can also help eliminate incomplete
  entries and respond in real time. All in all, the
  idea is to be granularly driven in the data
  analytics process.
• Monitoring the Influence of Data Analytics
• Contextualization of data and the subsequent
  conceptualization of its impact makes all the
  difference when you are identifying the
  feasibility of various interdepartmental
  analytics strategies.
• For instance, let us assume that an institution
  is looking to utilize admission data. For the
  same, a case study is conducted to explore how
  various factors influence students to take
  admission to a particular university.

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• For the given scenario, data is aggregated from
  various sources, including
• Student interviews
• Website heat maps
• Survey responses from current students and
  alumni/ae
• Course module interaction via emails and social
  media
• Post the study of both qualitative and
  quantitative data, key conclusions are drawn
  about the students who complete the admission
  process. These students
• Are driven by the reported statistical success
  factor after the course has been completed
• Experience pressure from friends and family to
  complete the application process early.
• Submit the application forms before the deadline
  is extended.
• However, the results also show that increased
  engagement with one's course syllabus has a
  below-average influence on students' enrollment
  decisions. Besides, the amount of time spent on
  the University's website does not play a
  significant role in influencing one's decision to
  take admission.
• Based on such patterns, the University can now
  strategize better by concentrating on factors
  such as
• Increasing overall course activity and engagement
• Decreasing time spent on each page of the
  University's website
• Deepening engagement with students and parents
  through email and online portals.
• The Scope of Data-Driven Implementations in
  Education
• Although the use of data for enhanced learning
  and analytics is still at a nascent stage, it is
  already making its mark in the education
  industry. Here's how
• With the aid of anonymous data-point aggregation
  using machine learning algorithms, institutions
  can build predictive models to teach students
  what they are most likely to do.

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• With a data platform, institutions can create and
  leverage real-time dashboards where
  administrators and stakeholders can gauge the
  effectiveness of various interventions across
  campus (such as career counseling).
• With a data analytics solution, an institution
  can gain a 360-degree view of student engagement
  by tracking various activities across various
  touch points from registrations through
  product purchases to course completion and
  correlate those data points to program outcomes.
• By analyzing the most common data entry errors
  across various layers of a school or University,
  an institution can streamline its processes and
  eliminate glitches to cut down on wasted time and
  effort.
• Educators, too, can leverage analytics to
  identify the most common patterns in student
  engagement and build their future lessons and
  curricula accordingly.
• The possibilities are endless, and so are the
  benefits of the implementation of analytics in
  education. But, there's no denying that a lot of
  schools are still stuck in a rut when it comes
  to leveraging the immense power of their data to
  deliver tangible results.
• So, if you are one such school/college or an
  aspiring academician looking to transform the
  education space with analytics, reach out to us
  at EnFuse we'd be glad to help you make the
  best of the data!
• Read more Critical Pillars Of Your MDM Strategy