Know How Education Transformed With Data Analytics

1. 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.

2. 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.)

3. ● 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 don’t 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.

4. 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.

5. ● 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