Leveraging Big Data with Connected Content for Stronger Academic Outcomes

NEOLMS

The days of gauging learning success on the basis of metrics such as enrollment at your campus, attendance in your classes, or course completion are long gone! The only relevant metric these days is whether your institution is able to successfully deliver its curriculum; and whether the students were successful in getting what they expected from the course.
In other words, the metric that matters the most is student outcomes. So, how do you ensure that your courses meet the expectations? The answer is: Big Data.

Generally speaking, Big Data is the collection of large sets of data. In this case, the data comes from a learning context and can be algorithmically analyzed for trends and patterns. The resulting analysis can then be used to better respond to students’ needs by professionals like you, content creators, decision-makers, and administrators in higher education institutions.

Leveraging big data in a learning context

Historically, data has been used as a tool to market learning opportunities to students and to evaluate metrics that demonstrate why students should choose one learning center over another. These metrics are usually increased enrollment rates, higher graduation rates, and better test scores. While these are critical measures in the educational context, they do not directly correlate to better student outcomes.

If you wish to ensure that your institution is able to deliver strong academic outcomes, then you should leverage the power of Big Data. We recommend four considerations for doing so.

  1. Campus competitive analysis

    Most educational institutions, regardless of their affiliations or proficiencies, are highly competitive. As a campus administrator, you need to assess whether your institution is competitively positioned over other organizations offering students similar courses.

    Competitive learning centers are more likely to deliver better academic outcomes than those operating on the fringes. Here are two ways for analyzing how your campus is positioned.

    Using 30+ datasets compiled by the federal government, plus over 5,000 published analytical data sources, you’ll have access to aggregated data about a broad range of institutions, from secondary schools and high schools to higher education.

    Use the data to check where you stand on student financing, faculty qualification, teacher compensation, and other vital stats about what makes a truly competitive institution. Highly-competitive institutions often attract competitive learners, who will work smarter to achieve their goals. Completing this competitive analysis and acting on the results is the first step to increasing student academic outcomes in your institution.

  2. Course offerings

    One obstacle that often prevents better academic outcomes is a lack of choice for students when selecting their courses. Students usually gravitate to courses that offer them better long-term prospects.

    If learners can’t find what they are looking for, in terms of diversity of content, accessibility, interactivity, and real-world application, then they are likely to either not enroll. If they do register, they will not be fully engaged. As educators are well aware, a lack of engagement will usually result in poor academic outcomes.

    Big Data-driven course-selection decisions — and the connections among content in the courses you offer — are the precursors to ensuring students achieve the outcomes they need from the courses you offer. You can leverage Big Data to help you accomplish this.

    • Use the Bureau of Labor Statistics (BLS) database to analyze which occupations promise the most growth in coming years.
    • Create the optimal course-mix that addresses those professions.
    • Show students the connections between the content they’re learning and its applications in the real-world scenarios.

    Institutions should be able to offer learners the knowledge and training students will need when they enter the workforce. Thes institutions will be the ones that produce better student outcomes at every level—from enrollment and completion to engagement and knowledge transference.

  3. Course design

    Big Data can be a powerful tool for course designers looking for ways to make their content more connected and, thus, more appealing to a broad segment of learners. You can use Big Data when designing courses in several ways.

    • When making decisions about the technologies to use when building and deploying courses, either online or instructor-led, you can use Big Data to find and select these technologies. The more popular and user-friendly the learning environment is, the more engaged students will be.
    • When analyzing whether tests, quizzes, and assignments need to be changed or optimized to ensure students are not over- or under-challenged, you can use Big Data analytics to then re-visit your content to ensure underperforming students aren’t left behind and underchallenged students are given enhanced ways to demonstrate their abilities.
    • When assessing what learners like or dislike about a course, you can use Big Data to then adjust your content to ensure your students are taught what needs to be learned in a way that aligns with their learning preferences and shows them why the content matters.
    • By analyzing learner feedback, Big Data can help you personalize your designs, so that students will interact more closely with your content.

    More advanced levels of Big Data usage, such as artificial intelligence, adaptive learning, and predictive analysis, can help course designers create connected content adjusts itself to deliver unique lessons to students from a collection of connected learning objects. This will ensure better absorption and retention of material, which translates to stronger academic outcomes.

    Partnering with a learner-centered course design company like Wisewire that uses evidence-based techniques to create real-world courses, will give you a head-start in creating data-driven learning solutions.

  4. Data sharing

    Many higher education institution have formal and informal partnerships with other learning centers, such as other schools, community learning organizations, or online teaching centers. There is likely a lot of data that is originated, stored, and analyzed at your partners’ institutions. If accessed and used appropriately by you and your partners, that data can lead to better student academic outcomes.

    • Consider using a Customer Relationship Management (CRM) tool that tracks a student’s/learner’s interaction at each of those partners. Learn your future students’ strengths and weaknesses, as well as their learning preferences, so that you can tailor your teaching approach to build on their strengths and address their weaknesses.
    • Use a Learning Management System (LMS) to manage, monitor, track, and analyze learner interactions within your own campus, or, if available, integrate your LMS with your external partners’ systems. Most LMSs have powerful data analytics capabilities that can be utilized to ensure better student outcomes.
    • From these sources, use data to prioritize and personalize learning outcomes based on real-life needs and broad student preferences.

    Combining internally-generated data with data from external sources will give you much better and broader insights of how students are performing across a continuum of learning environments. This then offers you even more opportunities to manage the learning environment within your campus, to create content that is connected to what students learn in other courses and environments, and to develop best practices that results in stronger academic outcomes.

Big Data = Big Responsibility

While Big Data is a powerful tool to increase academic outcomes, it also poses some great challenges to campus administrators, course designers, and educators at large. You need to be extremely conscious of your responsibilities about the type of data that you collect, request from third parties, share, or store.

There are government policies and regulations around what data is acceptable to collect and what’s not allowed. How you use that data is also regulated, both federally and at the state-level. Finally, while Big Data is “amalgamated” or “rolled-up” data, often without personal or individual identifying elements, the potential for hacking and cyber attacks needs to always be on your mind when using Big Data.