Ethan belongs to a math class of 50 students, average when it comes to algebra, but an outstanding performer in probability lessons and tests. His teacher notices this but doesn’t have the means or time to offer specialised guidance, as the remaining 49 students also have unique learning capabilities and interests, just like Ethan.
The problem with the current education system starts here. Unlike the times of our predecessors who formulated the system, we’re living in a completely different world where the “one-size-fits-all” approach doesn’t work anymore. That’s where big data in education can make all the difference.
By aggregating student data, analysing it for improvement and delivering actionable insights to teachers, the education system can undoubtedly embrace the potential of big data. Its applications are especially crucial because 57% of students aren’t done with college even after six years, out of which 33% drop out.
Benefits of Big Data in Education
1. Reduces Dropout Rates
Dropouts are costly – for both the university and the students. Although visionaries like Bill Gates and Mark Zuckerberg were college dropouts, they only represent a small fraction of the undergraduate students who dropped out.
With big data analytics (BDA), universities can continually monitor students, provide instant feedback and help in nurturing individual skills for better academic performance and success after graduation.
2. Personalised Learning
With behavioural analytics, universities can identify the unique interests of students (sometimes even before they realise them) and offer personalised and interactive learning opportunities that adapt to a student’s strengths and weaknesses.
3. Performance Assessment
Both educators and students require continual assessments to maintain their performance. By analysing the feedback offered by students, universities can understand the effectiveness of an educator. Similarly, by analysing performance in class activities, BDA will enable teachers to identify students requiring special care, way before exams decide their fate.
4. Career Prediction
By analysing interests, progress, strengths and weaknesses, universities can predict the most appropriate career options for students. This will help the institution and the student to proactively prepare for excellence.
5. Tweak Course Plans
By understanding the courses and factors that led a student to be successful in real-life, educational institutions can tweak and better their curriculum for higher success rates. This will also help in reducing the mismatch between academic learning and future job responsibilities.
Application of Big Data in Education
1. GSU Reduced DFW Rates by 24%
Image Credit: commkit.gsu.edu
Georgia State University (GSU) realised that the majority of its students were collecting credit but weren’t graduating. To increase the student success rate, the university took the help of big data experts to set up a system that tracks the decisions of each student, every day and predicts the likelihood of their academic success.
To set up the predictive model, the university used data of the past ten years that included student information, grades and courses to start with. It is interesting to note that the system now checks 800 variables, every night, for at least 30,000 undergraduate students.
A graph that depicts the risk score of a student. Image Credit: github.com
The results were almost immediate. If a student were flagged to be falling behind, the system would set up a meeting to discuss the problem. Within a year, the system prompted around 50,000 meetings.
GSU’s graduation rate rose from 32% to 54%, and their drop-fail-withdrawal (DFW) rate plummeted from 43% to 19%. Every 1% increase in retention rate brought in an additional US$3 million return on investment (ROI) for the university.
2. PU Witnessed Increase in Academic Performance
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What works for a student won’t necessarily work for another. For universities with thousands of students, having a “one-size-fits-all” approach for teaching never works – even though universities have no other choice other than to stick to a particular curriculum.
Purdue University (PU) has over 40,000 students and wants to nurture an environment in which the different levels of knowledge, learning styles and abilities are recognised and appreciated.
For this purpose, the university embraced BDA to develop a system named Course Signals. This system helps in predicting behavioural and academic issues of students and proactively notifying teachers of the same.
Course Signals is an excellent example of applying big data in the education sector as it helps students to explore their full potential by reducing failing and drop out rates. In essence, Course Signals combines predictive analytics with data mining.
Data is collected from course management systems and student information. Course Signals has the capability to understand a student’s engagement and academic performance by looking into the preparation, efforts put into discussions, quizzes, and tests, and previous grades.
The algorithm uses an easy to follow system to predict the risk profile of each student as follows.
- Green (there is a high probability for the student to excel in a particular course)
- Yellow (there are some potential difficulties)
- Red (the chances are high that the student will fail)
With Course Signals, PU witnessed nearly 28% increase in As and Bs, and Bs and Cs with students who would otherwise have scored Cs or Ds.
3. WKU Uses Analytics for Student Success and Informed Decisions
Image Credit: crwflags.com
Universities have to deal with humongous volumes of data. For example, consider the different combinations of class schedules, accommodation details, financial information, grades and academic submissions and multiply it with thousands of students belonging to each academic year.
At Western Kentucky University (WKU), the Institutional Research (IR) office is responsible for storing and managing such vast volumes of data. They are also responsible for converting the data into information and sharing it with other staff members.
With big data analytics, the IR office was able to analyse and derive meaningful information from the data sets. This helped in identifying students who are at the brink of dropping out or who are at the risk of failing so that they can notify the concerned faculty.
A box plot chart depicting a “risky student” with high chances of dropping out. Image Credit: journals.plos.org
Advanced analytics also enabled the IR office to determine the factors that drove a student to be successful. The results of using analytics led WKU to employ it in the admission process – which helped them conclude that high school GPA is more critical than ACT scores.
The data used for the admissions process includes:
- High school GPA
- Amount of merit scholarships awarded
- Gender
- First-generation identifier
- Age
- Geographical category
- Ethnicity category
4. Khan Academy Uses BDA to Improve Lesson Quality
Image Credit: boards.greenhouse.io
When it comes to employing big data in education, the online educational organisation, Khan Academy focuses on increasing the lesson quality and learning experience. As the entire platform is digital, collecting data is much easier as compared to other universities.
With the massive volume of data collected from each user, Khan Academy has numerous opportunities to utilise BDA.
They use behavioural analytics to understand lesson quality. By monitoring the number of students who pause, stop or watch a video up to a particular point, the organisation can infer the quality. The revisits made to each lesson, and the results of tests following a lesson are also considered.
A/B testing website UI elements. Image Credit: demltd.com
Khan Academy also uses BDA to test UI/UX elements. As user experience (UX) is a critical factor that decides engagement and retention, the organisation takes it seriously and continually tests variations. The effectiveness of each design is determined by the time on the website, lesson actions, clicks and bounce rates.
5. ASU Decreased Dropout Rates by 54%
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Big data in education helps universities utilise the accumulated student data to create highly-specific student profiles – useful to perfect their teaching models.
Analytics is especially useful if institutions use software programs as tools for teaching as each keystroke, hesitation on a mouse click, and engagement time is data waiting to be exploited.
By embracing BDA, Arizona State University (ASU) developed a fully customised system known as adaptive learning. As the university describes, it is a “made-to-measure” learning tool that enables teachers to offer specialised guidance to students.
A simple representation of how adaptive learning works. Image Credit: pinterest.com
Student data such as registration information, academic records and background characteristics are taken into account. Teachers can now monitor the difficulties faced by each student, and unlike the previous system, they can communicate and solve problems.
After adopting adaptive learning, ASU experienced a notable improvement in student success rates, and its dropout rates have decreased by a whopping 54%.
6. DMACC Uses Analytics to Help Students Prosper
Image Credit: dmacc.edu
As previously mentioned, universities produce massive volumes of data each year. Accumulating and analysing such diverse sets of data can be an arduous task, particularly if they are spread across multiple departments and schools – until big data analytics enters the picture.
Des Moines Area Community College (DMACC) sees BDA and data visualization as an opportunity to enhance student enrolment, retention and graduation rates. Reports that otherwise took weeks or months to generate now take only a few seconds.
One of the most significant advantages of using predictive analytics is to rightly place each student into the perfect course – based on their affective and cognitive development.
A need for such a predictive model arose when Joe DeHart (Provost, DMACC) discovered that the entrance exam or ACT had the least correlation to a student’s performance – let alone much significance when it comes to course selection.
Relying solely on entrance scores caused students to drop out (in a broader sense), especially when students had lower placement test scores and had to attend developmental courses.
With a predictive model in place, DMACC can supplement placement scores with additional affective elements such as early registration, student orientation attendance and meetup with advisors to foresee chances of a student dropping out.
DMACC also looks into the past academic performance of a returning student, along with data regarding parental college experience, socioeconomic factors and veteran status. DMACC is also known to compare the real-world progress of students with the courses they took to formulate a model on what makes a student successful.
7. OU sees 90% Accuracy in Recruiting
Image Credit: commonapp.org
When it comes to recruitment, universities must compete to entice and enrol the best. The University of Oklahoma (OU) (as a matter of fact most of the universities) has limited resources when it comes to recruitment.
Because of such increasingly restrictive budgets, universities and recruitment officers must focus more on students who are more likely to enrol. Although experience and intuition have been great tools for recruitment, OU wanted to pursue a systematic, evidence-based approach.
An example of a college application funnel. Image Credit: medium.com
As a result, Lisa Moore, a data scientist at OU, teamed up with a big data company to utilise predictive analytics for recruitment. They used the previous two years’ admissions data to create predictive models.
They created four predictive models (for four different student groups based on residency) with the help of methods such as,
- Logistic regression (a statistical model used to estimate the probability of an event)
- Decision trees (a supervised learning algorithm with a flowchart-like tree structure)
- Forward stepwise regression and backward stepwise regression (variable selection methods)
By shifting from gut instincts to predictive analytics, the University witnessed 89-92% accuracy in recruitment – thereby allowing officers to focus their efforts on a smaller list of students. The predictive models also revealed that a large scholarship amount isn’t a vital factor for enrolment.
Case study to understand data science application in education
To grasp the impact of big data in higher education, consider how a Southeast Asian university deployed the “Student 360” strategy. By integrating diverse data sources to craft personalized student profiles, the university tailored support and communications effectively.
Segmenting students by academic performance, engagement, and financial needs led to a 15% increase in enrollment and a 25% decrease in dropouts. These results underscore the transformative power of data-driven strategies in boosting student success and operational efficiency.
To learn more about the effects of big data in education, read our case study here.
Bottom Line
Similar to personalised medicines and personalised shopping experiences, embracing big data in the education sector will enable institutions to break free from the “one-size-fits-all” approach and pass the control of choice to the students.
The revolution may be still in its infancy, but there are enough reasons to conclude that BDA will transform the way we receive and utilise knowledge in this decade alone. As online learning has become the new “normal”, BDA and artificial intelligence can now join hands to deliver gamified and personalised educational experiences.