Why Amazon Can Read Your Mind but Your Students are Still Falling Asleep in Class

10.14.2024 | Written by Jessi Watson

Imagine that you just finished binge-watching the season finale of that exciting new show on Netflix. You reach for the remote in disappointment, but a banner pops up at the bottom of the screen that says “suggested for you.” You may have turned off the TV or switched to another streaming service, but now you’ve been given another option: the option to stay and continue consuming entertainment that is tailored specifically for you. This is the future of not only our entertainment industry, but our online shopping experiences, our fast-food dining, and most importantly to me, the future of education.

So, how does Netflix know what you want to watch? How does Amazon know that you are out of dog food? How can an online learning platform keep a student’s attention and provide a personalized experience? The answer: Big Data. Adaptive learning AI, machine learning, and predictive analytics are transforming the landscape of education based on behavioral data, leading to a future of tailored learning experiences that are as streamlined as the Amazon shopping cart, and as enthralling as a juicy new series.

Netflix tracks clicks with analytic software (Netflix, n.d.). Starbucks takes it a step further and sends out personalized emails to customers who haven’t visited the store in a while, re-engaging them with discounts and other marketing materials (Danao, 2024). McDonald’s drive-thru has digital menus that change based on time of day, weather, and historical sales data (McDowell, n.d.).

This click-tracking technology is essentially the same technology that is used to track logins or views on a Learning Management System (LMS). Predictive analytics features of LMS like Blackboard and Canvas use student data to identify problems with engagement and give instructors the opportunity to intervene (Sghir, Adadi, and Lahmer, 2022). Educational institutions should be able leverage data in the same way that corporate America has.

So, why does it feel like Amazon can read my mind, but my students still only log into my LMS once a week? Why does it seem like corporate America is ahead of education in terms of personalized experiences? The answer is complicated. Large companies not only have a vast amount of customer data, but they also have more resources to invest in infrastructure like on-staff data scientists and advanced analytic tools. The business sector is not constrained by the regulatory requirements of educational institutions or the bureaucratic and often government-funded decision-making processes. Big companies also have simpler and more accessible goals than colleges and universities, making their data easier to collect and analyze. It is easier to collect data to enhance things like profitability and customer satisfaction than to collect data that leads to enhanced equity, improved learning outcomes, and retention rates. Educators don’t just sell stuff. They give students opportunities for personal and professional growth through our learning experiences. Not as simple as selling a cheeseburger.

Data collection and use in education presents some unique challenges. Performance data is harder to collect in fields like English and Philosophy, where evaluation of work products is at the mercy of rubric makers (Xu & Yang, 2024). Hard data can also overlook student demographic factors, leading to unintended consequences. For example, when academic institutions use cost data from instructional programs to set differential tuition, it can negatively impact low-income students. Although the institution's goal may be to increase access for underrepresented groups, budget constraints could lead to higher tuition for engineering and lab courses. This, in turn, might limit access for low-income students who are unable to afford the increased costs of these courses (Beattie et al., 2013).  Additionally, many educators may be skeptical of new tools and unwilling to adopt changes (Dana et al., 2021). College faculty may have fears that data will be used against them or that administrators will cherry pick data to influence decisions. For some K-12 educators, data collection has come to represent the weaponized use of standardized testing to monitor and punish schools and teachers (Henig et al., 2019).

Despite these challenges, many higher educational institutions are beginning to recognize the potential of big data and are making strides to integrate data analytics into their operations. A Colombian university used predictive analytic software to predict the dropout probability for all of their 7000+ undergraduate students on the first day of the semester with 93% accuracy (Castillo, 2019). An increased focus on data literacy, investment in analytics tools, and partnerships with educational technology companies like Civitas and IBM’s Watson are helping to bridge the gap and unlock the potential of big data in education.

Moving forward, higher education institutions should focus on training educators in the analysis and use of the data that their LMS are collecting. This means significant financial, technical, and human resources to motivate and engage faculty and administrators in data use processes (Davis, 2023). It also means more designers. Learning designers are key to making retention platforms and learning materials something that both educators and learners want to interact with.

The potential for big data to revolutionize education is immense. Just as corporate America has used predictive analytics to personalize experiences, educational institutions can leverage similar technology to enhance student engagement, retention, and success. By investing in data literacy for administrators and instructors, developing partnerships with education technology companies, bolstering creative designers, and fostering a culture that embraces data-driven decision-making, the education sector can catch up with our corporate contemporaries. With the right tools and approach, educators can transform data into insights, making the learning process as engaging and intuitive as your favorite app. While big education and big business are wildly different animals, the future for both will be measured in billions of clicks.

• References

  Beattie, J., Thornton, B., Laden, R., & Brackett, D. (2013). 21st Century Challenges in Higher 

  Education: Strategic Changes and Unintended Consequences . International Journal of Educational Leadership Preparation, 8(1). https://doi.org/National Council of Professors of Educational Administration 

  Castillo, J. R. (2019, December 9). Predictive analytics for student dropout reduction at 

  Pontificia Universidad Javeriana Cali. EDUCAUSE Review: the Voice of the Higher Education Technology Community. https://er.educause.edu/articles/2019/12/predictive-analytics-for-student-dropout-reduction-at-pontificia-universidad-javeriana-cali 

  Dana, C., Soffe, B., Shipley, J., Licari, F., Larsen, R., Plummer, K., Bybee, S., & Jensen, 

  J. (2021). Why do faculty resist change? MedEdPublish, 10(1). https://doi.org/10.15694/mep.2021.000089.1 

  Danao, M. (2024, July 22). 7 amazing email marketing examples in 2024. Forbes. https://www.forbes.com/advisor/business/software/email-marketing-examples/

  Davis, S. C. (2023). Engaging faculty in Data use for program improvement in teacher education: How leaders bridge individual and collective development. Teaching and Teacher Education, 129, 104147. https://doi.org/10.1016/j.tate.2023.104147 

  Henig, J. R., Jacobsen, R., & Reckhow, S. (2019). Outside money in school board elections: The Nationalization of Education Politics. Harvard Education Press. 

  McDowell, E. (n.d.). Spicy Chicken Nuggets are back at McDonald’s. here’s how the McDonald’s  menu has changed through the years. Business Insider. https://www.businessinsider.com/then-and-now-mcdonalds-menu-items-2020-2

  Netflix Research. Netflix research. (n.d.). https://research.netflix.com/research-area/analytics

  Sghir, N., Adadi, A., & Lahmer, M. (2022). Recent advances in Predictive learning analytics: A decade systematic review (2012–2022). Education and Information Technologies, 28(7), 8299–8333. https://doi.org/10.1007/s10639-022-11536-0

  Xu, S., & Yang, Q. (2024). Exploring the data turn of philosophy of language in the era of Big Data. Trans/Form/Ação, 47(4). https://doi.org/10.1590/0101-3173.2024.v47.n4.e0240050