The Courage to Consider the Role of Student Data in Education

As technology continuously reshapes how we learn, teach, and engage with ideas and information, we continue to confront challenging questions about the role of data in education. This year’s OEB conference, “Brace for Transformation: The Courage to Redefine Learning,” invites us to explore these pressing issues and envision the future of education. Once again, the 2024 OEB Annual Debate will challenge us to deeply consider the use of student data in educational settings. Let’s take a look at some of the issues that must be reconciled.

The Data Dilemma: Then and Now

Our collective concern about the impact of Data in Education was first debated during the 2014 OEB Conference. Ten years ago, the Motion before the OEB House considered whether “Data was corrupting education”. Dr. Inge de Waard and I had the pleasure of debating Dr. George Siemens and Dr. Viktor Mayer-Schönberger. We collectively questioned whether a focus on data would corrupt learning processes or reveal unimagined insights for more effective, personalized learning. Inge and I took the position that data indeed had the potential to corrupt education, while George and Victor explained how student data was going to rock our world. At the time, when we were all becoming giddy at the prospect of using “Big Data” in the service of student success, it was hard to imagine that things with data might go wrong, but Inge and I applied ourselves to the challenge and found arguments showing how data had the potential to corrupt education if not used wisely.

We made a valiant effort, generated great crowd support, but eventual lost the debate. You can read more about the Great OEB Depart from 2014 by clicking here.

The 2024 OEB Great Debate

Fast forward to 2024, and our concerns about using student data in education have only intensified. It turns out that Inge and I were right about a lot of things.

Pragmatically speaking, during the past ten year many of us have found that there may be as many problems with using student data as there are benefits. That discovery has forced us to consider and reconsider what can be done to restore confidence and trust in educational decision-making, especially where it is informed by student data.

This year’s Motion boldly asserts that “Educational Institutions Should be Banned from Collecting and Storing any Data on Students.” This progression from questioning data’s influence to considering an outright ban reflects the continuous, growing unease about the pervasive role of data in our educational systems.

The Promises and Perils of Educational Data

Over the past ten years, many potential benefits of data-driven education have been realized. Personalized learning experiences, early intervention for struggling students, and data-informed curriculum design are just a few of ways where student data are being used to remove barriers to success. Here are a few more specific examples where using student data in design and evaluation can reveal ways to actively improve student success:

Early Intervention for At-Risk Students: Data analytics can help identify students who are at risk of falling behind or dropping out. By analyzing attendance patterns, grades, and engagement metrics, educators can intervene early with targeted support.
Curriculum Optimization: Aggregated student performance data can reveal which parts of the curriculum are most effective and which need improvement. This allows for data-driven decisions in curriculum design and resource allocation.
Predictive Analytics for Advising and Career Guidance: By analyzing historical data on student interests, academic performance, and career interest alignments, institutions can provide more informed course selection and career planning advice to students.
Adaptive Learning: By analyzing individual student performance data, adaptive learning systems can tailor content difficulty, pacing, and style to each student’s needs. This personalization can lead to more efficient and effective learning experiences.
Personalized Learning Paths: By analyzing a student’s strengths, weaknesses, and learning style preferences, schools can create individualized learning paths that optimize the educational journey for each student.
Enhanced Feedback for Educators: Detailed data on student performance can provide teachers with insights into their teaching effectiveness, allowing them to refine their methods and address specific areas where students are struggling.
Efficient Administrative Processes: Data systems can streamline administrative tasks like enrollment, scheduling, and record-keeping, allowing educators to spend more time on teaching and less on paperwork.
Research and Innovation: Anonymized student data can contribute to educational research, leading to new insights about learning processes and the development of innovative teaching methods.

While these benefits are notable, it’s crucial to remember that they must be balanced against privacy concerns and potential misuse of data. As we’ve embraced these possibilities, we’ve also exposed ourselves to significant risks. Let’s take a look at some of the most significant concerns.

Privacy Concerns

In 2014, we were just beginning to grasp the implications of data analytics in education. Today, with the proliferation of online learning platforms and educational technology, the volume and variety of data collected on students have exploded. This raises serious questions about privacy, consent, and the long-term consequences of creating comprehensive digital profiles of learners from a young age.

Power Dynamics

As I noted in the 2014 debate, “Data without context has marginal value. However, data WITH context is information. Information is power. And as the British historian Lord Acton reminded us, Power corrupts. Absolute power corrupts absolutely.” This observation remains acutely relevant. The concentration of student data in the hands of educational institutions and technology companies creates a significant power imbalance. How can we ensure this power is wielded responsibly and in the best interests of students?

The Risk of Misuse

The potential for data misuse extends beyond privacy breaches. There’s a real risk of data-driven decision-making leading to discrimination or the perpetuation of existing biases. For instance, predictive analytics might be used to steer students towards or away from certain educational paths based on historical data, potentially limiting opportunities rather than expanding them.

Generative AI: A Game-Changer for Education

As we grapple with the implications of data use in education, we must also confront the impact of artificial intelligence, particularly generative AI, upon the evolving educational landscape. Gen AI technologies are poised to revolutionize education, offering unprecedented opportunities for personalized learning and educational support. However, they also introduce new complexities to our ongoing debate about student data.

Generative AI, exemplified by large language models and other AI systems capable of creating content, has the potential to transform educational practices:

Personalized Learning Companions: AI tutors could provide individualized instruction and support, adapting to each student’s learning style and pace.
Content Generation: AI can create customized educational materials, exercises, and assessments tailored to individual student needs.
Language Learning: AI-powered language learning tools can offer immersive, conversational practice in foreign languages.
Accessibility: AI can generate real-time captions, translations, and explanations, making education more accessible to diverse learners.
Automated Grading and Feedback: AI systems can provide instant, detailed feedback on assignments, potentially freeing up teacher time for more personalized interaction.

New Dimensions of the Data Debate

The integration of AI, especially generative AI, into educational settings adds new layers to our concerns about student data:

“Data Hunger”: AI systems, particularly those using machine learning, require vast amounts of data to function effectively. This could intensify the push for comprehensive data collection on students.
Privacy and Consent: As AI systems become more sophisticated in analyzing and generating content based on student data, questions of privacy and consent become more complex. How do we ensure meaningful consent when the full implications of AI data use may not be immediately apparent?
Bias and Fairness: AI systems can perpetuate or even amplify existing biases if not carefully designed and monitored. How do we ensure that AI-driven educational tools promote equity rather than reinforce disparities?
Data Ownership and Control: With AI systems potentially generating new insights and content based on student data, questions of ownership and control become more pressing. Who owns the outputs of an AI system trained on student data?
Long-term Implications: The use of AI in education could lead to the creation of even more detailed and persistent digital profiles of students. How might these AI-enhanced profiles impact students’ future opportunities and privacy?
Transparency and Explainability: Many AI systems, especially deep learning models, operate as “black boxes,” making it difficult to understand how they arrive at their outputs. This lack of transparency poses challenges for accountability in educational decision-making.
Dependence and Digital Literacy: As AI becomes more integrated into education, there’s a risk of over-reliance on these systems. How do we ensure that students and educators maintain critical thinking skills and the ability to evaluate AI-generated content?

Navigating the AI-Enhanced Educational Data Landscape

In our quest to redefine learning for the digital age, we must ensure that our use of AI and student data aligns with our fundamental values of privacy, autonomy, and equal opportunity. The courage to embrace these technologies must be matched by the wisdom to implement them responsibly, always keeping the well-being and rights of learners at the forefront of our innovations.

Conclusion: A Call for Thoughtful Progress

As we debate the motion to ban data collection in educational institutions, let’s remember that the goal is not to impede progress, but to ensure that our educational practices align with our values of privacy, autonomy, and equal opportunity.

The courage to redefine learning in the age of big data may not in avoiding data altogether, but in confronting its challenges head-on. We must dare to imagine and create educational systems that leverage the power of data to enhance learning while rigorously protecting student rights and privacy.

Written for OEB Global 2024 by Ellen Wagner.

About the author: Ellen D. Wagner, Ph.D.

Ellen Wagner is an ed tech innovator, analyst and advisor. Her experiences range from tenured research professor, department chair and administrator in Academic Affairs and Continuing and Professional Education, to successful tech entrepreneur, with three exits to her credit. She has served as a senior executive in five commercial software companies, including Macromedia and Adobe Systems. These experiences helped inform her role as Vice President of Technology and Innovation for the Western Interstate Commission for Higher Education, as she led their community of practice membership association, WCET. At WCET, she co-founded the Predictive Analytics Reporting (PAR) Framework, a predictive analytics research effort funded by the Bill & Melinda Gates Foundation. PAR launched as an independent non-profit service provider; Shortly thereafter PAR was acquired by Hobsons. where it became part of Starfish Retention Solutions. Proceeds from this sale established and funded the Foundation for Student Success, now managed by NCHEMS.

Ellen is currently managing partner of North Coast EduVisory Services, LLC, where she advises learning tech companies and higher educational stakeholders on digital transformation and emerging technologies for learning, human performance and organizational capacity development.

She is affiliated with the Mixed Emerging Technology Integration Laboratory, Institute for Simulation and Training, University of Central Florida. She serves on the editorial board of eLearn Magazine, and is a reviewer for several juried journals in the fields of learning tech, elearning and online learning. She is a member of the Advisory Board of the Kirwan Innovation Center of the University System of Maryland, as well as the Global Advisory Board for OEB (Online Educa Berlin).

Ellen is speaking at:

The OEB Annual Debate: This House believes that Educational Institutions Should be Banned from Collecting and Storing any Data on Students

Thursday, November 28, 2024 5:45 PM to 7:00 PM

Learning Engineering: Current Perspectives and Future Directions

Friday, November 29, 2024 2:30 PM to 3:30 PM