How cognitive systems and personal digital records will improve education
In a recent survey, an educator in a US secondary school said, “If I had…data, before my students walk in, I could know exactly where I need to start with each one and how I need to present my lesson. It would be incredible.”
This desire seems simple enough to become reality, but doing so requires both thought and care. To give teachers actionable insight about their students, individually or collectively, a system that can help them manage the multiple sources of data is needed. If we want to give them recommended actions, not just the insights, then we also need to help them manage the multiple options of interventions and digital content.
A worthy objective, but what about data privacy?
Educators, parents and students truly embrace any goal that enables personalized instruction and learning for each student. However, concerns with student data privacy and access protections, while real and understandable, could drive policy or regulatory frameworks that actually prevent the data sharing needed to enable that personalization. Ensuring appropriate data privacy protection is essential if we are to reach the goal.
The sensitivity around data privacy evokes a comparison that is often made with healthcare—also a very sensitive area—where in most developed countries an established personal electronic healthcare record is shared among healthcare providers. A recent IBM Education paper includes a quote from “2020 Vision: A History of the Future,” (Global Silicon Valley, 2015), that further develops this comparison: “If 50 percent of patients who entered a hospital died, they would close the hospital. In education, if 50 percent of kids drop out...they bring in the next class.”
Recognizing that this healthcare data serves two scenarios may be useful to consider. In one scenario, such as a discussion between doctor and patient, the data is personal to the patient and highly sensitive. In a second scenario, in which the doctor searches all available medical data for taking a next-best action, the data is rendered anonymous.
A cognitive approach for the personalized touch
Similar parallels exist in education where personal records follow students throughout their education journeys. Advanced intelligent assistants can use the anonymous data to help a teacher make the best personal recommendations. Consider the following scenario:
Teacher: Cordelia, you did OK on your latest mathematics test; you got 72 percent. But it looks like you were challenged by the algebra questions. Is that a fair assessment?
Cordelia: Yes, I’m not sure I really get algebra. Are there any particular areas where I could improve?
Teacher: Well, let’s see what my assistant suggests.
Cognitive-enabled teacher assistant: From an analysis of Cordelia’s learning profile and her last five tests, algebra is a relatively weak area for her in mathematics. Based against learning outcomes of 1.2 million similar Year-8 students with matching learning characteristics, her understanding could be improved by either reviewing algebra module 2.3 or looking at instructional video 7.
Teacher: Codelia, I think you’ll find the video suits your learning style best. I suggest that you start with that, and then we'll see how you do with the algebra section of your next test.
This practical example shows how understanding both the assessment data of a student—the test score—and understanding the particular student offers a cognitive system that supports both the teacher and the student. IBM uses the term cognitive to reference the ability of technological systems to understand, reason and learn.
A mutual understanding of data sharing
Until recently, computing was programmable and based around human-defined inputs. Cognitive systems learn with experience. They interact with humans naturally to interpret data, learning from virtually every interaction and proposing new possibilities through reasoning, to support human decision making. In the previous example, the cognitive system is pulling together not only disparate sources of information about the student, but also patterns learned from interactions with other students and the outcomes of their experiences. This amalgamation of information from experiential sources and interactions helps in making the optimal recommendation for the particular situation—a recommendation to the teacher, who can decide whether or not to follow it.
Students and teachers understand the value of sharing data and information. Governments clearly have a role to play in assuring that personal data is protected and handled correctly. Distinguishing the narrow uses of personal data from the broader uses for anonymous data is vital for achieving the goal of personalized education and cognitive insights. Being clear about this objective can help lay the foundation for all the benefits that cognitive systems can bring. But don't just take our word for it; take a look at our interactive quiz, personalize education with cognitive insight, and see what you think.