As of last week, I’ve started doing research with an incredible professor at my university! His work revolves around using machine learning to develop personalized online education systems, something I’ve dreamed of developing since my teen years. These systems factor in a user’s background (familiarity with course context, self-professed learning style, etc.) when providing explanations for new topics and problems, facilitating a deeper contextual understanding.
For instance, let’s use the situation of two sets of students taking an introductory programming course: students new to coding and students experienced at coding. While the experienced coders might find text explanations more helpful, it might be the case that those new to coding prefer videos. Personalized learning systems take this context into account and adapt future explanations appropriately. In this way, learners are given the explanations that are best for them.
What if there’s a subset of experienced coders that are actually audio-visual learners? Though on average text-based explanations might be more helpful for experienced coders, certainly videos would work better for these students. Given enough data, these systems can discern the existence of such subgroups and modify their learning experiences accordingly. In essence, personalized learning systems provide individually-tailored educational experiences, and on account of their machine learning base, the more they are used, the better they become.
For now, I’m still trying to learn the statistical concepts underlying the technology, but I hope to improve my skills over the coming weeks and participate more in this cutting-edge research!