Topic — Online learning with learning science, cognitive science, and computer science.
I have published seven research papers at ACM L@S conference, AAAI Ed AI conference, regarding this research theme in the past one year. I’d love to share the findings with a broader science community, on the interdisciplinary research area, and how to step into such an area and deliver things that bring inner peace. There are five sub topics covering the breadth and depth of the research topic, that are described as follows.
Sub topics -
- Build student models of learning
- Build skill models of learning
- Personalized learning
- Build models with little data
- Collaborative learning
Build student models of learning
A student model of learning is a computational model that describes or simulates how individuals learn through a learning experience. Knowledge tracing is the family of the techniques that aim to approximate the knowledge state of the brain, as well as how the knowledge state changes from one to another by observing the way individuals learn. Accuracy, reliability, and interpretability of knowledge tracing is the primary research focus that aims to improve the simulation quality.
I did some investigation, knowledge state representation, evaluating KT, interpretable KT, around how to apply the SOTA machine learning algorithms to improve the accuracy, provide interpretation and reliability of knowledge tracing with learning data collected through an online workforce learning platform.
Build skill models of learning
A skill model of learning, or a cognitive model of learning, is a descriptive account or computational representation of human thinking about a given concept, skill or domain. It includes both a way of organizing knowledge within a subject area, and an account of how humans develop accurate and complete knowledge of that subject area. Due to the fact that the cognitive processes happening in brain is hard to observe and domain experts having the expert blind spot, it is important to design experiments that are observable, analyze observations, optimize the understanding of cognitive process, and ultimately improve the way humans learn.
I did some research, under blind review, around building cognitive models of learning from learning interaction data, with which the instructional design could be improved.
Personalized learning is defined here as understanding the individual, the learning goal, the content, and the learning context, and providing targeted learning experience to achieve some desired learning outcome. The challenge is how to describe the individual and his/her goal, to represent the learning content, and to contextualize the learning experience so that the experience can be tailored for that specific learning need in its most effective way.
I did some research on this topic by understanding and modeling the individual, the content, and the learning context to provide recommendations on the next learning move and also insights on where he/she is in the learning journey, how far it is towards the end goal.
Build models with little data
How to model and provide insights when data is too little to train? Algorithms in cold start setting is challenged when it comes to reliability and accuracy. Meta learning is outperforming by either optimizing the gradient descent of gradient descent, or leveraging external memory bank to keep track of the gradient descent procedure for reference in the later optimization.
I did some research around using attention mechanism and external memory bank, along with reading and writing operations, to provide accurate prediction with little training data.
Collaborative learning here is defined as agent-human interactive learning. Agent refers to a system, a voice assistant, or a bot providing hints and nudging. This research topic is around how to leverage the collected data, the rich insights from the data, and provide interactive learning environment to make learning more fun, effective and additive.
I did some research, targeted feedback with CRQ [link coming soon], around introducing Alexa, the voice assistant, into online learning to provide in-person practice for developing some desired skills where in-person practice is a more effective approach for developing that skill. I also researched a bit into how to provide targeted feedback for the free-form text responses in achieving some learning outcome.