Continual learning is a subfield of machine learning that deals with the ability of a model to continuously learn from new data, while not forgetting previously learned information. In other words, a model that is capable of continual learning can accumulate knowledge and skills over time, without the need to constantly retrain from scratch.
In the context of education, continual learning has numerous potential applications. For example, it can be used to build intelligent tutoring systems that are able to adapt their instruction to the changing needs and abilities of individual students. It can also be used to develop models that are able to learn from a wide variety of sources, including multimedia content, social media, and online resources.
At the 5 Steps Academy Research Center, we are actively researching continual learning techniques and their applications in education. Our team of experts is exploring ways to develop models that are able to learn from a variety of different data sources, and that are able to adapt their instruction to the changing needs and abilities of individual students.
One of the challenges of continual learning is the problem of catastrophic forgetting. This occurs when a model learns new information that is so different from what it has learned before that it essentially “forgets” the old information. To address this challenge, our researchers are investigating a variety of approaches, including regularizing the model’s weights and biases, constraining the model’s capacity, and using ensembles of models.
Another challenge of continual learning in education is the need to account for the many different ways in which students can learn. Different students may have different learning styles, preferences, and abilities, and it is important for models to be able to adapt to these individual differences. Our researchers are exploring ways to incorporate individual differences into the design of models, including using adaptive regularization techniques and incorporating feedback from students themselves.
Finally, our team is also investigating the use of continual learning for personalized assessment. By developing models that are able to learn from a variety of different data sources, including student performance on assessments, online resources, and social media, we can develop more accurate and comprehensive assessments of student learning.