Evidence-Based Adaptation of Learning: Supporting Informed Decision-Making

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People are currently inundated with information and need support to make sense of this information in complex environments, such as classrooms. One way to provide this support is through the modelling of people, their environments, and their experiences. This modelling enables adaptation so elements of the environment, user experience, or user abilities can be improved. The specific context of study for this proposal will center on educational applications of user models, which will be called student or learner models. However, the resulting modelling and visualization approaches should generalize to other application domains, especially when the problems these fields face do not have correct or incorrect solutions. Rather the questions that are asked and the appropriateness of the solutions provided are open to interpretation. The incredible amount of data produced through people's interactions with educational technologies can be extracted and used to infer information about students, teachers, and the learning environment. The output of the algorithms making these inferences can then be used to drive educational improvement through personalization. Unfortunately, this is not being done effectively for 2 reasons: 1) most of the student models that have been created for areas where there is no such thing as a correct or incorrect answer are not grounded in theory, which means principled adaptations cannot be made to learning materials, learning processes, or the learning environment itself, and 2) existing student models are not communicated in a way that supports stakeholder decision-making because they do not communicate the limitations of the underlying student models and they fail to communicate model information in a way that is understandable to stakeholders. To mitigate these problems, this program of research synthesizes knowledge and methods from cognitive science with those from several areas of computer science (artificial intelligence, human-computer interaction, user modelling and personalization, and educational data mining) to develop theoretically-grounded computational models of people's knowledge, beliefs, skills, and interests. These new student models will provide more nuanced information in an understandable way so that people and software can appropriately adapt and personalize the support and activities that are provided to students. The use of these techniques should help people to learn more, enjoy learning, and make better decisions because these student models and their visualizations will help people to understand their learning, and in the case of teachers, how their students are learning.This use of evidence to drive educational improvement will be evaluated in real learning environments. Tools will be built so that they are compatible with major educational technologies and they will be freely released once they are shown to be effective.