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Abstract:
Personalized learning has been a promising but often elusive ideal sought after in education. We'll demonstrate the progress made with two concrete examples of personalized learning supports implemented at scale in a massive open online course (MOOC) and on the UC Berkeley campus in a collaboration with the Office of the Registrar. Both approaches employ long short-term memory to leverage a collaborative signal out of millions of historic learner actions. In the case of the MOOC, the next page a learner is expected to spend considerable time on is predicted and offered as a real-time suggestion. At the university, we consider sequences of millions of historic enrollments over the past eight years. These sequences of course identifiers, when modeled with representation learning approaches most commonly applied to natural language, reveal a tremendous degree of semantic relational information about the courses which can be visualized, reasoned about, and surfaced to students. Our course information platform uses this automatically inferred semantic information to help students navigate the university's offerings and provides personalized course suggestions based on topic preference.
Personalized learning has been a promising but often elusive ideal sought after in education. We'll demonstrate the progress made with two concrete examples of personalized learning supports implemented at scale in a massive open online course (MOOC) and on the UC Berkeley campus in a collaboration with the Office of the Registrar. Both approaches employ long short-term memory to leverage a collaborative signal out of millions of historic learner actions. In the case of the MOOC, the next page a learner is expected to spend considerable time on is predicted and offered as a real-time suggestion. At the university, we consider sequences of millions of historic enrollments over the past eight years. These sequences of course identifiers, when modeled with representation learning approaches most commonly applied to natural language, reveal a tremendous degree of semantic relational information about the courses which can be visualized, reasoned about, and surfaced to students. Our course information platform uses this automatically inferred semantic information to help students navigate the university's offerings and provides personalized course suggestions based on topic preference.  Back
 
Topics:
AI Application Deployment and Inference, Consumer Engagement and Personalization, AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8597
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