Professor Jaideep Srivastava

CSENG Computer Science & Eng
College of Science & Engineering
Twin Cities
Project Title: 
Predicting Dynamic Embedding of User and Item for Personalized Recommendation

With the growth of the internet, online learning platforms such as edX, Coursera, and Udacity have emerged. These Massive Open Online Courses (MOOCs) are changing the landscape of higher education. The advantage of MOOCs are that they make courses available at nominal cost to students all across the globe. With the ability to reach a large number of learners around the world, MOOCs have made a positive impact on open education. In addition, the working community finds online courses useful for acquiring specialization needed in their jobs rather than to earn credits. Due to these reasons, MOOCs have emerged as an unparalleled technology challenging traditional educational settings.

Despite the convenience provided by MOOCs, drop-out rates on these platforms remain high. Some learners who drop out report lack of support by these platforms as a major reason for their disengagement. A factor contributing to this lack of personal guidance is that the online learning platforms are generalized and not customized for different individuals. To tackle this problem, systems must be developed that support personalized learning. Personalized learning is dependent on a teaching and learning process that addresses the strengths, needs, and interests of individual learners. Massive data generated by online learning platforms has made research in this direction possible. Machine learning and data mining communities are focusing on application of AI in MOOC education research. Personalized learning can be provided along three different dimensions, namely assessment, intervention, and recommendation. This research encompasses all these dimensions.

The first step leading to development of personalized systems is to identify the needs of individual learners. Skill assessment also opens opportunities for automatic hint generation which helps in providing an interactive system for promoting student learning. In addition to personalization, education sector demands equitable growth of each subpopulation of students. Educationalists have advocated development of targeted strategies to help all students succeed. For this purpose, they need objective data to ensure that no subgroup of students is overlooked, for which they need that student data be disaggregated.

To summarize, MOOCs are still in beginner mode and there exists enormous scope for research in this field. This research transforms MOOCs into test-beds for advancing educational research, and ultimately, improving learning. Advancements in the MOOC framework provides a cost-effective way for everyone to afford a high quality education and produces a huge impact on society.

Project Investigators

Himanshu Kharkwal
Jiacheng Liu
Abhiraj Mohan
Meghna Singh
Professor Jaideep Srivastava
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