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791 results on '"Gupta, Varun"'

25. Current Challenges in Efficient Lithium‐Ion Batteries’ Recycling: A Perspective

Author

Yu, Xiaolu, Li, Weikang, Gupta, Varun, Gao, Hongpeng, Tran, Duc, Sarwar, Shatila, and Chen, Zheng

Subjects
Engineering, Materials Engineering, Chemical Sciences, Physical Chemistry, Affordable and Clean Energy, Responsible Consumption and Production, challenges, electric vehicles, profits, recycling, spent Li-ion batteries, spent Li‐ion batteries
Abstract

Li-ion battery (LIB) recycling has become an urgent need with rapid prospering of the electric vehicle (EV) industry, which has caused a shortage of material resources and led to an increasing amount of retired batteries. However, the global LIB recycling effort is hampered by various factors such as insufficient logistics, regulation, and technology readiness. Here, the challenges associated with LIB recycling and their possible solutions are summarized. Different aspects such as recycling/upcycling techniques, worldwide government policies, and the economic and environmental impacts are discussed, along with some practical suggestions to overcome these challenges for a promising circular economy for LIB materials. Some potential strategies are proposed to convert such challenges into opportunities to maintain the global expansion of the EV and other LIB-dependent industries.

Published
2022

33. Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

Author

Kochmar, Ekaterina, Vu, Dung Do, Belfer, Robert, Gupta, Varun, Serban, Iulian Vlad, and Pineau, Joelle

Abstract

Intelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using "hints" and "Wikipedia-based explanations." Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in Korbit, a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

Published
2022
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