Browsing by Author "Wang, Y."

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    The beginning of a beautiful friendship? Intelligent tutoring systems and MOOCs
    (2015) Aleven, V.; Sewall, J.; Popescu, O.; Xhakaj, F.; Chand, D.; Baker, R.; Wang, Y.; Siemens, G.; Ros�, C.; Gasevic, D.
    A key challenge in ITS research and development is to support tutoring at scale, for example by embedding tutors in MOOCs. An obstacle to at-scale deployment is that ITS architectures tend to be complex, not easily deployed in browsers without significant server-side processing, and not easily embedded in a learning management system (LMS). We present a case study in which a widely used ITS authoring tool suite, CTAT/TutorShop, was modified so that tutors can be embedded in MOOCs. Specifically, the inner loop (the example-tracing tutor engine) was moved to the client by reimplementing it in JavaScript, and the tutors were made compatible with the LTI e-learning standard. The feasibility of this general approach to ITS/MOOC integration was demonstrated with simple tutors in an edX MOOC �Data Analytics and Learning.� � Springer International Publishing Switzerland 2015.
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    SUPER-NATURALINSTRUCTIONS: Generalization via Declarative Instructions on 1600+ NLP Tasks
    (Association for Computational Linguistics (ACL), 2022) Wang, Y.; Mishra, S.; Alipoormolabashi, P.; Kordi, Y.; Mirzaei, A.; Arunkumar, A.; Ashok, A.; Dhanasekaran, A.S.; Naik, A.; Stap, D.; Pathak, E.; Karamanolakis, G.; Lai, H.G.; Purohit, I.; Mondal, I.; Anderson, J.; Kuznia, K.; Doshi, K.; Patel, M.; Pal, K.K.; Moradshahi, M.; Parmar, M.; Purohit, M.; Varshney, N.; Kaza, P.R.; Verma, P.; Puri, R.S.; Karia, R.; Sampat, S.K.; Doshi, S.; Mishra, S.; Reddy, S.; Patro, S.; Dixit, T.; Shen, X.; Baral, C.; Choi, Y.; Smith, N.A.; Hajishirzi, H.; Khashabi, D.
    How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce SUPER-NATURALINSTRUCTIONS, a benchmark of 1, 616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions-training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-INSTRUCT, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-INSTRUCT outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models. © 2022 Association for Computational Linguistics.
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    The beginning of a beautiful friendship? Intelligent tutoring systems and MOOCs
    (Springer Verlag service@springer.de, 2015) Aleven, V.; Sewall, J.; Popescu, O.; Xhakaj, F.; Chand, D.; Baker, R.; Wang, Y.; Siemens, G.; Rosé, C.; Gašević, D.
    A key challenge in ITS research and development is to support tutoring at scale, for example by embedding tutors in MOOCs. An obstacle to at-scale deployment is that ITS architectures tend to be complex, not easily deployed in browsers without significant server-side processing, and not easily embedded in a learning management system (LMS). We present a case study in which a widely used ITS authoring tool suite, CTAT/TutorShop, was modified so that tutors can be embedded in MOOCs. Specifically, the inner loop (the example-tracing tutor engine) was moved to the client by reimplementing it in JavaScript, and the tutors were made compatible with the LTI e-learning standard. The feasibility of this general approach to ITS/MOOC integration was demonstrated with simple tutors in an edX MOOC “Data Analytics and Learning.†© Springer International Publishing Switzerland 2015.