InPHYNet: Leveraging attention-based multitask recurrent networks for multi-label physics text classification
Authors:
- Vishaal UdandaraoVUVishaal Udandarao
- Abhishek AgarwalAAAbhishek Agarwal
- Anubha GuptaAGAnubha Gupta
- Tanmoy ChakrabortyTCTanmoy Chakraborty
- 1 January 2021
- journal article
- research article
- Published by Elsevier in Knowledge-Based Systems
- Vol. 211
Abstract
No abstract availableKeywords
Funding Information
- (SERB, DST, India)
This publication has 24 references indexed in Scilit:
- BEETLE II: Deep Natural Language Understanding and Automatic Feedback Generation for Intelligent Tutoring in Basic Electricity and ElectronicsInternational Journal of Artificial Intelligence in Education, 2014
- An investigation of course-level factors as predictors of online STEM course outcomesComputers & Education, 2014
- A system for formative assessment and monitoring of students' progressComputers & Education, 2014
- The automatic assessment of free text answers using a modified BLEU algorithmComputers & Education, 2011
- Student perceptions of computer scienceACM SIGCSE Bulletin, 2008
- Educational Neuroscience: Defining a New Discipline for the Study of Mental RepresentationsMind, Brain, and Education, 2007
- ML-KNN: A lazy learning approach to multi-label learningPattern Recognition, 2007
- Transforming Physics EducationPhysics Today, 2005
- Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics coursesAmerican Journal of Physics, 1998
- The initial knowledge state of college physics studentsAmerican Journal of Physics, 1985