@inproceedings{c6a731da534049c6a15757d35660f19a,
title = "Automated grading of short literal comprehension questions",
abstract = "This paper presents an algorithm for automated marking of natural language responses of literal comprehension questions. It highlights the crucial differences between the content and style of questions for assessing different levels of reading comprehension, and argues that the literal question type can be effectively handled by largely syntax and structural based algorithm. The efficient algorithm compares student answers with model answers along a token-based approach. The small semantic variations in student answers made the omission of corpus-based approaches more sensible. The algorithm was evaluated with real data obtained from a local secondary school, and the performance was found to be very promising.",
keywords = "Automated grading, Natural language processing, Parsing, Part-of-speech tagging, Reading comprehension",
author = "Lui, \{Andrew Kwok Fai\} and Lee, \{Lap Kei\} and Lau, \{Hiu Wai\}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; 2nd International Conference on Technology in Education: Technology-Mediated Proactive Learning, ICTE 2015 ; Conference date: 02-07-2015 Through 04-07-2015",
year = "2015",
doi = "10.1007/978-3-662-48978-9\_23",
language = "English",
isbn = "9783662489772",
series = "Communications in Computer and Information Science",
pages = "251--262",
editor = "Wong, \{Tak Lam\} and Jeanne Lam and Ng, \{Kwan Keung\} and Wang, \{Fu Lee\} and Cheung, \{Simon K.S.\} and Li, \{Kam Cheong\}",
booktitle = "Technology in Education",
}