Improving Learning and Teaching through Automated Short-Answer Marking

Abstract

Automated short-answer marking cannot "guarantee" 100% agreement between the marks generated by a software system and the marks produced separately by a human. This problem has prevented automated marking systems from being used in high-stake short-answer marking. Given this limitation, can an automated short-answer marking system have any practical application? This thesis describes how an automated short-answer marking system, called IndusMarker, can be effectively used to improve learning and teaching.The design and evaluation of IndusMarker are also presented in the thesis. IndusMarker is designed for factual answers where there is a clear criterion for answers being right or wrong. The system is based on structure matching, i.e. matching a pre-specified structure, developed via a purpose-built structure editor, with the content of the student's answer text. An examiner specifies the required structure of an answer in a simple purpose-designed language called Question Answer Markup Language (QAML). The structure editor ensures that users construct correct required structures (with respect to QAML's syntax and informal semantics) in a form that is suitable for accurate automated marking.

Date of Award	31 Dec 2010
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Christopher Harrison (Supervisor)