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|Title: ||CAA of Short Non-MCQ Answers|
|Authors: ||Callear, David|
|Keywords: ||computer-assisted assessment|
intelligent and expert systems
natural language processing
structured knowledge representation schemes
|Issue Date: ||2001|
|Publisher: ||© Loughborough University|
|Citation: ||CALLEAR, JERRAMS-SMITH and SOH, 2001. CAA of Short Non-MCQ Answers. Proceedings of the 5th CAA Conference, Loughborough: Loughborough University|
|Abstract: ||This paper presents a new approach for the computer-assisted assessment (CAA)
of non- multiple choice questions (Non-MCQ) type and short answers given by
students. The technique is developed for the assessment of text contents of free text
answers to questions of factual disciplines.
The Automated Text Marker (ATM) prototype automatically breaks down an expertly
written model answer, to a closed-ended question, into the smallest viable unit of
concepts with their dependencies accounted for by automatically tagging the
resultant concepts and their dependencies with numbers. The same process is
applied to each student’s answer and the resultant concepts and their
dependencies are then pattern-matched with those of the model examiner’s answer.
Two main components of ATM are the syntax and semantics analysers. In a
prototype test, ATM provides for one score for the grammars and the other for the
The focus of this paper is on semantic analysis of text contents since the syntactic
analysis of sentences has been generally and successfully automated.
Various examples of sentences of different factual disciplines such as those of
Prolog programming, psychology and biology-related fields are analysed.
Justifications for these analyses of sentences are provided and the corresponding
prototype tests are conducted. The expected results from prototyping using ATM are
obtained, indicating the reliability and feasibility of this new approach for the detailed
assessment of text contents incorporating word order. Work is currently underway for building a larger and more comprehensive ATM
system for analysing and assessing text components larger than sentences such as
paragraphs and whole text passages. Unlike existing computerised assessment
systems, ATM is not a predictive system, although, like a human assessor, it is not
|Description: ||This is a conference paper.|
|Appears in Collections:||CAA Conference|
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