Categorise

1
Email r.m.foster_at_ wlv.ac.uk Web
http//www.bobfoster.co.uk
RESEARCH INSTITUTE IN INFORMATION AND LANGUAGE
PROCESSING (RIILP)
Define
CONTROLLED SPECIFIC LEARNING OBJECTIVES
Research Subject Multiple Choice Question (MCQ)
Generation system enhancement through
disambiguation of source documents.
REMEMBERING
Recall
Identify
Introduction The CSLO standard has been created
in an attempt to improve the output from a system
1,2 which automatically generates Multiple
Choice Question (MCQ) test items from source
documents. The CSLO standard combines the
benefits of the general construction framework
for a Specific Learning Objective (SLO) defined
in the Theory of Criterion Reference Instruction
3.5 with the relevant features from existing
Controlled Languages (CLs) including AECMA
Simplified English 4.
Recognise
Compare
UNDERSTANDING
Distinguish
Step 1 - Specific Learning Objectives Each
paragraph of the source document is re-written to
ensure that the components of a Specific Learning
Objective as defined in the Criterion Referenced
Instruction theory of R. Mager 3 are
specifically and unambiguously stated.
Modify
Step 2 - Controlled Language The principles from
several Controlled Language (CL) standards have
combined to form the CSLO definition. The current
CL Wikipedia article distinguishes between two
purposes of CLs Knowledge representation and
Human readability. CSLOs have been defined to
address both requirements and so CLs from both
sides of this divide have contributed rules that
are included in the CSLO standard. These include
AECMA Simplified English 4
Apply
DOING
Use
Justify
Analyse
How will CSLOs be used? CSLOs will first be used
to define the output of an algorithm for
pre-processing source documents before they are
operated upon by a Multiple Choice Question (MCQ)
Item generator 1,2
ANALYSING
Categorise
Hypothesise
Pre-processed Source Documents for Multiple
Choice Question Test Item Generator 1,2
Compose
References 1 Mitkov, R., and L. A. Ha. 2003.
Computer-Aided Generation of Multiple-Choice
Tests. In Proceedings of HLT-NAACL 2003 Workshop
on Building Educational Applications Using
Natural Language Processing, pp. 17-22. Edmonton,
Canada. 2 Mitkov, R., L. A. Ha, and N.
Karamanis. 2006. A computer-aided environment for
generating multiple-choice test items. Natural
Language Engineering 12(2) 177-194. 3 Mager,
R. (1975). Preparing Instructional Objectives
(2nd Edition). Belmont, CA Lake Publishing Co.
4 AECMA Simplified English, http//www.aecma.or
g/Publications/SEnglish/senglish.htm
2003-04-16 5 Bloom Benjamin S. and David R.
Krathwohl, (1956). Taxonomy of Educational
Objectives The Classification of Educational
Goals, by a committee of college and university
examiners. Handbook I Cognitive Domain. New
York Longman, Green.
Plan
CREATING
EVALUATING
Assess
Judge
Estimate