TY - GEN
T1 - Extending LMS to Support IRT-Based Assessment Test Calibration
AU - Fotaris, Panagiotis
AU - Mastoras, Theodoros
AU - Mavridis, Ioannis
AU - Manitsaris, Athanasios
N1 - The final authenticated version is available online at https://doi.org/10.1007/978-3-642-13166-0
PY - 2010/5/19
Y1 - 2010/5/19
N2 - Developing unambiguous and challenging assessment material for measuring educational attainment is a time-consuming, labor-intensive process. As a result Computer Aided Assessment (CAA) tools are becoming widely adopted in academic environments in an effort to improve the assessment quality and deliver reliable results of examinee performance. This paper introduces a methodological and architectural framework which embeds a CAA tool in a Learning Management System (LMS) so as to assist test developers in refining items to constitute assessment tests. An Item Response Theory (IRT) based analysis is applied to a dynamic assessment profile provided by the LMS. Test developers define a set of validity rules for the statistical indices given by the IRT analysis. By applying those rules, the LMS can detect items with various discrepancies which are then flagged for review of their content. Repeatedly executing the aforementioned procedure can improve the overall efficiency of the testing process.
AB - Developing unambiguous and challenging assessment material for measuring educational attainment is a time-consuming, labor-intensive process. As a result Computer Aided Assessment (CAA) tools are becoming widely adopted in academic environments in an effort to improve the assessment quality and deliver reliable results of examinee performance. This paper introduces a methodological and architectural framework which embeds a CAA tool in a Learning Management System (LMS) so as to assist test developers in refining items to constitute assessment tests. An Item Response Theory (IRT) based analysis is applied to a dynamic assessment profile provided by the LMS. Test developers define a set of validity rules for the statistical indices given by the IRT analysis. By applying those rules, the LMS can detect items with various discrepancies which are then flagged for review of their content. Repeatedly executing the aforementioned procedure can improve the overall efficiency of the testing process.
KW - e-learning
KW - Assessment Test Calibration
KW - Computer Aided Assessment
KW - Item Analysis
KW - Item Response Theory
KW - Learning Management System
U2 - 10.1007/978-3-642-13166-0
DO - 10.1007/978-3-642-13166-0
M3 - Conference contribution with ISSN or ISBN
SN - 9783642131653
VL - 73
T3 - Communications in Computer and Information Science
SP - 534
EP - 543
BT - 1st International Conference, Tech-education 2010
PB - Springer-Verlag
CY - Berlin Heidelberg, Germany
T2 - 1st International Conference, Tech-education 2010
Y2 - 19 May 2010
ER -