TY - JOUR
T1 - Mining formative evaluation rules using web-based learning portfolios for web-based learning systems
AU - Chen, Chih Ming
AU - Hong, Chin Ming
AU - Chen, Shyuan Yi
AU - Liu, Chao Yu
PY - 2006
Y1 - 2006
N2 - Learning performance assessment aims to evaluate what knowledge learners have acquired from teaching activities. Objective technical measures of learning performance are difficult to develop, but are extremely important for both teachers and learners. Learning performance assessment using learning portfolios or web server log data is becoming an essential research issue in web-based learning, owing to the rapid growth of e-learning systems and real application in teaching scenes. The traditional summative evaluation by performing examinations or feedback forms is usually employed to evaluate the learning performance for both the traditional classroom learning and the web-based learning. However, summative evaluation only considers final learning outcomes without considering learning processes of learners. This study presents a learning performance assessment scheme by combining four computational intelligence theories, i.e., the proposed refined K-means algorithm, the neuro-fuzzy classifier, the proposed feature reduction scheme, and fuzzy inference, to identify the learning performance assessment rules using the web-based learning portfolios of an individual learner. Experimental results indicate that the evaluation results of the proposed scheme are very close to those of summative assessment results of grade levels. In other words, this scheme can help teachers to assess individual learners precisely utilizing only the learning portfolios in a web-based learning environment. Additionally, teachers can devote themselves to teaching and designing courseware since they save a lot of time in evaluating learning. This idea can be beneficially applied to immediately examine the learning progress of learners, and to perform interactively control learning for e-learning systems. More significantly, teachers could understand the factors influencing learning performance in a web-based learning environment according to the obtained interprtable learning performance assessment rules.
AB - Learning performance assessment aims to evaluate what knowledge learners have acquired from teaching activities. Objective technical measures of learning performance are difficult to develop, but are extremely important for both teachers and learners. Learning performance assessment using learning portfolios or web server log data is becoming an essential research issue in web-based learning, owing to the rapid growth of e-learning systems and real application in teaching scenes. The traditional summative evaluation by performing examinations or feedback forms is usually employed to evaluate the learning performance for both the traditional classroom learning and the web-based learning. However, summative evaluation only considers final learning outcomes without considering learning processes of learners. This study presents a learning performance assessment scheme by combining four computational intelligence theories, i.e., the proposed refined K-means algorithm, the neuro-fuzzy classifier, the proposed feature reduction scheme, and fuzzy inference, to identify the learning performance assessment rules using the web-based learning portfolios of an individual learner. Experimental results indicate that the evaluation results of the proposed scheme are very close to those of summative assessment results of grade levels. In other words, this scheme can help teachers to assess individual learners precisely utilizing only the learning portfolios in a web-based learning environment. Additionally, teachers can devote themselves to teaching and designing courseware since they save a lot of time in evaluating learning. This idea can be beneficially applied to immediately examine the learning progress of learners, and to perform interactively control learning for e-learning systems. More significantly, teachers could understand the factors influencing learning performance in a web-based learning environment according to the obtained interprtable learning performance assessment rules.
KW - Data Mining
KW - Learning Performance Assessment
KW - Web-based Learning
KW - Web-based Learning Portfolio
UR - http://www.scopus.com/inward/record.url?scp=33746874829&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33746874829&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:33746874829
SN - 1176-3647
VL - 9
SP - 69
EP - 87
JO - Educational Technology and Society
JF - Educational Technology and Society
IS - 3
ER -