Assessment Feedback supporting E-learning

Abstract

With the rapidly development of distance learning and the XML (Extensible Markup Language) technology, metadata becomes an important item in an e-learning system. Today, many distance learning standards such as SCORM, AICC CMI, IEEE LTSC Learning Object Meta-data (LOM), and IMS Learning Resource Metadata XML Binding Specification, use metadata to tag learning materials, shareable content objects, and learning resources. However, most metadata is used to define learning materials and test problems. Few metadata is dedicated for assessment in learning. In this paper, we proposed an assessment metadata model for e-learning operations. With the support from the assessment metadata, we can collect information at the question cognition level, Item Difficulty Index, Item Discrimination Index, questionnaire style, and question style. The assessment analysis model provides individual questions, summary of test results, and analytical suggestions. The suggestions and results can tell teachers why a question is not suitable and how to correct it. Teachers can see the test result analysis and fix some problematic questions. With the cognition level analysis, teachers can correct their cognition level to avoid missing items in teaching. The mechanism developed also suggests to an e-learning system, to adaptive learning content and test to individual learners, as well as provides good advice to the teachers.