| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 90 === Because of the booming of Internet, teaching is no longer a traditional school instruction. Besides conventional classroom instruction, it is also a good way to learn through Internet. The development of technology changes learning ways and teaching styles, and new technologies should adapt themselves to the new teaching methods. However, no matter whichever teaching styles are adopted, there are always to yield learners that cannot accommodate them, especially in such uncertain environment, Internet. When situated in an on-line collaboration learning environment, teachers could not utilize traditional index information (e.g. attendance rate, participation degree, assignment, and test scores etc.) for achievement measuring and find out learners who have learning obstacles. They can only keep track huge learners’ learning history records promptly through system functions, and exploit such records to be the basis for evaluating learners’ performance and differentiating learners who have learning obstacles.
Through this research, we first proposed a three-stage information analysis mode and filtered meaningful learners’ learning history records for generating learning history database for learners and teams. Next, we explore measurable learners’ interaction indexes from history information database and find out learners who have learning obstacles. Finally, through some qualitative interviews and the above database, we recognize some critical factors that cause learning obstacles and obtain propositions as follow:
1. From early interaction index such as “ranking of assignment hand in”, “ranking of browsing times”, “ranking of document upload”, “ranking of times to be browsed”, and “ranking of publishing times”, we can find out whether students have obstacles of information accomplishment or not.
2. From early and middle interaction indexes such as “ranking of browsing times”, “ranking of times to be browsed”, and “ranking of publishing times”, we can find out whether students have obstacles of role identification or not.
3. From early and middle interaction indexes such as “ranking of assignment hand in”, “ranking of browsing times”, “ranking of document upload”, “ranking of response times”, and “ranking of publishing times”, we can detect whether students have obstacles to the information richness of web sites or not.
4. Through most parts of interaction indexes, we can effectively differentiate students that have obstacles due to economic considerations.
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