TY - GEN
T1 - Sequencing strategy with learning portfolio analysis for personalized English reading
AU - Wu, Ting Ting
AU - Sung, Tien Wen
AU - Huang, Yueh Min
AU - Chao, Han Chieh
AU - Park, Jong Hyuk
AU - Yang, Chu Sing
PY - 2010
Y1 - 2010
N2 - Learning English is extremely popular in non-native English speaking countries, thus, the development of useful computer assisted learning programs and tools to support effective English learning are a critical issue in the educational field of learning the English-language. With the rapid growth of Internet technologies and easy access to information, the field of digital learning has stimulated innovation and diversified developments, and the emergence of guidance systems provide learners with adaptive learning paths that promote learning performance during learning processes. However, most guidance systems neglect to consider the level of difficulty, and recommended materials are proprietary in nature, and thus, do not fully utilize portfolio features of learners incorporated into personalized learning services when implementing personalized guidance. Therefore, this study proposed a guidance mechanism which utilizes the data of a learning portfolio to evaluate guidance parameters, and simultaneously considers information regarding the degree of relational reading, reading difficulty, and the average ability of learners, which can be entered into a genetic algorithm to construct a near optimal reading sequence during learning activities. Experiments were conducted to compare the free browsing reading mode without the guidance of a personalized reading path, as in most present systems. Based on the experimental results, this proposed guidance mechanism generates a high quality and concise learning path for individual learners, and the reading learning system results in an efficient and effective learning performance to promote learning motivation through appropriate learning paths.
AB - Learning English is extremely popular in non-native English speaking countries, thus, the development of useful computer assisted learning programs and tools to support effective English learning are a critical issue in the educational field of learning the English-language. With the rapid growth of Internet technologies and easy access to information, the field of digital learning has stimulated innovation and diversified developments, and the emergence of guidance systems provide learners with adaptive learning paths that promote learning performance during learning processes. However, most guidance systems neglect to consider the level of difficulty, and recommended materials are proprietary in nature, and thus, do not fully utilize portfolio features of learners incorporated into personalized learning services when implementing personalized guidance. Therefore, this study proposed a guidance mechanism which utilizes the data of a learning portfolio to evaluate guidance parameters, and simultaneously considers information regarding the degree of relational reading, reading difficulty, and the average ability of learners, which can be entered into a genetic algorithm to construct a near optimal reading sequence during learning activities. Experiments were conducted to compare the free browsing reading mode without the guidance of a personalized reading path, as in most present systems. Based on the experimental results, this proposed guidance mechanism generates a high quality and concise learning path for individual learners, and the reading learning system results in an efficient and effective learning performance to promote learning motivation through appropriate learning paths.
UR - http://www.scopus.com/inward/record.url?scp=77958156323&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77958156323&partnerID=8YFLogxK
U2 - 10.1109/HUMANCOM.2010.5563359
DO - 10.1109/HUMANCOM.2010.5563359
M3 - Conference contribution
AN - SCOPUS:77958156323
SN - 9781424475704
T3 - 2010 3rd International Conference on Human-Centric Computing, HumanCom 2010
BT - 2010 3rd International Conference on Human-Centric Computing, HumanCom 2010
T2 - 2010 3rd International Conference on Human-Centric Computing, HumanCom 2010
Y2 - 11 August 2010 through 13 August 2010
ER -