The Experts below are selected from a list of 1266 Experts worldwide ranked by ideXlab platform
Yueh-min Huang - One of the best experts on this subject based on the ideXlab platform.
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Web Intelligence - Real-Time Learning Behavior Mining for e-Learning
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Juei-nan Chen, Yu-lin Jeng, Yueh-min HuangAbstract:Over the last years, we have witnessed an explosive growth of e-Learning. More and more Learning contents have been published and shared over the Internet. Therefore, how to progress an efficient Learning process becomes a critical issue. This paper proposes a sequential mining algorithm to analyze Learning behaviors for discovering frequent sequential patterns. By these patterns, we can provide suggestions for learners to select their Interest Learning contents. Different to other sequential mining algorithms, this study provides an incrementally method to analyze Learning sequencing. More specifically, the mining algorithm in this paper can provide real-time analysis, and then report to learners for selecting Learning contents more easily.
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Real-time Learning behavior mining for e-Learning
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Juei-nan Chen, Yu-lin Jeng, Yueh-min HuangAbstract:Over the last years, we have witnessed an explosive growth of e-Learning. More and more Learning contents have been published and shared over the Internet. Therefore, how to progress an efficient Learning process becomes a critical issue. This paper proposes a sequential mining algorithm to analyze Learning behaviors for discovering frequent sequential patterns. By these patterns, we can provide suggestions for learners to select their Interest Learning contents. Different to other sequential mining algorithms, this study provides an incrementally method to analyze Learning sequencing. More specifically, the mining algorithm in this paper can provide real-time analysis, and then report to learners for selecting Learning contents more easily.
Juei-nan Chen - One of the best experts on this subject based on the ideXlab platform.
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Web Intelligence - Real-Time Learning Behavior Mining for e-Learning
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Juei-nan Chen, Yu-lin Jeng, Yueh-min HuangAbstract:Over the last years, we have witnessed an explosive growth of e-Learning. More and more Learning contents have been published and shared over the Internet. Therefore, how to progress an efficient Learning process becomes a critical issue. This paper proposes a sequential mining algorithm to analyze Learning behaviors for discovering frequent sequential patterns. By these patterns, we can provide suggestions for learners to select their Interest Learning contents. Different to other sequential mining algorithms, this study provides an incrementally method to analyze Learning sequencing. More specifically, the mining algorithm in this paper can provide real-time analysis, and then report to learners for selecting Learning contents more easily.
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Real-time Learning behavior mining for e-Learning
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Juei-nan Chen, Yu-lin Jeng, Yueh-min HuangAbstract:Over the last years, we have witnessed an explosive growth of e-Learning. More and more Learning contents have been published and shared over the Internet. Therefore, how to progress an efficient Learning process becomes a critical issue. This paper proposes a sequential mining algorithm to analyze Learning behaviors for discovering frequent sequential patterns. By these patterns, we can provide suggestions for learners to select their Interest Learning contents. Different to other sequential mining algorithms, this study provides an incrementally method to analyze Learning sequencing. More specifically, the mining algorithm in this paper can provide real-time analysis, and then report to learners for selecting Learning contents more easily.
Yu-lin Jeng - One of the best experts on this subject based on the ideXlab platform.
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Web Intelligence - Real-Time Learning Behavior Mining for e-Learning
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Juei-nan Chen, Yu-lin Jeng, Yueh-min HuangAbstract:Over the last years, we have witnessed an explosive growth of e-Learning. More and more Learning contents have been published and shared over the Internet. Therefore, how to progress an efficient Learning process becomes a critical issue. This paper proposes a sequential mining algorithm to analyze Learning behaviors for discovering frequent sequential patterns. By these patterns, we can provide suggestions for learners to select their Interest Learning contents. Different to other sequential mining algorithms, this study provides an incrementally method to analyze Learning sequencing. More specifically, the mining algorithm in this paper can provide real-time analysis, and then report to learners for selecting Learning contents more easily.
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Real-time Learning behavior mining for e-Learning
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 2005Co-Authors: Juei-nan Chen, Yu-lin Jeng, Yueh-min HuangAbstract:Over the last years, we have witnessed an explosive growth of e-Learning. More and more Learning contents have been published and shared over the Internet. Therefore, how to progress an efficient Learning process becomes a critical issue. This paper proposes a sequential mining algorithm to analyze Learning behaviors for discovering frequent sequential patterns. By these patterns, we can provide suggestions for learners to select their Interest Learning contents. Different to other sequential mining algorithms, this study provides an incrementally method to analyze Learning sequencing. More specifically, the mining algorithm in this paper can provide real-time analysis, and then report to learners for selecting Learning contents more easily.
Dagmar Berndorff - One of the best experts on this subject based on the ideXlab platform.
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Interest Learning and the psychological processes that mediate their relationship
Journal of Educational Psychology, 2002Co-Authors: Mary Ainley, Suzanne Hidi, Dagmar BerndorffAbstract:Although influences of Interest on Learning are well documented, mediating processes have not been clarified. The authors investigated how individual and situational Interest factors contribute to topic Interest and text Learning. Traditional self-report measures were combined with novel interactive computerized methods of recording cognitive and affective reactions to science and popular culture texts, monitoring their development in real time. Australian and Canadian students read 4 expository texts. Both individual Interest variables and specific text titles influenced topic Interest. Examination of processes predictive of text Learning indicated that topic Interest was related to affective response, affect to persistence, and persistence to Learning. Combining self-rating scales with dynamic measures of student activities provided new insight into how Interest influences Learning.
Mary Ainley - One of the best experts on this subject based on the ideXlab platform.
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Students, tasks and emotions: Identifying the contribution of emotions to students' reading of popular culture and popular science texts
Learning and Instruction, 2005Co-Authors: Mary Ainley, Matthew Corrigan, Nicholas RichardsonAbstract:Abstract In this investigation young adolescent students (N = 181) engaged in an interactive computer reading task. The aim was to explore sequences of students' affective responses to expository texts by identifying their character, intensity and their relationship with further text processing. Affective responses were measured using probes consisting of face icons and were recorded immediately prior to students' choices about whether to continue reading. Emotions reported and their intensity influenced further reading of the texts. Findings support a model linking topic Interest, affect and persistence [Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, Learning, and the psychological processes that mediate their relationship. Journal of Educational Psychology, 94(3), 545–561] and demonstrate the role of Interest in the motivation of text processing.
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Interest Learning and the psychological processes that mediate their relationship
Journal of Educational Psychology, 2002Co-Authors: Mary Ainley, Suzanne Hidi, Dagmar BerndorffAbstract:Although influences of Interest on Learning are well documented, mediating processes have not been clarified. The authors investigated how individual and situational Interest factors contribute to topic Interest and text Learning. Traditional self-report measures were combined with novel interactive computerized methods of recording cognitive and affective reactions to science and popular culture texts, monitoring their development in real time. Australian and Canadian students read 4 expository texts. Both individual Interest variables and specific text titles influenced topic Interest. Examination of processes predictive of text Learning indicated that topic Interest was related to affective response, affect to persistence, and persistence to Learning. Combining self-rating scales with dynamic measures of student activities provided new insight into how Interest influences Learning.