Analysis on the adaptive scaffolding learning path and the learning performance of e-learning

  • Lee C
  • Lee G
  • Leu Y
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Abstract

Existing instruction websites record learners’ portfolios, they only collect the browsing time and homepage information, without directly provide teachers with more data for further analyzing learner behaviors. Consequently, this investigation uses the learners’ portfolio left in the e-learning environment, and adopts “data mining” techniques to establish for each cluster of learners the most adaptive learning path pattern, which can provide a “scaffolding” to guide each cluster of learners. Using statistical methods, this investigation analyzes whether the navigational learning map of “scaffolding learning path (SLP)” can improve learning performance. This investigation discovers that among the three clusters of learners, the learners in the experimental group under the two clusters other than high-score cluster, after taking the “scaffolding learning path” as their navigational learning map, achieve more significant progress than the learners of comparative group. This implies that through the “scaffolding learning path,” the learning performance of most learners can be improved

Author-supplied keywords

  • Data mining
  • E-learning
  • Learning path
  • Learning portfolio
  • Scaffolding theory

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Authors

  • Gwo-Guang LeeNational Taiwan University of Science and Technology

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  • Chun Hsiung Lee

  • Yungho Leu

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