Personlized English reading sequencing based on learning portfolio analysis

Ting Ting Wu, Yueh Min Huang, Han Chieh Chao, Jong Hyuk Park

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Situated learning can enable learners to interact with real world objects, deepen their impression of learning material, and establish a connection between those objects and the material. For the practice of English reading, a proper integration of learner portfolios and local environments, along with a sound reading strategy, not only strengthens the students' understanding of language and reading comprehension, but also improves the effectiveness of language learning. Extended from the methodology of situational language, this study developed a ubiquitous English reading learning system based on RFID-based location-aware technology and a portfolio-centric article reading guide. Through RFID technology, the learning system can detect a learner's location; it then sends the learner highly situational and relevant English articles to read and study. Linking to the local environment, the English content becomes more perceivable, helping students achieve situational learning: "what is seen is read." Concurrent with the mechanism for situated reading articles, this work conducted an analysis of learner study portfolios incorporating the average learner ability to obtain parameters of standing point. Using the genetic algorithm approach, the system evaluates the difficulty of articles and their relationships. Through multiple evolutionary sequences, the system eventually provides the learner with appropriate and progressive articles for personalized study. The experiment conducted in both quantitative and qualitative evaluations revealed that this learning system can effectively assist learners as they immerse themselves in the study.

Original languageEnglish
Pages (from-to)248-263
Number of pages16
JournalInformation sciences
Volume257
DOIs
Publication statusPublished - 2014 Feb 1

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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