Developing simulation-based computer assisted learning to correct students' statistical misconceptions based on cognitive conflict theory, using "correlation" as an example

Tzu Chien Liu

Research output: Contribution to journalArticle

18 Citations (Scopus)


Understanding and applying statistical concepts is essential in modern life. However, common statistical misconceptions limit the ability of students to understand statistical concepts. Although simulation-based computer assisted learning (CAL) is promising for use in students learning statistics, substantial improvement is still needed. For example, few simulation-based CALs have been developed to address statistical misconceptions, most of the studies about simulation-based CAL for statistics learning lacked theoretical backgrounds, and design principles for enhancing the effectiveness of dynamically linked multiple representations (DLMRs), which is the main mechanism of simulation-based CAL, are needed. Therefore, this work develops a simulation-based CAL prototype, Simulation Assisted Learning Statistics (SALS), to correct misconceptions about the statistical concept of correlation. The proposed SALS has two novel elements. One is the use of the design principles based on cognitive load and the other is application of the learning model based on cognitive conflict theory. Further, a formative evaluation is conducted by using a case study to explore the effects and limitations of SALS. Evaluation results indicate that despite the need for further improvement, SALS is effective for correcting statistical misconceptions. Finally, recommendations for future research are proposed.

Original languageEnglish
Pages (from-to)180-192
Number of pages13
JournalEducational Technology and Society
Issue number2
Publication statusPublished - 2010 Apr 1



  • Cognitive conflict theory
  • Cognitive load
  • Dynamically linked multiple representations
  • Learning model
  • Misconception
  • Simulation-based CAL

ASJC Scopus subject areas

  • Education
  • Sociology and Political Science
  • Engineering(all)

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