TY - JOUR

T1 - Developing simulation-based computer assisted learning to correct students' statistical misconceptions based on cognitive conflict theory, using "correlation" as an example

AU - Liu, Tzu Chien

PY - 2010/4

Y1 - 2010/4

N2 - 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.

AB - 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.

KW - Cognitive conflict theory

KW - Cognitive load

KW - Dynamically linked multiple representations

KW - Learning model

KW - Misconception

KW - Simulation-based CAL

UR - http://www.scopus.com/inward/record.url?scp=77954269075&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77954269075&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:77954269075

VL - 13

SP - 180

EP - 192

JO - Educational Technology and Society

JF - Educational Technology and Society

SN - 1436-4522

IS - 2

ER -