Description
Scaffolding, defined as support to help students perform and gain skill at complex tasks, has been integrated into instruction in a wide range of educational levels, disciplines, and problem-centered instructional approaches. In the underlying project, we aim to use machine learning to dynamically customize computer-based scaffolding. In this paper, we estimate the predictive power of machine learning classifiers with different types of input variables such as scaffolding characteristics, study characteristics, student characteristics, and study quality characteristics. Three classifiers (i.e., Naïve Bayes, support vector machines, and decision tree) can accurately predict with an averaged accuracy of over 50% the effectiveness of scaffolding using only scaffolding characteristics information. In addition, Decision Tree classifier leads to 91.34% accuracy when scaffolding characteristics and study characteristics are classified. Our findings from this preliminary experiment provide a foundation to set up initial parameters that a smart learning system can utilize to provide individualized and customized scaffolding.
Recommended Citation
Kim, Nam Ju; Belland, Brian; and Kim, Yong Seog, "Data Mining Meta-analysis Coding to Develop Smart Learning Systems that Dynamically Customize Scaffolding" (2017). AMCIS 2017 Proceedings. 30.
https://aisel.aisnet.org/amcis2017/ISEducation/Presentations/30
Data Mining Meta-analysis Coding to Develop Smart Learning Systems that Dynamically Customize Scaffolding
Scaffolding, defined as support to help students perform and gain skill at complex tasks, has been integrated into instruction in a wide range of educational levels, disciplines, and problem-centered instructional approaches. In the underlying project, we aim to use machine learning to dynamically customize computer-based scaffolding. In this paper, we estimate the predictive power of machine learning classifiers with different types of input variables such as scaffolding characteristics, study characteristics, student characteristics, and study quality characteristics. Three classifiers (i.e., Naïve Bayes, support vector machines, and decision tree) can accurately predict with an averaged accuracy of over 50% the effectiveness of scaffolding using only scaffolding characteristics information. In addition, Decision Tree classifier leads to 91.34% accuracy when scaffolding characteristics and study characteristics are classified. Our findings from this preliminary experiment provide a foundation to set up initial parameters that a smart learning system can utilize to provide individualized and customized scaffolding.