Developing and Validating a High School Version of the Robotics Motivated Strategies for Learning Questionnaire

Introduction

Educational technologies are becoming increasingly critical. Among these, edutainment artifacts provide students with a pleasurable and meaningful learning experience through hands-on training using programming bricks (Resnick, Martin, Sargent et al., 1996). Educational robots are a highly popular form of edutainment artifact used in a wide range of subjects and classroom contexts. Previous studies have found that learners are highly motivated and satisfied with courses based on Lego design (Liu, Lin, & Chang, 2010) and other Lego-accompanied learning environments (Wei, Hung, Lee et al., 2011). In terms of learning performance, observational evidence has shown that learners can acquire basic programming skills through competition-based Lego games (Atmatzidou, Markelis, & Demetriadis, 2008), requiring the identification of factors influencing system performance in a project-based robotics environment (Barak & Zadok, 2009). In addition, when integrating robotics into schooling, elementary students exhibit active learning behavior with instructional humanoid robots, such as raising their hands to answer questions and greeting robots loudly in English class (Chang, Lee, Chao et al., 2010). Besides, the university students in South Africa also have well responses to problem solving, critical thinking, and collaborative learning in an introductory programming course via Lego Mindstorm-NXT (Chetty, 2015). Different from the perspective of the pedagogical tool, students also can improve their English Verbs using via teaching the robot about the care-receiving (Tanaka & Matsuzoe, 2012). These studies have indicated that robots could be effective elements in enhancing learning motivation and learning performance.

Though instructors can integrate different kinds of technologies to academic learning, researchers still explore some relevant factors of students’ effective learning. In traditional classroom context, seventh-grade students’ scores in MSLQ survey indicated that self-regulation, self-efficacy and test anxiety were the best predictors of English and science performance (Pintrich & DeGroot, 1990). Today, many children regard educational robots as toys and as a way to access high-level technology (Liu, 2010), but few researchers have studied the actual psychological status of those learning robotics. Researchers in robotics learning domain also have to consider which motivational or cognitive components would influence students’ engagement in their learning processes. Thus, such an understanding would enable instructors to optimize their instructional design and approaches according to the motivation of students and the various aspects of self-regulated learning.

The aim of this study was to design an instrument for measuring personal learning motivation and strategies use associated with robotic activities in order to verify the reliability and validity of the robotic learning scales. Currently, two inventories are used as the most common measures of self-regulated learning: the Learning and Study Strategies Inventory (LASSI) and the Motivated Strategies for Learning Questionnaire (MSLQ), in the fields of education and psychology, respectively (Magno, 2011).

LASSI is commonly used to measure general aspects of self-regulated learning; however, MSLQ is used to address specific characteristics based on Social Cognitive Theory (Dunken & McKeachie, 2005). According to advocates of reciprocal determinism, learning is actively influenced by ones’ own cognition, behavior, and existing social context (Bandura, 1986), making MSLQ more flexible in the assessment of the dynamic cognitive processes associated with a wide variety of subjects. Establishing a framework to describe the relationships among various factors in self-regulated learning system is crucial. MSLQ has helped to establish the existence of a relationship between self-regulated abilities and academic performance in class and at the course level (Pintrich & DeGroot, 1990; Pintrich, et al., 1993). For this reason, we applied MSLQ to the learning content of robotics courses to investigate the structural factors associated with robotics in the learning domain.