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In this paper, we share our approach and the process for qualitative analysis of online video data recorded during an after-school robotics program that emphasized computational thinking (CT). Online research strategies may be necessary for various reasons such as when working with a geographically distributed research team, when conducting research with students in an online program, or when resources are inaccessible due to campus closures like those experienced during the COVID-19 pandemic. We followed a three-stage process during qualitative analysis of the videos that included planning and setup, online analysis of videos, and structural coding of memos to explore patterns across the data. Analysis was conducted with a combination of technologies including Google Drive for collaborative coding online and NVivo to collate and summarize findings. The methods and process we describe are readily applicable to other research studies that include video as part of the data set. 

Purpose This study aims to investigate the relationship between the personal traits and computational thinking skills of second graders within the context of robotics activities. Design/methodology/approach Through literature review, a research model and hypotheses were tested with 122 second graders after robotic activities. Findings The hypothesized model showed that learning preference, intrinsic motivation and self-efficacy were the main predictors of coding achievement and computational thinking skills, while no direct relationship was found between learning preference, intrinsic or extrinsic motivation. The final path analysis revealed that intrinsic and extrinsic motivation predict self-efficacy, self-efficacy predicts coding achievement and coding achievement predicts computational thinking skills. Another important finding was the strong impact of self-efficacy on coding achievement, as well as computational thinking skills. Results are interpreted with reference to implications for potential methods of improving computational thinking skills when using robotics in the lower grades in elementary schools. Research limitations/implications This study not only examined these relationships but also proposed, tested and built a research model containing a wide range of personal traits based on path analysis and multiple regression analysis, which, to the best of the researchers’ knowledge, has not been investigated in the current literature. Practical implications As reflected in the final research model, self-efficacy played an important role in impacting second grader’s coding achievement and computational thinking skills. Originality/value Few studies have investigated the various relationships in the context of robotics instruction in elementary schools as in this study. Given the increasing popularity of robotics education in elementary schools, the re-examination and identification of the pivotal role of self-efficacy in predicting second graders’ learning of coding and computational thinking skills have important implications for the implementation of robotics education. 

Robotics has been advocated as an emerging approach to engaging K-12 students in learning science, technology, engineering, and mathematics (STEM). This study examined the impacts of a project-basedSTEMintegrated robotics curriculumon elementary school students’ attitudes towardSTEMandperceivedlearninginanafterschoolsetting.Threeelementary schoolteachersand18fourth to sixth graders participatedinaneight-week-longprogram.Quantitativeandqualitativedatawere collectedandanalyzed,andshowedstudents’ attitudes toward math improved significantly at the end of the robotics curriculum. Three specific areas of perceived learning were identified, including STEM content learning and connection, engagement and perseverance, and development and challenge in teamwork. The findings also identified the opportunities and challenges in designing a STEMintegrated robotics afterschool curriculum for upper elementary school students. Implications for future research studies and curriculum design are discussed. 

Chi and colleagues have argued that some of the most challenging engineering concepts exhibit properties of emergent systems. However, students often lack a mental framework, or schema, for understanding emergence. Slotta and Chi posited that helping students develop a schema for emergent systems, referred to as schema training, would increase the understanding of challenging concepts exhibiting emergent properties.

We tested the effectiveness of schema training and explored the nature of challenging concepts from thermodynamics and heat transfer. We investigated if schema training could (a) repair misconceptions in advanced engineering students and (b) prevent them in beginning engineering students.

We adapted Slotta and Chi's schema training modules and tested their impact in two studies that employed an experimental design. Items from the Thermal and Transport Concept Inventory and expert‐developed multiple‐choice questions were used to evaluate conceptual understanding of the participants. The language used by students in their open‐ended explanations of multiple‐choice questions was also coded.

In both studies, students in the experimental groups showed larger gains in their understanding of some concepts—specifically in dye diffusion and microfluidics in Study One, and in the final test for thermodynamics in Study Two. But in neither study did students exhibit any gain in conceptual questions about heat transfer.

Our studies suggest the importance of examining the nature of the phenomena underlying the concepts being taught because the language used in instruction has implications for how students understand them. Therefore, we suggest that instructors reflect on their own understanding of the concepts.