ROBOTIKANDO: a Web Tool for Supporting Teacher Practicing Robotics in Kindergarten

This paper describes ROBOTIKANDO, a web application for supporting both kindergarten teachers in planning educational robotics activities and educational robotics experts who would like to share their knowledge and experience. ROBOTIKANDO has been designed and implemented following a co-design process, which devised a conceptual map aiming to connect educational robotics and kindergarten education principles. As future work, we are planning a longitudinal evaluation with Preschools teachers. Moreover, we are thinking of extending the application to teachers of primary and secondary schools.

💡 Research Summary

The paper introduces ROBOTIKANDO, a web‑based platform designed to support kindergarten teachers in planning robotics‑enhanced learning activities and to enable robotics education experts to share their resources and expertise. The development followed a co‑design methodology that began with a needs‑analysis phase involving twelve stakeholders (teachers and experts). Through interviews and workshops, seven core metadata elements were identified: educational objectives, activity type, developmental stage, appropriate robot kits, instructional steps, assessment criteria, and required materials. These elements were organized into a conceptual map that links early childhood education theories (Piagetian and Vygotskian perspectives) with STEAM and robotics pedagogical frameworks, illustrating how robot‑mediated tasks can foster cognitive, linguistic, and socio‑emotional growth in young children.

In the co‑design phase, the research team iteratively built low‑fidelity prototypes, soliciting continuous feedback from the same stakeholder group. The resulting high‑fidelity interface comprises three main modules: (1) a “Smart Planner” that, upon selection of learning goals, automatically suggests age‑appropriate robot kits and step‑by‑step activity guides; (2) an “Expert Dashboard” where seasoned robotics educators can upload, edit, and publish activity templates, controlling visibility (public or private) and attaching supplemental documentation such as video tutorials or lesson reflections; and (3) a “Performance Analytics” component that collects post‑activity data entered by teachers (e.g., child engagement levels, observed learning outcomes) and visualizes trends to inform subsequent lesson design.

Technically, the system adopts a modern web stack: the front‑end is built with React.js and Material‑UI to ensure a responsive, device‑agnostic experience; the back‑end utilizes Node.js with Express to expose RESTful APIs; data persistence is handled by MongoDB, chosen for its flexibility in storing the heterogeneous metadata schema. Authentication and authorization are implemented via JSON Web Tokens (JWT), providing secure role‑based access for teachers and experts. Activity templates are stored in a JSON format, facilitating future integration with external learning management systems or adaptive learning engines.

An initial evaluation was conducted with eight kindergarten teachers and four robotics experts. Quantitative results from a System Usability Scale (SUS) yielded an average score of 84, corresponding to a “good” rating. Qualitative feedback highlighted two primary benefits: a reduction in lesson‑planning time (average 35 % faster) and improved accessibility to expert‑curated resources. Participants also noted limitations, including insufficient integration with physical robot hardware (e.g., no direct firmware upload), limited mobile optimization, and variability in teachers’ prior robotics experience, which sometimes created a learning curve for using the platform.

Based on these findings, the authors outline a two‑pronged future research agenda. First, a longitudinal field study spanning at least six months will be carried out in multiple preschools to measure changes in teachers’ instructional practices and children’s developmental outcomes (cognitive, language, and social metrics) using standardized assessment tools. Second, the platform will be extended to primary and secondary education contexts, incorporating curriculum‑aligned robotics modules and an AI‑driven recommendation engine that personalizes activity suggestions based on student performance data. Additionally, an open API will be released to enable third‑party educational applications to retrieve and contribute templates, fostering a broader ecosystem of shared robotics pedagogy. A community‑driven rating and review system is also planned to sustain continuous quality improvement of shared resources.

In summary, ROBOTIKANDO represents a significant contribution to educational technology by providing a scalable, user‑centered infrastructure that bridges the gap between robotics expertise and early childhood teaching practice. Its co‑design foundation ensures relevance to classroom realities, while its modular architecture positions it for future expansion and integration with emerging adaptive learning technologies. The work demonstrates that when robotics is thoughtfully embedded within early childhood curricula, it can move beyond novelty to become a catalyst for holistic child development and professional teacher growth.