Undergraduate Robotics Education with General Instructors using a Student-Centered Personalized Learning Framework

TL;DR

The paper addresses instructor shortages and diverse student backgrounds that impede scalable undergraduate robotics education. It proposes a student-centered personalized learning framework delivered through the ISPeL platform, using topic-based authoring and domain-topic dependency graphs to enable general instructors to teach robotics. Instructors act as facilitators while learners navigate personalized paths with pre/post assessments and dashboards, supported by a chatbot and curated FAQs. User studies with over 100 students indicate ISPeL enhances perceived organization, supports understanding of topic interconnections, and shows strong potential for broader adoption in higher education.

Abstract

Recent advancements in robotics, including applications like self-driving cars, unmanned systems, and medical robots, have had a significant impact on the job market. On one hand, big robotics companies offer training programs based on the job requirements. However, these training programs may not be as beneficial as general robotics programs offered by universities or community colleges. On the other hand, community colleges and universities face challenges with required resources, especially qualified instructors, to offer students advanced robotics education. Furthermore, the diverse backgrounds of undergraduate students present additional challenges. Some students bring extensive industry experiences, while others are newcomers to the field. To address these challenges, we propose a student-centered personalized learning framework for robotics. This framework allows a general instructor to teach undergraduate-level robotics courses by breaking down course topics into smaller components with well-defined topic dependencies, structured as a graph. This modular approach enables students to choose their learning path, catering to their unique preferences and pace. Moreover, our framework's flexibility allows for easy customization of teaching materials to meet the specific needs of host institutions. In addition to teaching materials, a frequently-asked-questions document would be prepared for a general instructor. If students' robotics questions cannot be answered by the instructor, the answers to these questions may be included in this document. For questions not covered in this document, we can gather and address them through collaboration with the robotics community and course content creators. Our user study results demonstrate the promise of this method in delivering undergraduate-level robotics education tailored to individual learning outcomes and preferences.

Figures (3)

• Figure 1: Proposed student-centered personalized learning framework: Students are placed at the center, with instructors required to assist rather than lead them. Course materials are developed by faculties from ECU and UNR, encompassing reading materials, hands-on exercises, and dependency graphs (see Figure 2 for more details). Instructors must ensure that students make progress in every class and gather questions from them. Students can work on different topics within the same classroom with different required hardware. Computationally intensive tasks will be carried out on a personalized learning platform server named ISPeL. Additionally, ISPeL will gather students' performance data and track their learning progress.
• Figure 2: Course domain and domain dependency graph. Domain 1 will be studied first; Domain 5 cannot be reached until a student learns Domains 1, 2, and 3. A student can choose a unique path to learn the required course content. Each domain can include multiple topics, e.g., Domain 3 contains four topics.
• Figure 3: An instructor can choose topics (i.e., select area, see left top corner), select topic components, and design how to connect topic components (see middle column) with a book chapter and sub-chapter style to define the dependencies. When the “Apply” button is clicked, a dependency graph will be generated automatically.