The main goal of this study was to propose an application using AI and data analysis to intrinsically motivate and prepare students in a flipped classroom approach.
Methods: The study involved conducting a survey of 140 postgraduate students at FHNW School of Business about their classroom preparation habits, as well as a literature review on the use of AI and analytics in education. Based on these findings, the researchers propose a conceptual design for an AI-powered application to support classroom preparation.
Key Findings:
- Only 34.7% of surveyed students regularly prepared for classes, with lack of time being the main reason for not preparing.
- Existing tools for classroom preparation often focus on extrinsic motivation factors like grades and assessments.
- AI chatbots have shown promise in engaging students and supporting learning.
- Conversations and interactions are important for knowledge exchange and building intrinsic motivation.
Implications: The proposed AI application could enhance student preparation and engagement while providing teachers with insights to adapt their teaching. This approach leverages AI to build intrinsic motivation rather than relying solely on extrinsic factors.
Limitations: The study is primarily conceptual at this stage, without empirical testing of the proposed application. The sample size for the student survey was relatively small.
Future Directions: The researchers plan to further develop the requirements and design of the application, conduct expert interviews with lecturers, and build and evaluate prototypes in actual classroom settings.
Title and Authors: "Enhance Classroom Preparation for Flipped Classroom using AI and Analytics" by Prajakta Diwanji, Knut Hinkelmann, and Hans Friedrich Witschel
Published On: Not explicitly stated in the provided text
Published By: Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018)