A Rule-Based Approach for Mining Creative Thinking Patterns from Big Educational Data

Numerous studies have established a correlation between creativity and intrinsic motivation to learn, with defined as the process of generating original valuable ideas, often by integrating perspectives from different fields. The field educational technology has shown growing interest in leveraging promote classroom, several demonstrating positive impact on learning outcomes. However, mining creative thinking patterns data remains challenging task, even proliferation research adaptive for education. This paper presents an initial effort towards formalizing knowledge developing domain-specific Knowledge Base that identifies key concepts, facts, assumptions essential identifying patterns. Our proposed pipeline involves modeling raw data, such assessments class activities, graph facilitate contextualization knowledge. We then leverage rule-based approach enable contextualized graph. To validate our approach, we evaluate it real-world datasets demonstrate how can instructors gain insights into students’ their activities assessment tasks.