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

Abstract

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

Predictive Analysis Application for Academic Student Advancement in Secondary Education Institutions

“…In order to ease educational challenges, professionals must recognize the impact of big data in education. Modern cultures require training in creative thinking abilities in order to keep up with technological advancements, knowledge growth, and the constant flow of information [12]. It's all about devising tools for analyzing the precise types of knowledge that emerge from educational settings and using these approaches to better understand what students learn in such contexts [13].…”

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

“…To efficiently manage infinitely increasing data within limited storage space, graph compression is essential, allowing for the efficient use of storage space to accommodate increasing amounts of data [17][18][19][20][21][22]. Techniques that incorporate graph pattern mining methods also exist [23][24][25][26][27][28]. Generally, graph compression is performed using graph mining techniques to select frequently occurring sub-graphs as reference patterns, recording changes that occur in these reference patterns.…”

“…These approaches involve a time-consuming compression process, especially imposing constraints on immediate processing in a graph stream environment. High accuracy and compression rates are provided by pattern extraction techniques, such as those in [14][15][16][23][24][25][26]; however, their long processing times during graph compression also render them difficult to be applied in real-time environments. As a solution to these issues, research on the use of provenance has been proposed [29][30][31].…”

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

“…To efficiently manage infinitely increasing data within a limited storage space, graph compression is essential, allowing for the efficient use of storage space to accommodate increasing amounts of data [17][18][19][20][21][22]. Techniques that incorporate graph pattern mining methods also exist [23][24][25][26][27][28]. Generally, graph compression is performed using graph mining techniques to select frequently occurring sub-graphs as reference patterns and recording changes that occur in these reference patterns.…”

“…These approaches involve a time-consuming compression process, especially imposing constraints on immediate processing in a graph stream environment. High accuracy and compression rates are provided by pattern extraction techniques, such as [14][15][16][23][24][25][26]; however, their long processing times during graph compression also render them difficult to be applied in real-time environments. As a solution to these issues, research on the use of provenance has been proposed [29][30][31].…”