Promoting reflective learning in big data analytics: Key facets and pedagogical strategies
DOI:
https://doi.org/10.52041/iase2023.108Abstract
This paper features reflective learning as an effective pedagogical strategy for addressing the challenges of big data analytics, especially data complexity and uncertainty. The importance of metacognition in data science education is addressed. Based on a systematic review of the literature, key themes and concepts related to reflective learning and big data analytics were identified: evaluation and critical thinking, prior knowledge, beliefs, emotions, intentionality, and future actions. By incorporating these elements into the curriculum, educators can facilitate learners to make meaningful connections between their prior knowledge and new information, develop a deeper understanding of complex concepts, and learn how to apply them effectively in real-world situations. Further research is needed to investigate the effectiveness of different pedagogical approaches and technological tools, in enhancing reflective learning, in this context.References
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