A Model to Predict and Analyze Students' Learning Preferences and their Cognitive Development through Educational Big Data

Abstract

Underpinned by the accelerated progression of information technology, the role of educational big data in information gathering and analysis has been underscored, particularly so in finance, a discipline embedded in logic and analysis. Patterns in student learning and behavioral data, when examined, can afford educators invaluable insights to shape efficacious teaching strategies. Contemporary research probing into the dynamics of student learning preference evolution and cognitive advancement appears to over-depend on static data, often falling short of effectively addressing the intricate data structures in educational big data. In this light, it becomes imperative to delve into the temporal shifts in student learning preferences and their link to cognitive advancement. In this context, a novel dynamic trustaware preference evolution model is brought to the fore, with the potential to precisely track variations in learning preferences of finance students and elucidate their correlation with cognitive advancement. A correlation model is erected, laying bare the reciprocal interaction between the metamorphosis of student learning preferences and cognitive progression. This pioneering approach eclipses the constraints inherent in extant research methodologies, rendering deeper comprehension to educators. Findings from regression analysis divulge the association between the transformative journey of learning preferences and cognitive advancement, holding far-reaching implications for educational practices. These revelations can capacitate educators to fine-tune their teaching approaches in line with student development, fostering personalized learning ecosystems. This research further holds significant merits for addressing complexities within finance education, aiding in the cultivation of adept professionals capable of navigating the fluid landscape of modern finance.