  This research study delves into the conceptualization, development, and
deployment of an innovative learning analytics tool, leveraging the
capabilities of OpenAI's GPT-4 model. This tool is designed to quantify student
engagement, map learning progression, and evaluate the efficacy of diverse
instructional strategies within an educational context. Through the analysis of
various critical data points such as students' stress levels, curiosity,
confusion, agitation, topic preferences, and study methods, the tool offers a
rich, multi-dimensional view of the learning environment. Furthermore, it
employs Bloom's taxonomy as a framework to gauge the cognitive levels addressed
by students' questions, thereby elucidating their learning progression. The
information gathered from these measurements can empower educators by providing
valuable insights to enhance teaching methodologies, pinpoint potential areas
for improvement, and craft personalized interventions for individual students.
The study articulates the design intricacies, implementation strategy, and
thorough evaluation of the learning analytics tool, underscoring its
prospective contributions to enhancing educational outcomes and bolstering
student success. Moreover, the practicalities of integrating the tool within
existing educational platforms and the requisite robust, secure, and scalable
technical infrastructure are addressed. This research opens avenues for
harnessing AI's potential in shaping the future of education, facilitating
data-driven pedagogical decisions, and ultimately fostering a more conducive,
personalized learning environment.
