Equity gaps refer to the disparities in educational outcomes across race (ethnicity), socioeconomic status, gender, physical/mental abilities, and other demographic traits. In USA, the gaps grew significantly during the COVID-19 pandemic and presented a major challenge in education. In search for a solution to address this challenge proactively and early in the learning process, in this paper, we extend an existing relative approach on the modeling and prediction of teaching effectiveness (T-Matrices Rev) into one that can be used to study and predict educational equity gaps (T-Matrices Rev II). The proposed approach represents a practical and applicable methodical tool leveraging machine learning techniques for assisting the research on learning, inferring, and addressing the differences in students’ learning outcomes that may have been caused by their different demographic statuses. The proposed research is in its inception stage, and it faces many challenges, which are also discussed in this paper.