Apriori is the more complex of the Association Rule algorithms. This is because it is designed to take suggested associations and quantify the strength of each. Beyond business applications, education and service providers can use this algorithm to refine correlations for testing in a more inclusive way.
While Eclat is simpler than Apriori, it is useful for exploring possible associations when you don’t know where to start looking. This seems useful to me beyond business by offering teachers, developers, and others without explicit business focus a way to streamline the association exploration process. In education specifically, it could help support administrators and policy makers as they consider factors beyond teacher rating as impacting student assessment data.
A note on Python in this context:
This is the first time that Python has been less capable than R in modeling/evaluating data since I began learning it. As a lifelong learner, I will continue my study of Python to enrich my Data Engineering radius. However, this may be some of the reason my university employed R rather than Python for data analysis.