With the development of personalized learning in technological platforms, more data and information are given to instructors on what contents are appropriate for a learner's next step, with an aim of helping them support their students in navigating an optimized learning path that can promote an enhanced learning outcome. In this study, we collected data from an online learning platform, Learnta® TAD , which allows teachers to distribute tasks based on system recommendations. The recommendations are directed by the system's knowledge graph algorithm, determining whether the student is ready to learn the task (i.e. the task is within the student's Zone of Proximal Development), whether the student is not yet ready to learn the task, or whether the student has already mastered the task. We used the acquired data to investigate whether giving content in each of these groups results in different learning outcomes. Statistical methods such as subgroup analysis, Fisher's exact test, and logistic regression are conducted to address the proposed topic. Replicating a prior, smaller-scale study, our findings suggest that the student gains more mastery when assigned Ready-to-Learn tasks than when assigned Unready-to-Learn tasks, across Math and English, more and less successful students, and in-class and homework. Moreover, students who are given already mastered tasks perform better than those who are given Ready-to-Learn and Unready-to-Learn tasks across all groups. [For the full proceedings, see ED607784.]