Learners’ attainment of academic knowledge in postsecondary institutions is predominantly expressed by summative approaches. Instead, recent advances in educational technology have hinted at a means to continuously measure learning attainment in terms of personalized learner competency, capacity, and effectiveness. Similarly, educational technology also offers guidelines to continuously measure instructional attainment in terms of instructional competency, instructional capacity, and instructional effectiveness. While accurate computational models that embody these attainments, educational and instructional, remain a distant and elusive goal, big data learning analytics approaches this goal by continuously observing study experiences and instructional practices at various levels of granularity, and by continually constructing and using models from these observations. This article offers a new perspective on learning and instructional attainments with big data analytics as the underlying framework, discusses approaches to this framework with evidences from the literature, and offers a case study that illustrates the need to pursue research directions arising from this new perspective.