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The role of cognitive engagement in promoting deep learning is well established. This deep learning fosters attributes of success such as self-efficacy, motivation and persistence. However, the traditional chalk-and-talk teaching and learning environment is not conducive to engage students cognitively. The biggest impediment to implementing an environment for deep learning such as active-learning is the limited duration of a typical class period most of which is consumed by lecturing. In this paper, best practices and strategies for cognitive engagement of students in the classroom are discussed. Several lower level math and aerospace engineering courses were redesigned and offered during the academic year at a historically black university. The learning strategies in these redesigned courses included the “flipped” pedagogical model which allowed the integration of the active-learning strategy in the classroom. The research study is to determine the impact of these redesigned courses on student academic performance and persistence in STEM courses. The efficacy of the design of the flipped approach was also investigated. A between-group quasi-experimental research design was used for comparing student academic performance in traditional classroom (control group) and redesigned classroom (intervention group). A within-subject, repeated measures design was also used to assess the impact on the students’ self-regulated learning. A validated instrument was used to measure the effect of the redesigned learning environment on the motivational beliefs and self-regulated learning. Data on the academic performance of the students were collected. Analyses of these data indicated a significant impact on student academic performance. A positive change in student motivation and self-regulated learning was observed. Data analysis also validated the design of the intervention. This research is supported by NSF Grant# 1712156.