This work-in-progress research paper examines the relationship between two aspects of students' engagement and academic performance. With the boom of technology-mediated learning environments, many educational applications are integrated into STEM courses. However, the effectiveness of these applications in the learning environments is contingent upon factors including but not limited to applications' ease of use, relevance to courses, students' engagement with the application, and perceived value of the application in the context of students' learning. This work-in-progress paper uses two aspects of engagement in a mobile technology-mediated learning environment and explores their relationship with students' academic performances. The two perspectives of engagement include 1) students' engagement with the course -- Academic Engagement and 2) students' engagement with the application used in the course -- Application Engagement. We collected the data from 110 first-year engineering students enrolled in a required engineering class programming in MATLAB. Students self-reported their academic engagement on four dimensions: behavioral, emotional, social, and cognitive. In addition, students used a mobile application called CourseMIRROR. The application prompted students to write their reflections on each lecture throughout the semester, asking about its interesting or confusing points. The application uses Natural Language Processing (NLP) algorithm to create the summaries of these reflections. For application engagement, we used the number of times students viewed the summary through embedded data analytics in the CourseMIRROR application. For students' academic performance, we used the students' total scores in the course. We hypothesize that these two engagement perspectives are related to the students' academic performance. Specifically, the study will be guided by the following research questions: 1) To what degree do students' academic and application engagement relate to their academic performance? And 2) Do students with high engagement (i.e., academic or application) perform better in their exams? We analyzed the data using Pearson product-moment correlation and multiple regression to predict the students' academic performance and its relationship with students' academic engagement and application engagement. This work-in-progress paper presents the results of these analyses and their implications and provides future research directions. [This paper was published in: "2022 IEEE Frontiers in Education Conference (FIE)," IEEE, 2022.]