Big data and the data science skills to analyze data are critical in all science, technology, engineering, and mathematics (STEM) areas. Within this data-rich context, incoming STEM graduate students with different levels of data science and STEM training can complicate curricular approaches for mastery of data science techniques. Linear and uniform preparation models may even widen the performance gap among students with diverse levels of preparation. This issue might be circumvented by replacing traditional “one-size-fits-all” course content with personalized electronic training modules that are tailored to each student’s unique strengths, weaknesses, and training goals.
Penn State University aimed to develop, test, and refine a set of digital educational tools brought together by the Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education system (iPRACTISE). The goal of iPRACTISE was to direct each student toward a personally optimized training pathway for mastery of data science techniques. The iPRACTISE system allowed students to specify their own learning goals, provided customized assessments to evaluate their performance levels, and guided them to educational resources that help them reach their goals. In this way, the iPRACTISE system served as an initial proof-of-concept for a personalized, digital graduate educational system that could be adapted for use in a broad array of educational settings to enhance individual learning.
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