Directly Measuring Learning: A Community Engaged Learning Case Study with Implications for Human Centered Computing in Education

This paper investigates the empirical measurement of student growth in relation to institutional learning outcomes, focusing on academic development supported through applied learning. The research specifically examines the impact of service-learning participation on practical, affective skills, and presents a method for directly measuring learning using rubric data, avoiding the use of proxies or indirect measures. It highlights the possibility of scaling course design to enhance learning outcomes, including self-paced remediation. The authors emphasize the importance of service-learning as a means to cultivate global citizenship and describe the potential for learning analytics to facilitate student success in other domains. This approach is tested in a public university setting and considers the unique challenges posed by its diverse student populations. The findings suggest that well-designed applied learning and service-learning programs can enhance student learning outcomes, aligning with institutional goals. The study concludes by advocating for the focused integration of ML and AI tools in education to tailor learning experiences to individual student needs in alignment with desired program outcomes.