In a conventional teaching context, student disparities in the background of pre-knowledge, skills, or comprehension lead to the alienation of the struggling students while boring those who are more experienced. At issue is the appropriateness of the one-size-fits-all model. An alternative is adaptive learning. Dated back to 1912, Edward L. Thorndike proposed the idea of a mechanical miracle that intends to conduct personal instructions through print. This idea has inspired century-old efforts to automate education by creating teaching machines that adaptively feature automation, feedback, and self-pacing. With the technology evolving, many state-of-the-art works that provide digital “teaching machines” have emerged, allowing adaptive learning. However, most of them focus on math or elementary reading and writing skills, and few are on programming-based courses. The few available are costly, heavy-weighted in adaptability, or limited in mapping competencies to an entire formal assessment, with questions that are not naturally differentiated in an assessment and thus, lacking flexibility.

In response to these issues, we initialized an exploratory project and developed an agile adaptive learning web tool that maximizes students’ adaptive and connected learning experience. This demo provides a brief tutorial on the steps to set up learning concepts, learning objectives, and objective-to-question mappings as well as on-the-fly quiz generation that matches required learning outcomes along with visualization of assessment outcome coverage and student mastery map. A teaching case will also be demonstrated. In the demo, we will use OpenDSA as the learning materials and CS2-DS as an example, but the ideas apply generally.