Thesis Defense: Micro-Credentialing with Fuzzy Content Matching: An Educational Data-Mining Approach

Monday, March 27, 2023 3 p.m. to 5 p.m.

Announcing the Final Examination of Paul Amoruso for the degree of Master of Science 

There is a growing need to assess and issue micro-credentials within STEM curricula. Although one approach is to insert a free-standing academic activity into the students learning and degree path, herein the development and mechanism of an alternative approach rooted in leveraging responses on digitized quiz-based assessments is developed. An online assessment and remediation protocol with accompanying Python-based toolset was developed to engage undergraduate tutors who identify and fill knowledge gaps of at-risk learners. Digitized assessments, personalized tutoring, and automated micro-credentialing scripts for Canvas LMS are used to issue skill-specific badges which motivate the learner incrementally, while increasing self-efficacy. This consisted of building upon the available Canvas LMS application programming interface to design an algorithm that takes the given Canvas LMS data to develop the automation of dispersing badges. In addition, a user centric interface was prototyped and implemented to garner high user acceptance. As well as pioneering the potential steps to efficiently migrating the classical quizzes to "New Quizzes" format and investigating potentials steps to provide personalized YouTube video recommendations to students, based on assessment performance. Moreover, foundational research, operational objectives, and prototyping a user interface for
instructor-facing micro-credentialing was established through the work represented in this document. The approach developed is shown to provide a fined-grained analysis that credentials students understanding of material from a semester-wide perspective using a scalable automation approach evaluated within the Canvas LMS.

Committee in Charge: Ronad DeMara, Reza Abdolvand, Changchun Zou

Read More



College of Graduate Studies 14078232766


College of Graduate Studies Events




Thesis and Dissertation defense Computer Engineering