Jiang , Shengyu2021-02-222021-02-222020-11https://hdl.handle.net/11299/218710University of Minnesota Ph.D. dissertation. November 2020. Major: Psychology. Advisors: Chun Wang, Niels Waller. 1 computer file (PDF); iv, 122 pages.An online learning system has the capacity to offer customized content that caters to individual learner’s need and has seen growing interest from industry and academia alike in recent years. Noting the similarity between online learning and the more established adaptive testing procedures, research has focused on applying the techniques of adaptive testing to the learning environment. Yet due to the inherent difference between learning and testing, there exist some major challenges that hinder the development of adaptive learning systems. To tackle these challenges, a new online learning system is proposed which features a Bayesian algorithm that computes item and person parameters on the fly. The new algorithm is validated in two separate simulation studies and the results show that the system, while being cost-effective to build and easy to implement, can also achieve adequate adaptivity and measurement precision for the individual learner.enassessment for learningBayesian inferenceCATe-learningIRTitem-based learning systemsThesis or Dissertation