Supporting person-centered learning: Does the choice of the learning management system matter?: A case study with Moodle, Fronter and CEWebS

Comber, Oswald; Motschnig, Renate; Komlenov, Živana

doi:10.1109/educon.2010.5492485

Cited by 2 publications

(1 citation statement)

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“…In addition, we have observed a growing trend of college instructors' adoption in blended classrooms [82]. In blended learning, residential classroom instructors utilize existing MOOC content to save their efforts in course material preparation, and thus they can focus on interacting with students to create a learner-centered environment [119,29,55].…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Educational Content Linking for Enhancing Learning Need Remediation in MOOCs

Li¹

2020

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Since its introduction in 2011, there have been over 4,000 MOOCs (Massive Open Online Courses) on various subjects on the Web, serving over 35 million learners. MOOCs have shown the ability to transcend time and space, democratize knowledge dissemination, and bring the best education in the world to every learner. However, the disparate distances between participants, the size of the learner population, and the heterogeneity of the learners' backgrounds make it extremely difficult for instructors to interact with the learners in a timely manner, which adversely affects learning experience and outcome.To address the challenges, in this thesis, we propose a framework: educational content linking. By linking and organizing pieces of learning content scattered in various course materials into an easily accessible structure, we hypothesize that this framework can provide learners guidance and improve content navigation. Since most instruction and knowledge acquisition in MOOCs takes place when learners are surveying course materials, better content navigation may help learners find supporting information to resolve their confusion and thus improve learning outcome and experience.To support our conjecture, we present end-to-end studies to investigate our framework around two research questions. We first ask, can manually generated linking improve learning? For investigating this question, we choose two STEM courses, statistics and programming language, and demonstrate how the annotation of linking among course materials can be done with collaboration between course staff and online workers. With the annotation, we implement an interface that can present learning materials and visualize the linking among them simultaneously. We observe that, in a large-scale user study, this interface enables users to search for desired course materials more efficiently, and retain more concepts more readily. This result supports the notion that manual linking can indeed improve learning outcomes. Second, we ask, can learning content be generated with machine learning methods? For this question, we propose an automatic content linking algorithm based on conditional random fields. We demonstrate that automatically generated linking can still

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Section: Chapter 1 Introductionmentioning

confidence: 99%