The project aims to improve quantitative thinking of students and adult learners. International students from the partner universities will participate in three blended mobility activities hosted by different universities using innovative learning and teaching methods. Participation will improve transversal and basic skills and competencies in quantitative thinking such as communication and presentation, research ethics, and programming skills. The partners will develop and exchange interactive software with tasks and exercises for teaching quantitative thinking. These tasks and exercises will be embedded in a modular knowledge structure on quantitative thinking that promotes various skills (e.g., writing, presentation, programming, research ethics, data processing, analyses, inference, model selection). In a further development it is planned to map this knowledge structure onto geographical locations to create a virtual learning environment featuring social and cultural highlights of a hosting university. For example a participant may embark on a guided tour to explore places of interest around the University of Tartu, the surrounding region and Estonia introducing basic game-play. While visiting different virtual places associated with the hosting university each participant solves tasks in this knowledge structure. This leads to a personalized learning within a given domain or module because their knowledge state is continuously assessed and updated. The knowledge structures on quantitative thinking enforce learning of transferable skills. Adaptive learning is achieved through a combination of video tutorials, interactive programming exercises including visualizations in an attractive environment. This innovative approach to e-learning opens up new avenues for commercial exploration in HE. For example, the most successful and popular modules can be provided as a massive open-access e-learning course of quantitative thinking on a suitable e-learning platform.
TquanT Shiny R Apps
Bayesian Statistics and ComputationConsortium AppIntraclass CorrelationIRTKnowledge Space Theorylearnr TutorialMachine LearningMaximum LikelihoodMixed ModelsModel ComparisonMonte CarloMultisensory InputNetwork ModelsParameter EstimationProbability TheoryReproducibilitySignal Detection TheoryStatisticsStudent AppUtilities