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Towards a Personalized Summative Model Based on Learner's Preferences

机译:基于学习者偏好的个性化总结模型

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Nowadays, instructors have many different methodologies to assess students. Formative and summative models are mainly applied to multiple combinations independently of the learning environment (on-site, online or blended). When we move to an adaptive learning, students are assessed depending on the selected learning path and the scheduled assessment activities. The adaption tends to be in the learning process (activities, feedback, materials) mainly related to formative models but little adaption can be found related to summative models and very restrictive. In this paper, we introduce the basis to a novel personalized summative model based on learner's preferences. Although this model conceptually may allow to pass a course without acquiring all learning outcomes, it is not far from other summative models based on predefined grade calculation formulas. The paper introduces the model and it also summarizes results of a qualitative survey to instructors and learners.
机译:如今,教师有许多不同的方法来评估学生。形成性和总结模型主要应用于多种组合,独立于学习环境(现场,在线或混合)。当我们搬到自适应学习时,学生根据所选的学习路径和预定的评估活动进行评估。适应倾向于在学习过程(活动,反馈,材料)中主要与形成模型相关,但可以与总结模型和非常限制有关的适应性。在本文中,我们介绍了基于学习者的偏好的新颖性比例模型的基础。虽然这一模型概念上可能允许在没有获取所有学习结果的情况下通过课程,但它与基于预定级计算公式的其他总结模型不远。本文介绍了该模型,它还总结了对教师和学习者进行定性调查的结果。

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