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Towards a Personalized Summative Model Based on Learner's Preferences

机译:建立基于学习者偏好的个性化汇总模型

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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,讲师有许多不同的方法来评估学生。形成和总结模型主要应用于独立于学习环境的多种组合(现场,在线或混合)。当我们转向适应性学习时,将根据所选的学习路径和预定的评估活动对学生进行评估。适应往往是在学习过程中(活动,反馈,材料),主要与形成模型有关,但与汇总模型相关的适应性很小,而且限制性很强。在本文中,我们介绍了基于学习者偏好的新型个性化汇总模型的基础。尽管此模型从概念上讲可以允许在不获得所有学习成果的情况下通过课程,但与基于预定义成绩计算公式的其他汇总模型相距不远。本文介绍了该模型,还总结了对教师和学习者进行定性调查的结果。

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