Towards a Personalized Summative Model Based on Learner's Preferences
David Bañeres · 2016
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.