Carry-Forward Effect: Early scaffolding learning processes

Abstract

Multimodal data enables us to capture the cognitive and affective states of students to provide a holistic understanding of learning processes in a wide variety of contexts. With the use of sensing technology, we can capture learners’ states in near real-time and support learning. Moreover, multimodal data allows us to obtain early-predictions of learning performance, and support learning in a timely manner. In this contribution, we utilize the notion of “carry forward effect”, an inferential and predictive modelling approach that utilizes multimodal data measurements detrimental to learning performance to provide timely feedback suggestions. carry forward effect can provide a way to prioritize conflicting feedback suggestions in a multimodal data based scaffolding tool. We showcase the empirical proof of carry forward effect with the use of two different learning scenarios: debugging and game-based learning.