A Framework for Affective & Sustainable Learning

Abstract

Social, Emotional and Affective Factors [SEA] are critical to academic and career success [1]. Affective Education helps children better understand their feelings and respond to challenging situations. Although the importance of affect on learning and cognition is widely accepted in Education Research, the research so far has treated affect and cognition on two separate dimensions loosely coupled. Recent findings, scattered across various disciplines, have shown their relationship to be more tightly coupled than what the previous theories have posited. Also, with the advent of online education, students are on their own to overcome the challenges that arise in the course of learning without the guidance of a human teacher. Therefore, it is imperative that machines detect and respond to affect effectively. In this paper, we propose a unified \& scalable framework that brings together findings from multiple disciplines (Decision-making, Affective research, Chess studies, Education, Mindfulness studies), highlighting some of the challenges encountered in detecting/responding to affect and propose ways to address them. We then validate our framework through a Pilot Experiment (Chess puzzles) by examining the effectiveness of response strategies in mitigating the influence of incidental affect on performance. The results from the experiment, 80 chess amateurs solving chess puzzles, reveal that introducing time-delay between tasks \& being aware of their state, can minimize the impact of incidental affect. We are hopeful that the learning from these experiments can be incorporated into cognitive-affective agents making learning – effective, sustainable and enjoyable.