How Affect-Aware Feedback Can Improve Student Learning

Affecting Off-Task Behaviour: How Affect-aware Feedback Can Improve Student Learning Beate Grawemeyer Manolis Mavrikis Wayne Holmes London Knowledge Lab London Knowledge Lab London Knowledge Lab Birkbeck, University of London UCL Institute of Education UCL Institute of Education London, UK University College London, UK University College London, UK [email protected] [email protected] [email protected] Sergio Gutierrez-Santos Michael Wiedmann Nikol Rummel London Knowledge Lab Institute of Educational Institute of Educational Birkbeck, University of London Research Research London, UK Ruhr-Universität Bochum, DE Ruhr-Universität Bochum, DE [email protected] [email protected] [email protected] ABSTRACT Keywords This paper describes the development and evaluation of an Affect, Feedback, Exploratory Learning Environments affect-aware intelligent support component that is part of a learning environment known as iTalk2Learn. The intelligent support component is able to tailor feedback according to a 1. INTRODUCTION student’s affective state, which is deduced both from speech The aim of our research is to provide intelligent support to and interaction. The affect prediction is used to determine students, taking into account their affective states in order which type of feedback is provided and how that feedback to enhance their learning experience and performance. is presented (interruptive or non-interruptive). The system It is well understood that affect interacts with and in- includes two Bayesian networks that were trained with data fluences the learning process [19, 10, 3]. While positive gathered in a series of ecologically-valid Wizard-of-Oz stud- affective states (such as surprise, satisfaction or curiosity) ies, where the effect of the type of feedback and the presen- contribute towards learning, negative ones (including frus- tation of feedback on students’ affective states was investi- tration or disillusionment at realising misconceptions) can gated. This paper reports results from an experiment that undermine learning. Any learning experience is typically full compared a version that provided affect-aware feedback (af- of transitions between positive and negative affective states. fect condition) with one that provided feedback based on For example, while a student may be interested in a partic- performance only (non-affect condition). Results show that ular learning task, any misconceptions that they have might students who were in the affect condition were less bored and lead to frustration or disillusionment as they are forced to less off-task, with the latter being statically significant. Im- reconsider their existing understanding. However, if this portantly, students in both conditions made learning gains negative affective state is reconciled, the student might be- that were statistically significant, while students in the af- come deeply engaged with the task once again. D’Mello et fect condition had higher learning gains than those in the al., for example, elaborate on how confusion, which superfi- non-affect condition, although this result was not statisti- cially might be considered a negative affective state, is likely cally significant in this study’s sample. Taken all together, to promote learning under appropriate conditions [10]. the results point to the potential and positive impact of It is important therefore, to deepen our understanding of affect-aware intelligent support. the role of affective states for learning, and to be able to move students out of states that inhibit learning. Pekrun [24] discusses achievement emotions or affective states that arise in a learning situation. Achievement emotions are CCS Concepts states that are linked to learning, instruction, and achievement. We focus on a subset of affective states identified •Applied computing → Computer-assisted instruc- by Pekrun: flow/enjoyment, surprise, frustration, and bore- tion; dom. We also add confusion, which has been identified else- where as an important affective state during learning [25], for tutor support and for learning in general [10]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed As described in Woolf et al. [32], students can become for profit or commercial advantage and that copies bear this notice and the full cita- overwhelmed (very confused or frustrated) during learning, tion on the first page. Copyrights for components of this work owned by others than which may increase cognitive load [30]. However, appropri- ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- publish, to post on servers or to redistribute to lists, requires prior specific permission ate feedback might help to overcome such problems. Carenini and/or a fee. Request permissions from [email protected]. et al. [4] describe how effective support or feedback needs to LAK ’16, April 25-29, 2016, Edinburgh, United Kingdom answer three main questions: (i) when the support should c 2016 ACM. ISBN 978-1-4503-4190-5/16/04. $15.00 be provided during learning; (ii) what the support should DOI: http://dx.doi.org/10.1145/2883851.2883936 contain; and (iii) how it should be presented. In this paper we focus on the latter two questions: what on task complexity, user characteristics (such as perceptual and how support or feedback should be provided based on speed, visual working memory, and verbal working memory) the student’s affective state. and delivery times. Also, Grawemeyer & Cox [12] describe We report on the development of intelligent feedback that a system that is able to recommend a particular represen- is able to tailor the type of feedback as well as the presentation (bar chart, plot chart, pie chart, sector graph, eulers tation of the feedback in order to enhance a student’s learn- diagram, or table) based on the user’s expertise with repre- ing experience. It includes a Bayesian network for deter- sentations, their preferences for particular representations, mine the most effective feedback type, as well as a Bayesian the task, the information to be presented and the repre- network for detecting the most effective presentation of the sentation’s semantics. In the slightly different, but related, feedback. Both networks are trained with data from Wizard- use case of exploratory visual data analysis, researchers have of-Oz studies where the impact of a student’s affective state looked at inferring the user’s intended task and recommend- on the effectiveness of the feedback type and on the presen- ing alternative visualisations that may help in their analy- tation of feedback was investigated (c.f. [15, 13]). sis [11]. Further, Ahn & Brusilovsky [1] describe a system In those studies, we learned that a student’s affective state which adapts the visualisation of search results dynamically, can be enhanced when the feedback type is matched to the based on a user’s emerging interests. affective state of the student. For example, when students In this paper we describe how we add to this literature were confused, affect boosts and specific instructive feedback by developing and evaluating intelligent support within a were more effective in helping students [15]. Additionally, learning environment that is able to adapt the type of feed- adapting the presentation of the feedback according to the back as well as the presentation (high- or low-interruptive) student’s affective state is also important, especially when of feedback according to a student’s affective state in order they are confused or frustrated. For these particular af- to enhance their learning experience and learning gains. fective states, high-interruptive feedback is more effective, especially as the cost of not viewing the feedback is likely to 3. THE ITALK2LEARN PLATFORM be a negative affective state [13]. However, when students are in flow, low-interruptive feedback is preferred [21]. iTalk2Learn is a learning platform for children aged 8-12 In the next section an overview of related literature is years old who are learning fractions. It combines structured provided. Section 3 describes the development of the affect- practice with more open-ended activities in an exploratory aware intelligent support. Section 4 outlines the evaluation learning environment called Fractions Lab [22]. The over- of the support. Results of the evaluation are reported in all aim is to foster robust learning through providing ex- section 5. A detailed discussion that highlights the impor- ploratory exercises that help develop conceptual knowledge tance of including affect in learner modelling is provided in interleaved with structured practice activities that foster section 6; while section 7 concludes the paper. procedural knowledge. In this paper, we focus on the support provided while students are undertaking these learning 2. RELATED WORK activities (i.e. not the sequencing of those activities). Most of the related work in the field focuses on detecting affect in different input stimuli, ranging from spoken dia- logue (e.g. [31]) to keyboard and mouse interactions [27], including a combination of different stimuli such as conver- sational cues, body language and facial expression [8]. Only a limited amount of research has been undertaken to investigate how a student’s affect or motivation can been taken into account to provide learning material or motiva- tional feedback. One early example is del Soldato & du Boulay [7] who use a student’s motivation to decide whether to provide the next task or to provide hints. Another example is Santos et al. [28] who show that affect as well as motivation and self-efficacy impact the effectiveness of motiva- tional feedback and recommendations. Additionally, Woolf et al. [32] developed an affective pedagogical agent which is able to mirror a student’s affective state, or acknowledge a student’s affective state if it is negative. Another example is Conati & MacLaren [6], who developed a pedagogical agent to provide support according to the affective state of the student and their personal goal.

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