The Affective Meta-Tutoring Project: Lessons Learned

VanLehn, Kurt; Burleson, Winslow; Girard, Sylvie; Chavez-Echeagaray, Maria Elena; Gonzalez-Sanchez, Javier; Hidalgo-Pontet, Yoalli; Zhang, Lishan

doi:10.1007/978-3-319-07221-0_11

Cited by 14 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…VanLehn et al (2014) describe an a↵ective meta tutor, which is able to determine what kind of motivational feedback should be provided to students based on their a↵ective states and log data. Physiological sensors (facial expression camera and a posture-sensing chair) and a regression model are used to calculate whether a student is engaged, confused, or bored.…”

Section: Related Workmentioning

confidence: 99%

“…In general, these adaptations tend to be focused on changing the text within a message (e.g. VanLehn et al, 2014) or changing the dialogue (e.g. Forbes-Riley and Litman, 2011a; to include, for example, empathetic statements designed to motivate the students.…”

Section: Related Workmentioning

confidence: 99%

“…Other research (e.g. Conati and MacLaren, 2009;D'Mello et al, 2005VanLehn et al, 2014;Woolf et al, 2009;Rowe et al, 2009) have altered how feedback is delivered to students, for example through the use of a pedagogical agent capable of expressing empathy.…”

Section: Related Workmentioning

confidence: 99%

“…by including empathetic statements to motivate students (VanLehn et al, 2014;Forbes-Riley and Litman, 2011a;, we instead adapt the type of feedback (whether it is, for example, instructional feedback or a reflective prompt), with the feedback content being based on the student's interaction (mainly their performance). In addition, instead of adapting the delivery of the feedback using for example an empathetic pedagogical agent (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Affective learning: improving engagement and enhancing learning with affect-aware feedback

Grawemeyer

Mavrikis

Holmes

et al. 2017

User Model User-Adap Inter

View full text Add to dashboard Cite

This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students' a↵ective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students' a↵ective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students' performance (the non-a↵ect condition). Our results suggest that a↵ect-aware support contributes to reducing boredom and o↵-task behavior, and may have an e↵ect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that a↵ect-aware modelling plays in the adaptive delivery of formative feedback to support learning.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Affective learning: improving engagement and enhancing learning with affect-aware feedback

Grawemeyer

Mavrikis

Holmes

et al. 2017

User Model User-Adap Inter

View full text Add to dashboard Cite

show abstract

“…They found that affect-based feedback was often rated as helpful, and that learners often benefit more from affect-based feedback than hints and reminders. VanLehn et al (2014) describe the development of an affective intervention using a learning companion. The learner's behavioural categories after a task were modelled, and an affective message given afterwards.…”

Section: Adaptation Of Learning Systems To Motivation and Affective Smentioning

confidence: 99%

Adapting Progress Feedback and Emotional Support to Learner Personality

Dennis

Masthoff

Mellish

2015

Int J Artif Intell Educ

View full text Add to dashboard Cite

Abstract. As feedback is an important part of learning and motivation, we investigate how to adapt the feedback of a conversational agent to learner personality (as well as to learner performance, as we expect an interaction effect between personality and performance on feedback). We investigate two aspects of feedback. Firstly, we investigate whether the conversational agent should employ a slant (or bias) in its feedback on particular test scores to motivate a learner with a particular personality trait more effectively (for example, using "you are slightly below expectations" versus "you are substantially below expectations" depending on learner conscientiousness). Secondly, we investigate which emotional support messages the conversational agent should use (for example: using praise, emotional reflection, reassurance or advice) given learner personality and performance.We investigate the adaptation of this feedback to a learner personality, in particular the traits in the Five Factor Model. Five experiments were run where participants gave progress feedback and emotional support to students with different personalities and test scores. The type of emotional support given varied between different personalities (e.g. neurotic individuals with poor grades received more emotional reflection). Two algorithms were created using different methods to describe the adaptations and evaluated on how well they described the experimental data using DICE scores. A refined algorithm was created based on the results. Finally, we ran a qualitative study with teachers to investigate the algorithm's effectiveness and further refine the algorithm.

show abstract

A System Architecture for Affective Meta Intelligent Tutoring Systems

Gonzalez-Sanchez

Chavez-Echeagaray

VanLehn

et al. 2014

Intelligent Tutoring Systems

Self Cite

View full text Add to dashboard Cite

Abstract. Intelligent Tutoring Systems (ITSs) constitute an alternative to expert human tutors, providing direct customized instruction and feedback to students. ITSs could positively impact education if adopted on a large scale, but doing that requires tools to enable their mass production. This circumstance is the key motivation for this work. We present a component-based approach for a system architecture for ITSs equipped with meta-tutoring and affective capabilities. We elicited the requirements that those systems might address and created a system architecture that models their structure and behavior to drive development efforts. Our experience applying the architecture in the incremental implementation of a four-year project is discussed.

show abstract

The Affective Meta-Tutoring Project: Lessons Learned

Cited by 14 publications

References 11 publications

Affective learning: improving engagement and enhancing learning with affect-aware feedback

Affective learning: improving engagement and enhancing learning with affect-aware feedback

Adapting Progress Feedback and Emotional Support to Learner Personality

A System Architecture for Affective Meta Intelligent Tutoring Systems

Contact Info

Product

Resources

About