A Methodological Approach to Eliciting Affective Educational Recommendations

Santos, Olga C.; Saneiro, Mar; Salmerón-Majadas, Sergio; Boticario, Jesús G.

doi:10.1109/icalt.2014.234

Cited by 25 publications

(14 citation statements)

References 11 publications

(11 reference statements)

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“…Jaques et al (2014) describe how they use gaze data to predict boredom and curiosity within MetaTutor, a hypermedia environment designed to foster student self-regulated learning processes in the domain of biology (Azevedo et al, 2009). Another example is Santos et al (2014), which shows that personality and self-e cacy impact the e↵ectiveness of motivational feedback and recommendations. A↵ective states were detected from mouse and keyboard interactions as well as from physiological parameters.…”

Section: Related Workmentioning

confidence: 99%

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

Grawemeyer

Mavrikis

Holmes

et al. 2017

User Model User-Adap Inter

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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.

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Section: Related Workmentioning

confidence: 99%

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

Grawemeyer

Mavrikis

Holmes

et al. 2017

User Model User-Adap Inter

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show abstract

“…For this, user centered design approaches [87] can be of value, such as to consider recommending learning activities that, for instance, foster communication [1] and metacognition [124][77] [88]. At the same time, the potential of semantic technologies is being considered to describe the educational domain and therefore enrich the recommendation process In this sense, the application of affective computing in TEL recommender systems can provide added value to the recommendations when emotional and sentiment information is taken into account in the recommendation process [51] [92] and can provide interactive recommendations through sensorial actuators [91].…”

Section: Analysis According To the Frameworkmentioning

confidence: 99%

Panorama of Recommender Systems to Support Learning

Drachsler

Verbert

Santos

et al. 2015

Recommender Systems Handbook

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189

115

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“…Limiting the model to receive biophysical signals to identify the emotion, and perform recommendation for learning self-rhythmic. Moreover, [11] proposes a four activities methodology using methods of user-centered design and data mining techniques requiring large processing capabilities. In addition webcam is required to analyze the facial expression of the user to identify the emotion.…”

Section: Theoretical Aspects and Related Workmentioning

confidence: 99%

A Learning Object Recommendation Model with User Mood Characteristics

Méndez

Zapata

Collázos

2016

Social Computing in Digital Education

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Abstract. Emotions influence human cognition, affecting perception and understanding of a specific situation; therefore emotions can affect positively or negatively the learning process. Currently there are few information systems that analyze users' emotions to optimize their learning. This article proposes a model that includes users' temporary emotions to recommend Learning Objects (LO) and deliver relevant educational materials. Three stages are set; initially, the model recognizes a user's emotions and learning style; then a recommendation system is applied to identify relevant LOs; and finally, the presentation of this information is showed to the user. In this work we present a method that identifies the emotion of the user based on facial recognition, and the process of recommendation is presented.

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A Methodological Approach to Eliciting Affective Educational Recommendations

Cited by 25 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

Panorama of Recommender Systems to Support Learning

A Learning Object Recommendation Model with User Mood Characteristics

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