Challenges for Inclusive Affective Detection in Educational Scenarios

Santos, Olga C.; Rodríguez-Ascaso, Alejandro; Boticario, Jesús G.; Salmerón-Majadas, Sergio; Quirós, Pilar; Cabestrero, Raúl

doi:10.1007/978-3-642-39188-0_61

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…In particular, data mining techniques are to be used to process and combine this affective labeling from face expressions and body movements with breath and heart rate measures, body temperature levels, galvanic skin response, keyboard and mouse interaction, and the data from the participants' emotional self-reports. The purpose here is to take into account the needs of all users, including those with disabilities [ 50 ]. Taking all this into account, the main objective of our research within the MAMIPEC project is to facilitate an affective support that allows learners to perform cognitive tasks by improving their emotional control strategies.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, collaborative scenarios provide information about cognitive process and strategies specifically involved in social contexts, which deserve a different analysis [ 49 ]. Furthermore, it can be mentioned that participants with disability were also involved in the experience [ 50 ] but this analysis also deserves future work since there are differences in the variables to be considered when people have disabilities (e.g., there might be morphological or functional alterations that imply that their facial expressions and/or body movements vary in terms of intensity, location, or duration; users of screen readers use the keyboard for browsing interactions, not just typing). All these experiments took place in the so-called Madrid Science Week ( https://adenu.ia.uned.es/web/es/Proyectos/SemanaCiencia (in Spanish)).…”

Section: Experiments Descriptionmentioning

confidence: 99%

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Towards Emotion Detection in Educational Scenarios from Facial Expressions and Body Movements through Multimodal Approaches

Saneiro

Santos

Salmerón-Majadas

et al. 2014

The Scientific World Journal

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We report current findings when considering video recordings of facial expressions and body movements to provide affective personalized support in an educational context from an enriched multimodal emotion detection approach. In particular, we describe an annotation methodology to tag facial expression and body movements that conform to changes in the affective states of learners while dealing with cognitive tasks in a learning process. The ultimate goal is to combine these annotations with additional affective information collected during experimental learning sessions from different sources such as qualitative, self-reported, physiological, and behavioral information. These data altogether are to train data mining algorithms that serve to automatically identify changes in the learners' affective states when dealing with cognitive tasks which help to provide emotional personalized support.

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Section: Discussionmentioning

confidence: 99%

Section: Experiments Descriptionmentioning

confidence: 99%

Towards Emotion Detection in Educational Scenarios from Facial Expressions and Body Movements through Multimodal Approaches

Saneiro

Santos

Salmerón-Majadas

et al. 2014

The Scientific World Journal

Self Cite

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“…Additionally, self-directed learning acknowledges the importance of students' responsibility in their learning process. It is important to consider the flexibility needed in learning environments that are turned into inclusive spaces, as well as how technology is used to detect students' needs and preferences (Santos et al, 2013). We highlight this personal responsibility to the flexibility explained above, as a significant contribution to planning for teaching and learning processes that generate an impact on how courses are designed, considering all necessary adjustments from the design stage.…”

Section: Accessible and Inclusive Learning Outcomesmentioning

confidence: 99%

Research on Virtual Education, Inclusion, and Diversity

Fermín-González

2019

IRRODL

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This article covers a topic related to increases in the existing heterogeneity of the university student population, specifically in virtual learning environments. There is a growing concern for offering training alternatives that include all students. As the first step in a line of research related to quality, equity, and inclusion in e-learning, we aim to identify emerging trends in research on inclusive virtual education (IVE) at the higher education level and how inclusion is conceptualized. Our goal is to provide ideas on future research topics and raise issues for further exploration. This research was conducted through a systematic review of articles published in the last decade in the WOS and Scopus databases. Upon reflection, we suggest the need for inclusive e-learning educational designs with greater emphasis on human diversity in all of its complexity. By doing so, we may be able to contribute to increasing the equality of educational opportunities and overcoming the barriers that restrict the access, continuity, and successful exit of the entire student population, regardless of their individual learning needs.

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“…Additionally, the System Usability Scale (SUS) [ 64 ] was also used to gather participants' perception on the CLF usability. Except for the SUS, the infrastructure prepared is similar to the one used in a related individual activity also carried out at the 2012 MSW [ 65 ].…”

Section: Formative Evaluationmentioning

confidence: 99%

Involving Users to Improve the Collaborative Logical Framework

Santos

Boticario

2014

The Scientific World Journal

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In order to support collaboration in web-based learning, there is a need for an intelligent support that facilitates its management during the design, development, and analysis of the collaborative learning experience and supports both students and instructors. At aDeNu research group we have proposed the Collaborative Logical Framework (CLF) to create effective scenarios that support learning through interaction, exploration, discussion, and collaborative knowledge construction. This approach draws on artificial intelligence techniques to support and foster an effective involvement of students to collaborate. At the same time, the instructors' workload is reduced as some of their tasks—especially those related to the monitoring of the students behavior—are automated. After introducing the CLF approach, in this paper, we present two formative evaluations with users carried out to improve the design of this collaborative tool and thus enrich the personalized support provided. In the first one, we analyze, following the layered evaluation approach, the results of an observational study with 56 participants. In the second one, we tested the infrastructure to gather emotional data when carrying out another observational study with 17 participants.

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Challenges for Inclusive Affective Detection in Educational Scenarios

Cited by 13 publications

References 20 publications

Towards Emotion Detection in Educational Scenarios from Facial Expressions and Body Movements through Multimodal Approaches

Towards Emotion Detection in Educational Scenarios from Facial Expressions and Body Movements through Multimodal Approaches

Research on Virtual Education, Inclusion, and Diversity

Involving Users to Improve the Collaborative Logical Framework

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