FER in Primary School Children for Affective Robot Tutors

Cuadrado, Luis-Eduardo Imbernón; Riesco, Angeles Manjarrés; López, Félix de la Paz

doi:10.1007/978-3-030-19651-6_45

Cited by 5 publications

(3 citation statements)

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“…When robots are involved in child tutoring, they must be able to detect their mood and basic emotions to react accordingly. In [50], a facial expression recognition model is built and integrated into a complete system for the development of this kind of robots. The model is able to distinguish six types of emotions: anger, fear, joy, sadness, disgust and surprise, as well as an additional neutral state.…”

Section: Building DL Applicationsmentioning

confidence: 99%

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Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

et al. 2020

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Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in

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Section: Building DL Applicationsmentioning

confidence: 99%

“…Another example of the use of robots in education is the work in [50] that develops a tool to support education through social interaction. Authors use in their research, facial recognition of emotional expressions, aimed at improving ARTIE, an integrated environment for the development of affective robot tutors.…”

Section: Education and Social Robotsmentioning

confidence: 99%

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

et al. 2020

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“…The work presented here was carried out in the context of the project Affective Robot Tutor Integrated Environment (ARTIE), in which we are developing an environment for configuring an affective robot tutor, to be used to teach block-based programming in primary school education. This environment must be able to identify when a student needs help, what their emotional state is, and how the robot should act according to each situation and emotional state [13,14]. Therefore, the robot is "affective" because it deals with or arouses emotions.…”

Section: Introductionmentioning

confidence: 99%

Using LSTM to Identify Help Needs in Primary School Scratch Students

Imbernón Cuadrado,

Manjarrés Riesco,

de la Paz López

2023

Applied Sciences

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In the last few years, there has been increasing interest in the use of block-based programming languages as well as in the ethical aspects of Artificial Intelligence (AI) in primary school education. In this article, we present our research on the automatic identification of the need for assistance among primary school children performing Scratch exercises. For data collection, user experiences have been designed to take into account ethical aspects, including gender bias. Finally, a first-in-class distance calculation method for block-based programming languages has been used in a Long Short-Term Memory (LSTM) model, with the aim of identifying when a primary school student needs help while he/she carries out Scratch exercises. This model has been trained twice: the first time taking into account the gender of the students, and the second time excluding it. The accuracy of the model that includes gender is 99.2%, while that of the model that excludes gender is 91.1%. We conclude that taking into account gender in training this model can lead to overfitting, due to the under-representation of girls among the students participating in the experiences, making the model less able to identify when a student needs help. We also conclude that avoiding gender bias is a major challenge in research on educational systems for learning computational thinking skills, and that it necessarily involves effective and motivating gender-sensitive instructional design.

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