Using LSTM to Identify Help Needs in Primary School Scratch Students
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/23/12869 |
| _version_ | 1827592430018363392 |
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| author | Luis Eduardo Imbernón Cuadrado Ángeles Manjarrés Riesco Félix de la Paz López |
| author_facet | Luis Eduardo Imbernón Cuadrado Ángeles Manjarrés Riesco Félix de la Paz López |
| author_sort | Luis Eduardo Imbernón Cuadrado |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T01:54:27Z |
| format | Article |
| id | doaj.art-0cae891f07f44e87bdebf4f832abc88d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:54:27Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0cae891f07f44e87bdebf4f832abc88d2023-12-08T15:12:00ZengMDPI AGApplied Sciences2076-34172023-11-0113231286910.3390/app132312869Using LSTM to Identify Help Needs in Primary School Scratch StudentsLuis Eduardo Imbernón Cuadrado0Ángeles Manjarrés Riesco1Félix de la Paz López2Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia, 28040 Madrid, SpainDepartment of Artificial Intelligence, Universidad Nacional de Educación a Distancia, 28040 Madrid, SpainDepartment of Artificial Intelligence, Universidad Nacional de Educación a Distancia, 28040 Madrid, SpainIn 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.https://www.mdpi.com/2076-3417/13/23/12869distance calculationblock-based programming languageLSTM modelteaching with Scratchethics in AI |
| spellingShingle | Luis Eduardo Imbernón Cuadrado Ángeles Manjarrés Riesco Félix de la Paz López Using LSTM to Identify Help Needs in Primary School Scratch Students Applied Sciences distance calculation block-based programming language LSTM model teaching with Scratch ethics in AI |
| title | Using LSTM to Identify Help Needs in Primary School Scratch Students |
| title_full | Using LSTM to Identify Help Needs in Primary School Scratch Students |
| title_fullStr | Using LSTM to Identify Help Needs in Primary School Scratch Students |
| title_full_unstemmed | Using LSTM to Identify Help Needs in Primary School Scratch Students |
| title_short | Using LSTM to Identify Help Needs in Primary School Scratch Students |
| title_sort | using lstm to identify help needs in primary school scratch students |
| topic | distance calculation block-based programming language LSTM model teaching with Scratch ethics in AI |
| url | https://www.mdpi.com/2076-3417/13/23/12869 |
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