Forming automatic groups of learners using particle swarm optimization for applications of differentiated instruction

Zervoudakis, Konstantinos; Mastrothanasis, Konstantinos; Tsafarakis, Stelios

doi:10.1002/cae.22191

Cited by 21 publications

(7 citation statements)

References 61 publications

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“…In this research, it is assumed that n schoolchildren distinguished by m characteristics, are to be separated into a maximum of k clusters. In this fashion, a potential solution is a matrix of k × (m + 1) elements, as Zervoudakis, Mastrothanasis and Tsafarakis (2020) suggest. As a result, each cluster is represented by each row of a potential solution.…”

Section: Methodsmentioning

confidence: 99%

A bio-inspired computational classifier system for the evaluation of children’s theatrical anxiety at school

et al. 2023

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Theatrical performance constitutes a complicated way for students to express and to communicate with each other, since it targets both various artistic and educational goals. Even though it constitutes a top moment of students’ expression, several students do not feel comfortable when participating in such cultural activities, as performance anxiety, a negative emotional experience stemming from the public audience exposure, affects them. The aim of this research is to apply and evaluate a student segmentation technique with the help of bio-inspired computational intelligence, for identifying high levels of performance anxiety at schoolchildren. A Mayfly-based clustering optimization algorithm is applied on a dataset with 774 instances of students to classify them according to their levels of emotions and performance anxiety that are developed during the event. A comparison with a genetic algorithm as well as particle swarm optimization shows that the proposed method is distinguished by superior categorization capabilities. The findings demonstrate the effective dissimilar student groups formation, with the members of each being distinguished by similar characteristics in terms of emotions and performance anxiety, highlighting the ones with unmanageable emotional experiences. Therefore, the drama educator is able to effortlessly detect, manage students and develop coping practices in those at risk, by acknowledging each group’s characteristics.

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Section: Methodsmentioning

confidence: 99%

A bio-inspired computational classifier system for the evaluation of children’s theatrical anxiety at school

et al. 2023

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“…A subject of interest for future study is the investigation, formulation, and development of clusters based on the profile of special education teachers according to their self-efficacy beliefs. Approximation algorithms from the field of computational intelligence, can be used to form approximately similar groups of individuals, based on their common characteristics in the field of education (Chikh & Hank, 2016;Zervoudakis, Mastrothanasis, & Tsafarakis, 2020).…”

Section: Limitations and Further Researchmentioning

confidence: 99%

Secondary special education teachers’ beliefs towards their teaching self-efficacy

Mastrothanasis

Zervoudakis

Xafakos³

2021

Ppej

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The self-efficacy of special education teachers is a key aspect of the educational process for inclusive learning and co-teaching. However, research on self-efficacy perceptions in the area of special education and particularly in the field of parallel support is very limited. The aim of this research is to adapt a scale measuring the beliefs of special education teachers' self-efficacy in supporting students who need parallel support in secondary education and therefore to investigate those beliefs, as well as the factors that affect them. The study involved 147 special educators for supporting students in Greek junior high schools or high schools. The "Teachers' Sense of Efficacy Scale" (TSES) by Tschannen-Moran & Woolfolk-Hoy ( 2001) was adapted to study and investigate the research questions. The results demonstrate that special education teachers report high levels of teaching self-efficacy with male special education teachers being distinguished by significantly higher self-efficacy levels compared to females. Moreover, the results revealed a positive correlation between the experience of participants and their levels of self-efficacy, a small negative correlation between the number of supported students and their levels of self-efficacy regarding teaching strategies and a medium negative correlation between the number of supported students and their levels of self-efficacy regarding student management.

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“…Zervoudakis et al [25] present a method that uses computational intelligence techniques to classify students according to the principles of differentiated instruction. They apply a clustering algorithm based on particle swarm optimization to two data sets that emerge from the holistic assessment of the students' particular characteristics Interaction Design and Architecture(s) Journal -IxD&A, N.49, 2021, pp.…”

Section: Related Workmentioning

confidence: 99%

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

Sánchez¹,

Ordóñez²,

Duque³

2021

IxD&A

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Considering that group formation is one of the key processes when developing activities in collaborative learning contexts, this paper aims to propose a technique based on an approach of genetic algorithms to achieve homogeneous groups, considering the students' personality traits as grouping criteria. For its validation, an experiment was designed with 132 first semesters engineering students, quantifying their personality traits through the “Big Five Inventory”, forming workgroups and developing a collaborative activity in initial Programming courses. The experiment made it possible to compare the results obtained by the students applying the proposed approach to those obtained through other group formation strategies. It was demonstrated through the experiment that the homogeneous groups generated by the proposed technique produce better academic results compared to the grouping technique by students’ preference, traditionally used by the teachers when developing a collaborative activity.

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Forming automatic groups of learners using particle swarm optimization for applications of differentiated instruction

Cited by 21 publications

References 61 publications

A bio-inspired computational classifier system for the evaluation of children’s theatrical anxiety at school

A bio-inspired computational classifier system for the evaluation of children’s theatrical anxiety at school

Secondary special education teachers’ beliefs towards their teaching self-efficacy

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

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