Employing Trait Emotional Intelligence in an Adaptive E-learning Environment
Apstrakt
This paper investigates possibilities of harnessing trait emotional intelligence in e-learning ecosystems with particular focus on enhancing adaptivity. The goal of the paper is to develop a model for adaptive e-education based on trait emotional intelligence as a criterion. Employing Trait Emotional Intelligence (TraitEI) as a model and agglomerative hierarchical cluster analysis technique, we identify segments of students attending the online course Digital Marketing within the e-learning platform at University of Belgrade, Faculty of Organizational Sciences, Department for E-Business. We found out three important clusters of students exist: those that have "Average TraitEI, Average Performers"; those that have "Slightly above Average TraitEI, High Performers"; and those that have "Above Average TraitEI, Super Performers." The characteristics that most differentiate the super performers group from the rest is the extent to which cluster members have high score of well-being, self-con...trol, emotionality, sociability and a higher record of global TraitEI profiles in general. Comparative analysis using python machine learning packages is used to validate the relevant clusters based on Achievement Emotions Questionnaire (AEQ). The method is found to be useful tool to assist educators in segmenting students and by doing so, online course designers will have the ability to design and develop intervention course materials tailored to better meet the needs of different groups of students. The contribution of this study is reflected in the fact that the proposed model for segmenting students into relevant groups based on emotional intelligence can provide better adaptivity in e-education. In addition, the study can contribute to building more effective educational strategies in an e-learning environment.
Ključne reči:
Trait emotional intelligence / Digital marketing course / Cluster analysis / Adaptive E-learning / Academic performanceIzvor:
Marketing and Smart Technologies, Icmarktech 2021, Vol 2, 2022, 280, 75-86Izdavač:
- Springer International Publishing Ag, Cham
DOI: 10.1007/978-981-16-9272-7_7
ISSN: 2190-3018
WoS: 000833486400007
Scopus: 2-s2.0-85127047266
Institucija/grupa
Fakultet organizacionih naukaTY - CONF AU - Embaye, Teklehaimanot AU - Bogdanović, Zorica AU - Barać, Dušan AU - Naumović, Tamara AU - Radenković, Božidar PY - 2022 UR - https://rfos.fon.bg.ac.rs/handle/123456789/2384 AB - This paper investigates possibilities of harnessing trait emotional intelligence in e-learning ecosystems with particular focus on enhancing adaptivity. The goal of the paper is to develop a model for adaptive e-education based on trait emotional intelligence as a criterion. Employing Trait Emotional Intelligence (TraitEI) as a model and agglomerative hierarchical cluster analysis technique, we identify segments of students attending the online course Digital Marketing within the e-learning platform at University of Belgrade, Faculty of Organizational Sciences, Department for E-Business. We found out three important clusters of students exist: those that have "Average TraitEI, Average Performers"; those that have "Slightly above Average TraitEI, High Performers"; and those that have "Above Average TraitEI, Super Performers." The characteristics that most differentiate the super performers group from the rest is the extent to which cluster members have high score of well-being, self-control, emotionality, sociability and a higher record of global TraitEI profiles in general. Comparative analysis using python machine learning packages is used to validate the relevant clusters based on Achievement Emotions Questionnaire (AEQ). The method is found to be useful tool to assist educators in segmenting students and by doing so, online course designers will have the ability to design and develop intervention course materials tailored to better meet the needs of different groups of students. The contribution of this study is reflected in the fact that the proposed model for segmenting students into relevant groups based on emotional intelligence can provide better adaptivity in e-education. In addition, the study can contribute to building more effective educational strategies in an e-learning environment. PB - Springer International Publishing Ag, Cham C3 - Marketing and Smart Technologies, Icmarktech 2021, Vol 2 T1 - Employing Trait Emotional Intelligence in an Adaptive E-learning Environment EP - 86 SP - 75 VL - 280 DO - 10.1007/978-981-16-9272-7_7 UR - conv_2732 ER -
@conference{ author = "Embaye, Teklehaimanot and Bogdanović, Zorica and Barać, Dušan and Naumović, Tamara and Radenković, Božidar", year = "2022", abstract = "This paper investigates possibilities of harnessing trait emotional intelligence in e-learning ecosystems with particular focus on enhancing adaptivity. The goal of the paper is to develop a model for adaptive e-education based on trait emotional intelligence as a criterion. Employing Trait Emotional Intelligence (TraitEI) as a model and agglomerative hierarchical cluster analysis technique, we identify segments of students attending the online course Digital Marketing within the e-learning platform at University of Belgrade, Faculty of Organizational Sciences, Department for E-Business. We found out three important clusters of students exist: those that have "Average TraitEI, Average Performers"; those that have "Slightly above Average TraitEI, High Performers"; and those that have "Above Average TraitEI, Super Performers." The characteristics that most differentiate the super performers group from the rest is the extent to which cluster members have high score of well-being, self-control, emotionality, sociability and a higher record of global TraitEI profiles in general. Comparative analysis using python machine learning packages is used to validate the relevant clusters based on Achievement Emotions Questionnaire (AEQ). The method is found to be useful tool to assist educators in segmenting students and by doing so, online course designers will have the ability to design and develop intervention course materials tailored to better meet the needs of different groups of students. The contribution of this study is reflected in the fact that the proposed model for segmenting students into relevant groups based on emotional intelligence can provide better adaptivity in e-education. In addition, the study can contribute to building more effective educational strategies in an e-learning environment.", publisher = "Springer International Publishing Ag, Cham", journal = "Marketing and Smart Technologies, Icmarktech 2021, Vol 2", title = "Employing Trait Emotional Intelligence in an Adaptive E-learning Environment", pages = "86-75", volume = "280", doi = "10.1007/978-981-16-9272-7_7", url = "conv_2732" }
Embaye, T., Bogdanović, Z., Barać, D., Naumović, T.,& Radenković, B.. (2022). Employing Trait Emotional Intelligence in an Adaptive E-learning Environment. in Marketing and Smart Technologies, Icmarktech 2021, Vol 2 Springer International Publishing Ag, Cham., 280, 75-86. https://doi.org/10.1007/978-981-16-9272-7_7 conv_2732
Embaye T, Bogdanović Z, Barać D, Naumović T, Radenković B. Employing Trait Emotional Intelligence in an Adaptive E-learning Environment. in Marketing and Smart Technologies, Icmarktech 2021, Vol 2. 2022;280:75-86. doi:10.1007/978-981-16-9272-7_7 conv_2732 .
Embaye, Teklehaimanot, Bogdanović, Zorica, Barać, Dušan, Naumović, Tamara, Radenković, Božidar, "Employing Trait Emotional Intelligence in an Adaptive E-learning Environment" in Marketing and Smart Technologies, Icmarktech 2021, Vol 2, 280 (2022):75-86, https://doi.org/10.1007/978-981-16-9272-7_7 ., conv_2732 .