Cargando…

Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters

The ‘Learning Meta-Learning’ dataset presented in this paper contains both categorical and continuous data of adult learners for 7 meta-learning parameters: age, gender, degree of illusion of competence, sleep duration, chronotype, experience of the imposter phenomenon, and multiple intelligences. C...

Descripción completa

Detalles Bibliográficos
Autores principales: Corraya, Sonia, Mamun, Shamim Al, Kaiser, M. Shamim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694062/
http://dx.doi.org/10.1016/j.dib.2023.109777
_version_ 1785153293850247168
author Corraya, Sonia
Mamun, Shamim Al
Kaiser, M. Shamim
author_facet Corraya, Sonia
Mamun, Shamim Al
Kaiser, M. Shamim
author_sort Corraya, Sonia
collection PubMed
description The ‘Learning Meta-Learning’ dataset presented in this paper contains both categorical and continuous data of adult learners for 7 meta-learning parameters: age, gender, degree of illusion of competence, sleep duration, chronotype, experience of the imposter phenomenon, and multiple intelligences. Convenience sampling and Simple Random Sampling methods are used to structure the anonymous online survey data collection voluntarily for LML dataset creation. The responses from the 54 survey questionnaires contain raw data from 1021 current students from 11 universities in Bangladesh. The entire dataset is stored in an excel file and the entire questionnaire is accessible at (10.5281/zenodo.8112213) In this article mean and standard deviation for the participant's baseline attributes are given for scale parameters, and frequency and percentage are calculated for categorical parameters. Academic curriculum, courses as well as professional training materials can be reviewed and redesigned with a focus on the diversity of learners. How the designed courses will be learned by learners along with how they will be taught is a significant point for education in any discipline. As the survey questionnaires are set for adult learners and only current university students have participated in this survey, this dataset is appropriate for study andragogy and heutagogy but not pedagogy.
format Online
Article
Text
id pubmed-10694062
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-106940622023-12-05 Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters Corraya, Sonia Mamun, Shamim Al Kaiser, M. Shamim Data Brief Data Article The ‘Learning Meta-Learning’ dataset presented in this paper contains both categorical and continuous data of adult learners for 7 meta-learning parameters: age, gender, degree of illusion of competence, sleep duration, chronotype, experience of the imposter phenomenon, and multiple intelligences. Convenience sampling and Simple Random Sampling methods are used to structure the anonymous online survey data collection voluntarily for LML dataset creation. The responses from the 54 survey questionnaires contain raw data from 1021 current students from 11 universities in Bangladesh. The entire dataset is stored in an excel file and the entire questionnaire is accessible at (10.5281/zenodo.8112213) In this article mean and standard deviation for the participant's baseline attributes are given for scale parameters, and frequency and percentage are calculated for categorical parameters. Academic curriculum, courses as well as professional training materials can be reviewed and redesigned with a focus on the diversity of learners. How the designed courses will be learned by learners along with how they will be taught is a significant point for education in any discipline. As the survey questionnaires are set for adult learners and only current university students have participated in this survey, this dataset is appropriate for study andragogy and heutagogy but not pedagogy. Elsevier 2023-11-07 /pmc/articles/PMC10694062/ http://dx.doi.org/10.1016/j.dib.2023.109777 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Corraya, Sonia
Mamun, Shamim Al
Kaiser, M. Shamim
Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters
title Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters
title_full Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters
title_fullStr Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters
title_full_unstemmed Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters
title_short Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters
title_sort learning meta-learning (lml) dataset: survey data of meta-learning parameters
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694062/
http://dx.doi.org/10.1016/j.dib.2023.109777
work_keys_str_mv AT corrayasonia learningmetalearninglmldatasetsurveydataofmetalearningparameters
AT mamunshamimal learningmetalearninglmldatasetsurveydataofmetalearningparameters
AT kaisermshamim learningmetalearninglmldatasetsurveydataofmetalearningparameters