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. 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.


a b s t r a c t
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.

Value of the Data
• These data can be useful to understand the association among the meta-learning factors of adult learners.• Research findings with this data will help education designer, adult self-learners and learners' meta-data analysts.• Academic education and industrial training program structure might be reviewed based on the analysis findings with this data, to raise the quality of learners' accomplishments.• Underlying patterns of this dataset can direct significant insights about learning trends and the natural grouping of the learners.• The data collection can be replicated in other countries or with other kinds of meta-learning parameters to make a comparison between them.

Data Description
This data article reports survey questionnaire [14] , raw data [14] , and baseline characteristics of the study participants (see Table 1 ).In Table 1 , categorical data are reported as frequency (percentages), and continuous data as mean (standard deviation).LML dataset contain nominal, ordinal, and continuous variables.

Sampling
As per the 47 th Annual Report 2020 (Year of Publication: October 2021) of University Grants Commission of Bangladesh [5] , the total number of students at both public (including affiliated and constituent colleges/madrasas) and private universities is 46,90,876.It was unfeasible to conduct probability sampling on this huge population.For this, Simple Random Sampling (SRS) with Convenience sampling is incorporated for data collection to create this LML dataset.In this SRS and Convenience sampling structure, all universities, classrooms, and students are chosen randomly with convenience.Among the 11 universities, there were 4 general public universities, 4 general private universities, 2 engineering and technology universities and 1 institute of engineering and research.Required standard Sample Size (SS) to represent the population is calculated using Slovin's Formula [6 , 7] .SS can be represented as below: where, P is the known population size and C is the margin of error [6 , 7] .
SS for LML dataset with P = 46,90,876 and C = 0.05 with 95% confidence interval is 400.
The sample for the survey consisted of adult (age > 18) [13] participant who were approached verbally in the classroom and invited to voluntarily participate.Before accessing the survey, participants were provided with an information consent form, and only those who agreed proceeded to answer the survey.As the required standard SS to represent the population is 400, with an expectation that 30% of the people invited to take the survey will actually respond, we planned to approach (400/30% = 1333.33≈) 1300 to 1400 students (approx.).We ended up with 1021 responses, from which it can be said the survey response rate is (1021/1300 to 1021/1400 = ) 78.54% to 72.92%.An exact response rate (or completion rate) cannot be specified for this survey as all the students were approached only verbally in person in the classrooms and were invited to voluntarily participate then or later after class.
Survey questionnaires were made available to the participants through an online Google form link.The inclusion criteria for survey participants are: i. Currently studying at any university in Bangladesh ii.The minimum age is 18 years old

Survey Questionnaires
In the survey questionnaires, for most of the parameters, established and validated questions are adapted.The purpose of the LML dataset is to provide naturalistic self-reported data that is to be linked with individuals' general learning processes.Meta-learning parameter wise survey questionnaires are explained below.
• Age (Question 2): Participants selected a single category that represented their age.The minimum age option in the survey was 18 years, and the maximum age option was above 26 years.• Gender (Question 3): Participants selected one option as their gender identity: "Male," "Female," or "Other/Non-binary".• Degree of Illusion of Competence (Question 4): Participants rated their own degree of 'Illusion of Competence' experience as "mild", "moderate", or "severe".To the best of our knowledge, there exists no validated question for 'Illusion of Competence' measurement.The measures used here were developed for the creation of the LML dataset.• Sleep duration (Question 5): University students fall under the inclusive sleep range of 6-11 hours [8] .The survey participants had a single selection choice for each of the sleep hours in this range.• Chronotype (Questions 6-10): Each of the 5 questions for measuring chronotype had different single-selection options.Information on interpreting the scores is available in the data dictionary of the LML dataset file [14] .Questions were taken from the reduced Morningness-Eveningness Questionnaire (rMEQ) [10] for identifying the chronotype of individuals.Only questions 1, 7, 10, 18, and 19 of the original Morningness-Eveningness Questionnaire [9] are included in the rMEQ [10] .• Experience of Imposter Phenomenon (Questions 11-20): Participants provided their answers regarding their own experience of the 'Imposter Phenomenon' on a 5-point Likert scale ranging from 1 ('Not at all true') to 5 ('Very true').The data dictionary of the LML dataset file [14] contains details on how to interpret the final scores.The selected 10 question numbers from the Clance Imposter Phenomenon questionnaire [11] considering redundancy and relevancy for measuring learning handicap-Imposter Phenomenon, are: 1, 2, 5, 6, 7, 11, 12, 15, 19 and 20.• Multiple Intelligence (Questions 21-55): For each of the 7 sub-intelligences of multiple intelligences, as shown in Fig. 1 , there were 5 questions in the survey, and scores were taken on a 4-point Likert scale ranging from 1 ('Not at all true') to 4 ('Very true').The LML dataset file's [14] data dictionary contains information on how to interpret the scores.After tracing the literature of multiple intelligences [2 , 4] and looking at the previous relevant studies [3] , the scale and questionnaires developed by Chislett and Chapman (20 05-20 06) [1] have been adapted.

Limitations
The sample population of the LML dataset has a female to male ratio of (37.51/62.49= ) 0.60, which at first glance appears to be biased on gender.However, when taking into account the following ratios, this LML dataset is actually a true representation of the Gender Parity Index, a socioeconomic index used to measure how equally both men and women have access to education.The female to male ratio in Bangladesh's tertiary education was (22.8/27.3= ) 0.83 in 2021, according to UNESCO [12] .According to the University Grants Commission of Bangladesh's 47 th Annual Report 2020 (Year of Publication: October 2021), the ratio of female to male university students in Bangladesh is (43/57 = ) 0.75 [5] .
As learners, only current students from 11 different universities in Bangladesh were surveyed using SRS and Convenience sampling.In the future, the dataset will be expanded by including a wider range of learners from vocational training institutes, music schools, art schools, special education, and more.
© 2023 The Author(s).Published by Elsevier Inc.This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) Direct URL to data: 10.5281/zenodo.8112213Instructions for accessing these data: The dataset presented in this article is open for public access.It is mandatory to follow the correct citation guidelines when using this LML dataset.

Table 1
Baseline characteristics of the survey participants.