LML-Learning Meta-Learning Dataset_update version

The LML dataset presented in this paper contains both categorical and continuous data for 7 meta-learning parameters: age, gender, degree of illusion of competence, sleep duration, chronotype, experience of imposter phenomenon and multiple intelligence. Convenience sampling and simple random sampling method are used to structure the volunteered anonymous online survey data collection project for LML dataset creation. Survey questionnaires are set to explore adult learners' (age over 18) meta-learning parameters. The responses from the 54 survey questionnaires contains raw data from 1021 current university students from 11 universities of Bangladesh.

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 re-designed with focusing 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 pedagogy.

Ethics statements Ethical approval (Involvement of Human Subjects) were obtained from the Biosafety, Biosecurity and Ethical Clearance Committee, Jahangirnagar University (reference no. is BBEC,JU/M 2022/01 (18)). 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.

Credit author statement Sonia Corraya: Conceptualization, Methodology, Data curation, Visualization, Writing – original draft; Professor Shamim Al Mamun: Supervision; Professor M. Shamim Kaiser: Supervision.

This dataset belongs to Authors, Institute of Information Technology, Jahangirnagar University, savar, Dhaka-1342, Bangladesh.