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

Area of Science:

• Educational Psychology
• Data Science
• Adult Learning

Background:

• Understanding adult learner diversity is crucial for effective educational strategies.
• Existing datasets may not capture the nuanced factors influencing adult learning.
• Meta-learning parameters offer insights into individual learning processes.

Purpose of the Study:

• To introduce and describe the 'Learning Meta-Learning' (LML) dataset.
• To provide raw data on adult learner characteristics for research.
• To facilitate the redesign of academic curricula and professional training.

Main Methods:

• Data collected via anonymous online surveys from 1021 university students in Bangladesh.
• Convenience sampling and Simple Random Sampling methods were employed.
• Dataset includes categorical and continuous data on 7 meta-learning parameters.

Main Results:

• The LML dataset comprises data on age, gender, illusion of competence, sleep, chronotype, imposter phenomenon, and multiple intelligences.
• Descriptive statistics (mean, standard deviation, frequency, percentage) are provided for all parameters.
• The dataset is stored in an Excel file with the questionnaire accessible.

Conclusions:

• The LML dataset is valuable for studying andragogy and heutagogy in adult learners.
• Findings can inform the redesign of educational materials to accommodate learner diversity.
• This resource supports research into optimizing learning and teaching methodologies.