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

Sonia Corraya1, Shamim Al Mamun1, M Shamim Kaiser1

  • 1Institute of Information Technology, Jahangirnagar University, Bangladesh.

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|December 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the "Learning Meta-Learning" dataset, featuring diverse adult learner data to inform educational design. The dataset aids in understanding learner characteristics for improved curriculum development and teaching strategies.

Keywords:
ChronotypeIllusion of competenceImposter phenomenonLearners’ biosocial parameterMultiple Intelligence

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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.