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Machine learning and learning analytics are powerful tools that not only support researchers in the detailed measurement and enhancement of learning processes in various learning environments, but also enable the aggregation and synthesis of evidence regarding effective educational practices. This paper describes the development and application of machine learning algorithms aimed at semi-automatic selection of abstracts for a meta-analysis on the effects of simulation-based learning in higher education. The goal was to reduce the workload while also maintaining the transparency and objectivity of the selection process. The algorithms were trained, validated, and tested on a set of 3187 studies on simulation-based learning found in medical and educational databases collected before April 2018. Subsequently, they were utilized to classify abstracts for a follow-up meta-analysis consisting of 2373 studies (published between 2018 and 2020). The aim of training the algorithms was to predict studies' abstract eligibility based on words and combinations of words used in these abstracts. The application of the algorithms reduced the number of studies that had to be manually screened from 2373 to 711. A total of 458 studies from automatically selected abstracts were included in the full-text screening, indicating the high precision of the algorithms (also compared to the performance of human raters). We conclude that machine learning algorithms can be trained and used to classify abstracts for their eligibility, significantly reducing the workload for the researchers without diminishing objectivity and quality when updating systematic literature reviews with or without a meta-analysis.
Chernikova et al. (Tue,) studied this question.