Trends in automated writing evaluation systems research for teaching, learning, and assessment: A bibliometric analysis

This bibliometric analysis attempts to map out the scientific literature on automated writing evaluation (AWE) systems for teaching, learning, and assessment. A total of 170 documents published between 2002 and 2021 in Social Sciences Citation Index journals were reviewed from four dimensions, namely size (productivity and citations), time (publication growth), space (geographical distribution and publication venues), and composition (topic development). Overall findings show an increasing or expanding trend in all four dimensions, which is likely to continue for the next several years. The field has also shown movement toward methodological and theoretical maturity, especially during the past five years. This study has also provided strong evidence of the positive impact of AWE tools on the different dependent variables, such as writing accuracy, writing quality, and plagiarism. In terms of topic development, data shows that the field has significantly expanded and has maintained a sustained interest in popular research topics. However, a great majority of them remain underexplored. Practical, theoretical, and methodological implications and directions for future studies are discussed.

Automated summary

This bibliometric analysis examines the scientific literature on automated writing evaluation (AWE) systems for teaching, learning, and assessment. The analysis covers 170 documents published between 2002 and 2021 in Social Sciences Citation Index journals, reviewing the productivity, citations, publication growth, geographical distribution, publication venues, and topic development. The findings indicate an increasing trend in all dimensions and suggest continued growth in the field. The study also highlights the positive impact of AWE tools on writing accuracy, quality, and plagiarism detection.