Assessing students’ handwritten text productions: A two-decades literature review

In the context of early childhood education, students need to acquire fundamental writing skills for their lifelong development. Public schools, especially in low- and middle-income countries, should monitor individual student progress to mitigate the detrimental effects of socioeconomic vulnerabilities in education. Furthermore, the volume of students often overwhelms teachers responsible for assessing handwriting texts and providing feedback. This article conducts a Systematic Literature Review (SLR) focusing on solutions for automatically evaluating students’ handwriting, discussing their performance, future research directions, and areas needing further investigation. We used a mixed-methods approach to conduct our SLR, encompassing a search across four databases (ACM Digital Library, IEEE Xplore, ScienceDirect, and SpringerLink) and employed the snowballing technique. We used the initial set of papers from the database search as the foundation for the subsequent snowballing search. Findings revealed that the studies introduced computational techniques, examined or enhanced existing methods, and developed assessment tools. These solutions predominantly employed techniques such as artificial neural networks and used available datasets comprising handwritten images, answers, or student essays. Only some studies provide low-cost solutions for automatically assessing the writing abilities of underserved public school students. •We reviewed 491 papers, extracting data from 22 focusing on handwriting assessment.•We applied a mixed method using database search and the snowballing technique.•Studies frequently adopt deep learning methods to tackle the handwriting recognition problem.•Studies employ various methods to automatically assess text production, such as ANN, latent semantic analysis, content vector analysis, and CNN.•The mean accuracy for handwriting assessment was notably high at 93.64%, suggesting that the models in this group exhibited good performance.