Automated essay scoring in applied games: Reducing the teacher bandwidth problem in online training
Wim Westera, Open University of the Netherlands ; Mihai Dascalu, University “Politehnica” of Bucharest, Romania ; Hub Kurvers, Open University of the Netherlands ; Stefan Ruseti, Stefan Trausan-Matu, University “Politehnica” of Bucharest, Romania
Computers & Education Volume 123, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd
This paper presents a methodology for applying automated essay scoring in educational settings. The methodology was tested and validated on a dataset of 173 reports (in Dutch language) that students have created in an applied game on environmental policy. Natural Language Processing technologies from the "ReaderBench" framework were used to generate an extensive set of textual complexity indices for each of the reports. Afterwards, different machine learning algorithms were used to predict the scores. By combining binary classification (pass or fail) and a probabilistic model for Precision, a trade-off can be made between validity of automated score prediction (Precision) and the reduction of teacher workload required for manual assessment. It was found from the sample that substantial workload reduction can be achieved, while preserving high precision: allowing for a precision of 95% or higher would already reduce the teacher's workload to 74%; lowering precision to 80% produces a workload reduction of 50%.
Westera, W., Dascalu, M., Kurvers, H., Ruseti, S. & Trausan-Matu, S. (2018). Automated essay scoring in applied games: Reducing the teacher bandwidth problem in online training. Computers & Education, 123(1), 212-224. Elsevier Ltd.