Machine learning algorithm for grading open-ended physics questions in Turkish
- PDF / 1,915,198 Bytes
- 24 Pages / 439.37 x 666.142 pts Page_size
- 65 Downloads / 158 Views
Machine learning algorithm for grading open-ended physics questions in Turkish Ayşe Çınar, et al. [full author details at the end of the article] Received: 16 November 2019 / Accepted: 30 January 2020/ # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Worldwide, open-ended questions that require short answers have been used in many exams in fields of science, such as the International Student Assessment Program (PISA), the International Science and Maths Trends Research (TIMSS). However, multiple-choice questions are used for many exams at the national level in Turkey, especially high school and university entrance exams. This study aims to develop an objective and useful automatic scoring model for open-ended questions using machine learning algorithms. Within the scope of this aim, an automated scoring model construction study was conducted on four Physics questions at a University level course with the participation of 246 undergraduate students. The short-answer scoring was handled through an approach that addresses students’ answers in Turkish. Model performing machine learning classification techniques such as SVM (Support Vector Machines), Gini, KNN (k-Nearest Neighbors), and Bagging and Boosting were applied after data preprocessing. The score indicated the accuracy, precision and F1-Score of each predictive model of which the AdaBoost.M1 technique had the best performance. In this paper, we report on a short answer grading system in Turkish, based on a machine learning approach using a constructed dataset about a Physics course in Turkish. This study is also the first study in the field of open-ended exam scoring in Turkish. Keywords Machine learning . Automatic short answer grading . Short-answer scoring
Highlights • In this study, a very high performance of the AdaBoost.M1 algorithm was observed in the scoring of four physics questions which were quite different and difficult. • In the evaluation of scoring of open-ended questions by using machine learning algorithms, the systems imitate the field expert. It was constructed with the methods closest to the human scoring in this research. • In the case of open-ended questions in the selection and placement exam taking place at the national level in Turkey, the AdaBoost.M1 technique will be shown to be successful. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10639-02010128-0) contains supplementary material, which is available to authorized users.
Education and Information Technologies
1 Introduction Nowadays, technological developments are progressing rapidly, and this process also affects education. One of the most important reflections of these effects is the ability to measure knowledge (Jayashankar and Sridaran 2017). Assessment and evaluation play a central role in the education process. The assessment and evaluation used in determining the students’ knowledge and level are very important for both the students and the teacher. A reliable and valid assessment tool not on
Data Loading...
