Automatic Essay Assessment for Blended Learning in Elementary School

Endang Widi Winarni, Endina Putri Purwandari, Siti Hafiza


Blended learning combines traditional face-to-face learning in class and virtual learning. This system requires an evaluation process as a measuring instrument. This paper develops an automated essay correction on a blended learning system for elementary school. Assessment in education is obtaining, organizing, and presenting information about what and how the student learning. Open-answer questions allow teachers to understand the student's answer. Essay questions can be used to train students in conveying information verbally and measure their understanding. The teacher needs more time to examine the essay answers for each student. The essay correction needs to be guided with a scoring rubric as the keyword in the answer key that automatically makes essay corrections for elementary school. This system uses the Rabin Karp method to measure the similarity between answer keys to students' answers. The test was carried out by comparing Mean Absolute Error and Pearson Correlations from various k-gram values. The experiments show this assessment system produces a small error value and good performance in grading the student's answer with a low difference value between automatic assessment and expert judgment. Further research, this system can be applied to evaluate the student learning outcomes in an integrated manner with STEAM elements through blended learning. The use of automatic essay assessment in blended learning can improve elementary school students writing skills in the digital educational environment 4.0.


Automatic essay assessment; blended learning; elementary school; Rabin Karp.

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H. M. Balaha and M. M. Saafan, “Automatic exam correction framework (AECF) for the MCQS, essays, and equations matching,” IEEE Access, vol. 9, no. 1, pp. 32368–32389, 2021, doi: 10.1109/ACCESS.2021.3060940.

A. Moubayed, M. Injadat, A. B. Nassif, H. Lutfiyya, and A. Shami, “E-Learning: Challenges and Research Opportunities Using Machine Learning Data Analytics,” IEEE Access, vol. 6, pp. 39117–39138, 2018, doi: 10.1109/ACCESS.2018.2851790.

C. Senturk, “Effects of the blended learning model on preservice teachers’ academic achievements and twenty-first century skills,” Educ. Inf. Technol., vol. 26, no. 1, pp. 35–48, 2021, doi: 10.1007/s10639-020-10340-y.

H. Syam, M. Basri, A. Abduh, A. A. Patak, and Rosmaladewi, “Hybrid e-learning in Industrial Revolution 4.0 for Indonesia higher education,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 4, pp. 1183–1189, 2019, doi: 10.18517/ijaseit.9.4.9411.

M. Jaya Saragih, R. Mas Rizky Yohannes Cristanto, Y. Effendi, and E. M. Zamzami, “Application of Blended Learning Supporting Digital Education 4.0,” J. Phys. Conf. Ser., vol. 1566, no. 1, pp. 0–6, 2020, doi: 10.1088/1742-6596/1566/1/012044.

J. Moreno and A. F. Pineda, “A Framework for Automated Formative Assessment in Mathematics Courses,” IEEE Access, vol. 8, pp. 30152–30159, 2020, doi: 10.1109/ACCESS.2020.2973026.

M. Liu, Y. Li, W. Xu, and L. Liu, “Automated Essay Feedback Generation and Its Impact on Revision,” IEEE Trans. Learn. Technol., vol. 10, no. 4, pp. 502–513, 2017.

N. T. Thomas, A. Kumar, and K. Bijlani, “Automatic Answer Assessment in LMS Using Latent Semantic Analysis,” Procedia Comput. Sci., vol. 58, pp. 257–264, 2015, doi: 10.1016/j.procs.2015.08.019.

G. Liang, B. W. On, D. Jeong, H. C. Kim, and G. S. Choi, “Automated essay scoring: A siamese bidirectional LSTM neural network architecture,” Symmetry (Basel)., vol. 10, no. 12, pp. 1–16, 2018, doi: 10.3390/sym10120682.

R. Setiadi Citawan, V. Christanti Mawardi, and B. Mulyawan, “Automatic Essay Scoring in E-learning System Using LSA Method with N-Gram Feature for Bahasa Indonesia,” in MATEC Web of Conferences, 2018, vol. 164, doi: 10.1051/matecconf/201816401037.

A. Yudhana, S. -, and A. Djalil, “Implementation of Pattern Matching Algorithm for Portable Document Format,” in International Journal of Advanced Computer Science and Applications, 2017, vol. 8, no. 11, pp. 509–512, doi: 10.14569/ijacsa.2017.081162.

C. L. Frinhani, S. A. A. De Freitas, M. V. Fernandes, and E. Dias Canedo, “An automatic essay correction for an active learning environment,” Proc. IEEE/ACS Int. Conf. Comput. Syst. Appl. AICCSA, vol. 0, pp. 1–6, 2016, doi: 10.1109/AICCSA.2016.7945769.

R. Setiawan, W. Budiharto, I. H. Kartowisastro, and H. Prabowo, “Finding model through latent semantic approach to reveal the topic of discussion in discussion forum,” Educ. Inf. Technol., vol. 25, no. 1, pp. 31–50, 2020, doi: 10.1007/s10639-019-09901-7.

K. Kim, Y. J. Choi, M. Kim, J. W. Lee, D. S. Park, and N. Moon, “Teaching-learning activity modeling based on data analysis,” Symmetry (Basel)., vol. 7, no. 1, pp. 206–219, 2015, doi: 10.3390/sym7010206.

A. Petchprasert, “Utilizing an automated tool analysis to evaluate EFL students’ writing performances,” Asian-Pacific J. Second Foreign Lang. Educ., vol. 6, no. 1, pp. 1–16, 2021, doi: 10.1186/s40862-020-00107-w.

J. M. Carman, “Blended Learning Design: Five Key Ingredients,” in Proceedings of the Seventh IASTED International Conference on Computers and Advanced Technology in Education, 2002, no. October, pp. 491–496.

R. M. Karp and M. O. Rabin, “Efficient randomized pattern-matching algorithms,” IBM J. Res. Dev., vol. 31, no. 2, pp. 249–260, 1987.

S. I. Hakak, A. Kamsin, P. Shivakumara, G. A. Gilkar, W. Z. Khan, and M. Imran, “Exact String Matching Algorithms: Survey, Issues, and Future Research Directions,” IEEE Access, vol. 7, pp. 69614–69637, 2019, doi: 10.1109/ACCESS.2019.2914071.

V. Verma and R. K. Aggarwal, “A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: empirical and theoretical perspective,” Soc. Netw. Anal. Min., vol. 10, no. 1, pp. 1–16, 2020, doi: 10.1007/s13278-020-00660-9.

W. Wang and Y. Lu, “Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model,” IOP Conf. Ser. Mater. Sci. Eng., vol. 324, no. 1, 2018, doi: 10.1088/1757-899X/324/1/012049.

I. Atoum, “Scaled Pearson’s Correlation Coefficient for Evaluating Text Similarity Measures,” Mod. Appl. Sci., vol. 13, no. 10, p. 26, 2019, doi: 10.5539/mas.v13n10p26.

E. W. Winarni and E. P. Purwandari, “The effectiveness of turtle mobile learning application for scientific literacy in elementary school,” J. Educ. e-Learning Res., vol. 6, no. 4, pp. 156–161, 2019, doi: 10.20448/journal.509.2019.64.156.161.



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