PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

J

• Accuracy. (2017). Accuracy. In C. Sammut & G. I. Webb (Eds.) Encyclopedia of machine learning and data mining (pp. 1-48). Springer. https: //doi.org/10.1007/978-1-4899-7687-1_3

• Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation coefficient. In Noise reduction in speech processing (pp. 1-4). Springer. https: //doi.org/10.1007/978-3-642-00296-0_5

• Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.

• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https: //doi.org/10.1023/A:1010933404324

• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https: //doi.org/10.1007/BF00994018

• Cozma, M., Butnaru, A. M., & Ionescu, R. T. (2018). Automated essay scoring with string kernels and word embeddings. Computation and Language, 2018, 1-7.

• Cramer, J. S. (2002). The origins of logistic regression. Tinbergen Institute Working Paper No. 2002-119/4. https: //doi.org/10.2139/ssrn.360300

• Crossley, S. A., & McNamara, D. S. (2011). Understanding expert ratings of essay quality: Coh-Metrix analyses of first and second language writing. International Journal of Continuing Engineering Education and Life Long Learning, 21(2-3), 170-191.

• Crossley, S. A., & McNamara, D. S. (2016). Say more and be more coherent: How text elaboration and cohesion can increase writing quality. Journal of Writing Research, 7(3), 351-370.

• Crossley, S. A., Bradfield, F., & Bustamante, A. (2019a). Using human judgments to examine the validity of automated grammar, syntax, and mechanical errors in writing. Journal of Writing Research, 11(2), 251-270.

• Crossley, S. A., Kyle, K., & Dascalu, M. (2019b). The tool for the automatic analysis of cohesion 2.0: Integrating semantic similarity and text overlap. Behavioral Research Methods, 51(1), 14-27. https: //doi.org/10.3758/s13428-018-1142-4

• Darus, S., Stapa, S. H., & Hussin, S. (2003). Experimenting a computer-based essay marking system at Universiti Kebangsaan Malaysia. Jurnal Teknologi, 39(E), 1-18.

• Educational Testing Service. (n.d.). About the e-rater® scoring engine. Retrieved October 30, 2020, from https: //www.ets.org/erater/about

• Foltz, P. W. (2007). Discourse coherence and LSA. In T. K. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of latent semantic analysis (pp. 167-184). Lawrence Erlbaum Associates.

• Govindasamy, P. N., Tan, B. H., & Yong, M. F. (2013). Lower six students’ preferred mode of feedback for essay revision. Malaysian Journal of ELT Research, 9(2), 82-104.

• Janda, H. K., Pawar, A., Du, S., & Mago, V. (2019). Syntactic, semantic and sentiment analysis: The joint effect on automated essay evaluation. IEEE Access, 7, 108486-108503. https: //doi.org/10.1109/ACCESS.2019.2933354

• Kaggle (2012). The Hewlett foundation: Automated essay scoring. Retrieved October 30, 2020, from https: //www.kaggle.com/c/ASAP-AES

• Landauer, T. K., Foltz, P. W., & Laham, D. (1998). Introduction to latent semantic analysis. Discourse Processes, 25, 259-284. https: //doi.org/10.1080/01638539809545028

• Leave-One-Out Cross-Validation. (2011). Leave-One-Out Cross-Validation. In C. Sammut, & G. I. Webb (Eds.) Encyclopedia of machine learning. Springer. https: //doi.org/10.1007/978-0-387-30164-8_469

• LightSide. (2019). LightSide researcher’s workbench. Retrieved January 11, 2021, from http: //ankara.lti.cs.cmu.edu/side

• Malaysian Examination Council. (2014). Malaysian University English Test (MUET) - regulations, test specifications, test format and sample questions. Retrieved October 30, 2020, from https: //www.mpm.edu.my/images/dokumen/calon-peperiksaan/muet/regulation/Regulations_Test_Specifications_Test_Format_and_Sample_Questions.pdf

• Manap, M. R., Ramli, N. F., & Kassim, A. A. M. (2019). Web 2.0 automated essay scoring application and human ESL essay assessment: A comparison study. European Journal of English Language Teaching, 5(1), 146-161. https: //doi.org/ 10.5281/zenodo.3461784

• McNamara, D. S., Crossley, S. A., & McCarthy, P. M. (2010). Linguistic features of writing quality. Written Communication, 27(1), 57-86.

• Measurement Incorporated. (n.d.). Automated Essay Scoring - Project Essay Grade (PEG®). Retrieved October 31, 2020, from https: //www.measurementinc.com/products-services/automated-essay-scoring

• Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26, 3111-3119.

• Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39-41.

• Ng, S. Y., Bong, C. H., Hong, K. S., & Lee, N. K. (2019). Developing an automated essay scorer with feedback (AESF) for Malaysian University English Test (MUET): A design-based research approach. Pertanika Journal of Social Science & Humanities, 27(3), 1451-1468.

• Nguyen, H., & Litman, D. (2018). Argument mining for improving the automated scoring of persuasive essays. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 5892-5899.

• Omar, N., Razali, N. A. M., & Darus, S. (2009) Automated grammar checking of tenses for ESL writing. In P. Wen, Y. Li, L. Polkowski, Y. Yao, S. Tsumoto, & G. Wang (Eds.), Lecture notes in computer science, Vol 5589: Rough Sets and Knowledge Technology (pp. 475-482). Springer. https: //doi.org/10.1007/978-3-642-02962-2_60

• Page, E. B. (1966). The imminence of grading essays by computer. The Phi Delta Kappan, 47(5), 238-243.

• Pearson Education. (2010). Intelligent Essay Assessor (IEA)™ Fact Sheet [Fact sheet ]. Retrieved October 31, 2020, from https: //images.pearsonassessments.com/images/assets/kt/download/IEA-FactSheet-20100401.pdf

• Persing, I., & Ng, V. (2014). Modeling prompt adherence in student essays. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 1, 1534-1543.

• Persing, I., & Ng, V. (2016). Modeling stance in student essays. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 1, 2174-2184.

• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation (No. ICS-8506). California Univ San Diego La Jolla Inst for Cognitive Science.

• Shermis, M. D., & Burstein, J. (2003). Introduction. In M. D. Shermis & J. Burstein (Eds.), Automated essay scoring: A cross-disciplinary perspective (pp. xiii-xvi). Lawrence Erlbaum Associates.

• Maasum, T. N. R. T. M., Stapa, S. H., Omar, N., Aziz, M. J. A., & Darus, S. (2012). Development of an automated tool for detecting errors in tenses. GEMA Online Journal of Language Studies, 12(2), 427- 442.

• Vantage Learning, (n.d.). Intellimetric®. Retrieved October 31, 2020, from http: //www.intellimetric.com/direct

• Wong, W. S., & Bong, C. H. (2019). A study for the development of automated essay scoring (AES) in Malaysian English test environment. International Journal of Innovative Computing, 9(1), 69-78. https: //doi.org/10.11113/ijic.v9n1.220

• Zupanc, K., & Bosnic, Z. (2014). Automated essay evaluation augmented with semantic coherence measures. In R. Kumar, H. Toivonen, J. Pei, J. Z. Huang, & X. Wu (Eds.), 2014 IEEE International Conference on Data Mining (pp. 1133-1138). IEEE Conference Publication. https: //doi.org/10.1109/ICDM.2014.21

• Zupanc, K., & Bosnić, Z. (2017). Automated essay evaluation with semantic analysis. Knowledge-Based Systems, 120, 118-132. https: //doi.org/10.1016/j.knosys.2017.01.006