01885nas a2200241   4500000000100000000000100001008004100002260001200043653003500055653002900090653002700119653002300146100002000169700001800189700002600207700002500233245011200258856005800370300001000428490000600438520118500444022001401629       2024                            d  c03/202410aAutomated Short Answer Scoring10aHybrid Transfer Learning10aStudent Answer Dataset10aTrustworthy System1 aMartinus Maslim1 aHei-Chia Wang1 aCendra Devayana Putra1 aYulius Denny Prabowo00aA Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method  uhttps://www.ijimai.org/journal/bibcite/reference/3418  a37-450 v83 aTo measure the quality of student learning, teachers must conduct evaluations. One of the most efficient modes of evaluation is the short answer question. However, there can be inconsistencies in teacher-performed manual evaluations due to an excessive number of students, time demands, fatigue, etc. Consequently, teachers require a trustworthy system capable of autonomously and accurately evaluating student answers. Using hybrid transfer learning and student answer dataset, we aim to create a reliable automated short answer scoring system called Hybrid Transfer Learning for Automated Short Answer Scoring (HTL-ASAS). HTL-ASAS combines multiple tokenizers from a pretrained model with the bidirectional encoder representations from transformers. Based on our evaluation of the training model, we determined that HTL-ASAS has a higher evaluation accuracy than models used in previous studies. The accuracy of HTL-ASAS for datasets containing responses to questions pertaining to introductory information technology courses reaches 99.6%. With an accuracy close to one hundred percent, the developed model can undoubtedly serve as the foundation for a trustworthy ASAS system.  a1989-1660