27,088 research outputs found

    Deep learning based Arabic short answer grading in serious games

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    Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research

    Development of an Automated Scoring Model Using SentenceTransformers for Discussion Forums in Online Learning Environments

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    Due to the limitations of public datasets, research on automatic essay scoring in Indonesian has been restrained and resulted in suboptimal accuracy. In general, the main goal of the essay scoring system is to improve execution time, which is usually done manually with human judgment. This study uses a discussion forum in online learning to generate an assessment between the responses and the lecturer\u27s rubric in the automated essay scoring. A SentenceTransformers pre-trained model that can construct the highest vector embedding was proposed to identify the semantic meaning between the responses and the lecturer\u27s rubric. The effectiveness of monolingual and multilingual models was compared. This research aims to determine the model\u27s effectiveness and the appropriate model for the Automated Essay Scoring (AES) used in paired sentence Natural Language Processing tasks. The distiluse-base-multilingual-cased-v1 model, which employed the Pearson correlation method, obtained the highest performance. Specifically, it obtained a correlation value of 0.63 and a mean absolute error (MAE) score of 0.70. It indicates that the overall prediction result is enhanced when compared to the earlier regression task research

    Towards Better Support for Machine-Assisted Human Grading of Short-Text Answers

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    This paper aims at tools to help teachers to grade short text answers submitted by students. While many published approaches for short-text answer grading target on a fully automated process suggesting a grading result, we focus on supporting a teacher. The goal is rather to help a human grader and to improve transparency rather than replacing the human by an Oracle. This paper provides a literature overview of the numerous approaches of short text answer grading which were proposed throughout the years. This paper presents two novel approaches (answer completeness and natural variability) and evaluates these based on published exam data and several assessments collected at our university

    Practice and Assessment of Reading Classes Using Moodle

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    This research paper details the extensive use of Computer Assisted Language Learning (CALL) for a content-based reading syllabus at Gunma University, through the software program Moodle (Modular Object-Oriented Dynamic Learning Environment ), a free and open-source software learning management system used at Gunma University.   The research basis of this paper is within the sphere of Action Research , as a valuable professional development tool (Nunan, 2001) based on this researcher’s perceived valuation of the system and how it could better aid students to perform better in and be more motivated towards their English language and reading studies, introduce new technological skills and abilities, and aid teachers in better preparation, teaching and assessment of reading classes. Moodle enthuses that the Lesson Module ‘enables a teacher to deliver content and/or practice activities in interesting and flexible ways...teachers can choose to increase engagement and ensure understanding by including a variety of questions, such as multiple choice, matching and short answer.’ (Moodle, 2016). Therefore, this paper will ascertain whether the syllabus achieved a greater engagement and enjoyment by the students, and ensured better comprehension and understanding of key tasks and instructions. In addition, it will detail how teachers can benefit course management by employing such technology within the classroom

    Technology-supported assessment

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    Trials & Tribulations Encountered During the Development & Teaching of a Dual-Delivery Format Research Methods Course

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    This paper focuses on developmental and pedagogical/sociological issues related to a doctoral level research methodology course. This course is delivered in two formats, resident (face-to-face) and distance (web-based on Blackboard). Pedagogical, sociological, course development, course delivery, issues and challenges for both formats are discussed. An annotated bibliography is also included
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