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Academic literature on the topic 'Descriptive-Answer'

Author: Grafiati
Published: 6 June 2025
Last updated: 10 June 2025

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Journal articles on the topic "Descriptive-Answer"

1

YOON, Yeo-Chan, Chang-Ki LEE, Hyun-Ki KIM, Myung-Gil JANG, Pum Mo RYU, and So-Young PARK. "Descriptive Question Answering with Answer Type Independent Features." IEICE Transactions on Information and Systems E95.D, no. 7 (2012): 2009–12. http://dx.doi.org/10.1587/transinf.e95.d.2009.

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2

Sk, Asif Akram*1 Mousumi Saha2 &. Tamasree Biswas3. "EVALUATION OF DESCRIPTIVE ANSWER SHEET USING ARTIFICIAL INTELLIGENCE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 4 (2019): 184–86. https://doi.org/10.5281/zenodo.2650942.

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Automating the task of scoring descriptive answer is considered. Due to increasing number of courses and appearing students many hours of examiner and a lot of efforts are required for effective evaluation. Computer and technologies can be used to solve such complex problem. The goal is to evaluate and assign scores to descriptive answer which are comparable to those of human assigned score by coupling AI technologies. This process involves extraction and segmentation of words, removal of stop words, stemming. The scoring system is based on Machine Learning technology and Natural Language Processing. As a result the computer can assign score as good as human being.
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3

K A, Shirien, Neethu George, and Surekha Mariam Varghese. "Descriptive Answer Script Grading System using CNN-BiLSTM Network." International Journal of Recent Technology and Engineering 9, no. 5 (2021): 139–44. http://dx.doi.org/10.35940/ijrte.e5212.019521.

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Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren’t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to learn local information of words and capture long-term dependency information of contexts on the Tensorflow and Keras deep learning framework. The embedding semantic representation of texts can be used for computing semantic similarities between pieces of texts and to grade them based on the similarity score. The experiment used methods for data optimization, such as data normalization and dropout, and tested the model on an Automated Student Evaluation Short Response Scoring, a commonly used public dataset. By comparing with the existing systems, the proposed model has achieved the state-of-the-art performance and achieves better results in the accuracy of the test dataset.
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4

Kaur, Amarjeet, and M. Sasi Kumar. "High Precision Latent Semantic Evaluation for Descriptive Answer Assessment." Journal of Computer Science 14, no. 10 (2018): 1293–302. http://dx.doi.org/10.3844/jcssp.2018.1293.1302.

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5

Shirien, K. A., George Neethu, and Surekha Mariam Varghese Dr. "Descriptive Answer Script Grading System using CNN-BiLSTM Network." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 5 (2021): 139–44. https://doi.org/10.35940/ijrte.E5212.019521.

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<strong>Abstract</strong>&mdash;Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren&rsquo;t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to learn local information of words and capture long-term dependency information of contexts on the Tensorflow and Keras deep learning framework. The embedding semantic representation of texts can be used for computing semantic similarities between pieces of texts and to grade them based on the similarity score. The experiment used methods for data optimization, such as data normalization and dropout, and tested the model on an Automated Student Evaluation Short Response Scoring, a commonly used public dataset. By comparing with the existing systems, the proposed model has achieved the state-of-the-art performance and achieves better results in the accuracy of the test dataset.
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6

Kosisochukwu, Azubogu, Chibuogu Asogwa Emmanuel, Charles Ezeugbor Ifeanyi, Okwuchukwu Ejike Chukwuogo, and Nduaku Onyeizu Macdonald. "Development of Natural Language Processing-Based Descriptive Answer Evaluation Platform (Gradescriptive)." Engineering and Technology Journal 9, no. 08 (2024): 4958–65. https://doi.org/10.5281/zenodo.13578870.

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The manual method of descriptive answer evaluation inherently comes with a lot of problems like the stressful nature of the task, the subjectivity of the grading process as well as the delayed delivery of results. This research involved the development of a computer-based test platform utilizing Natural Language Processing (NLP) as a transformative solution for evaluating descriptive answer examinations. The motivation for this project are the issues of slow turnaround times, potential bias, and limited scalability faced in the manual method of evaluating descriptive answers. Leveraging a state-of-the-art large language model, the MERN (MongoDB, Express.js, React.js and Node.js) stack and Cascading Style Sheets (CSS), a system that meticulously analyzes student responses using criteria like textual semantic similarity, keyword matching and answer length, was developed. The results of the project include timely and accurate feedback, alleviating anxieties and uncertainties around students&rsquo; performances. It showed that descriptive questions can evaluate students' critical thinking, problem-solving, and creativity, unlike objective tests. Meanwhile, lecturers are relieved of the immense stress associated with traditional manual grading, fostering a more positive and productive learning environment.
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7

Kawahara, Seishi. "Preliminary classification of free descriptive answer about resilience in university students." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): 3EV—124–3EV—124. http://dx.doi.org/10.4992/pacjpa.79.0_3ev-124.

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8

Grabowsky, Adelia. "Smartphone Use to Answer Clinical Questions: A Descriptive Study of APNs." Medical Reference Services Quarterly 34, no. 2 (2015): 135–48. http://dx.doi.org/10.1080/02763869.2015.1019320.

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9

Paramitha, Thifal Ayu, Kristianto Usman, and Amril Maruf Siregar. "Kajian Implementasi Rencana Mutu Kontrak pada Proyek Irigasi Berdasarkan Metode Analisis Deskriptif." Jurnal Rekayasa Sipil dan Desain 11, no. 4 (2024): 659–66. https://doi.org/10.23960/jrsdd.v11i4.3697.

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Project quality management is a process to keep the project still fulfilled the quality level based on the demand or plan. Quality Plan Agreement is Quality Management System document that is very important in construction project. This research’s aims are to assess the quality implementation level based on Descriptive Analysis Method on Upgrading Irigation Area Project Way Sekampung, Ground Sub-Job. Primary data is obtained by providing questionnaries within 39 questions to 8 respondents and secondary data is obtained by observation and documentation in project site. Descriptive Analysis obtaining 51,28% respondents answer Good; 27,56% respondents answer Very Good; 16,67% respondents answer Acceptable; 4,17% respondents answer Not Good; and 0,32% respondents answer Very Not Good.
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10

Gayval, Bhagat, and Vanita Mhaske. "Evaluation of Descriptive Answer by using Probability Approach, Cosine Similarity and Pretrained model." JOURNAL OF SCIENTIFIC RESEARCH 67, no. 02 (2023): 105–9. http://dx.doi.org/10.37398/jsr.2023.670212.

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Evaluation of descriptive answers is important for analyzing the growth of students. It may be helpful for a job interview, for academic purposes, and in many more fields. In this research we discussed the importance of evaluating descriptive answers for analyzing student growth and how it is useful in various fields. With the increase in online exams due to the pandemic, objective-type questions are evaluated through different software, but there is a lack of system for evaluating descriptive answers.As manual evaluation is time-consuming, the probability approach is used in this research, which is compared with a pre trained model and cosine similarity approach.In this research, we have used a probability approach, a pre-trained model, a cosine similarity approach, and compared it with a manually assigned score by a subject expert. The analysis concludes that the probability approach provides efficient results compared to other methods.
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