Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach
Given the limited extent of study conducted on the application of ChatGPT in the realm of education, this domain still needs to be explored. Consequently, the primary objective of this study is to evaluate the impact of factors within the extended value-based adoption model (VAM) and to delineate th...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Array |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005624000055 |
| _version_ | 1827321182512218112 |
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| author | Arif Mahmud Afjal Hossan Sarower Amir Sohel Md Assaduzzaman Touhid Bhuiyan |
| author_facet | Arif Mahmud Afjal Hossan Sarower Amir Sohel Md Assaduzzaman Touhid Bhuiyan |
| author_sort | Arif Mahmud |
| collection | DOAJ |
| description | Given the limited extent of study conducted on the application of ChatGPT in the realm of education, this domain still needs to be explored. Consequently, the primary objective of this study is to evaluate the impact of factors within the extended value-based adoption model (VAM) and to delineate the individual contributions of these factors toward shaping the attitudes of university students regarding the utilization of ChatGPT for instructional purposes. This investigation incorporates dimensions such as social influence, self-efficacy, and personal innovativeness to augment the VAM. This augmentation aims to identify components where a hybrid approach, integrating partial least squares (PLS), artificial neural networks (ANN), deep neural networks (DNN), and classification algorithms, is employed to accurately discern both linear and nonlinear correlations. The data for this study were obtained through an online survey administered to university students, and a purposive sample technique was employed to select 369 valid responses. Following the initial data preparation, the assessment process comprised three successive stages: PLS, ANN, DNN and classification algorithms analysis. Intention is influenced by attitude, which is predicted by perceived usefulness, perceived enjoyment, social influence, self-efficacy, and personal innovativeness. Moreover, personal innovativeness has the maximum contribution to attitude followed by self-efficacy, enjoyment, usefulness, social influence, technicality, and cost. These findings will support the creation and prioritization of student-centered educational services. Additionally, this study can contribute to creating an efficient learning management system to enhance students' academic performance and professional efficiency. |
| first_indexed | 2024-03-07T14:00:25Z |
| format | Article |
| id | doaj.art-c038d20582cd46dfaf882966b3a81708 |
| institution | Directory Open Access Journal |
| issn | 2590-0056 |
| language | English |
| last_indexed | 2024-04-25T01:00:52Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj.art-c038d20582cd46dfaf882966b3a817082024-03-11T04:11:05ZengElsevierArray2590-00562024-03-0121100339Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approachArif Mahmud0Afjal Hossan Sarower1Amir Sohel2Md Assaduzzaman3Touhid Bhuiyan4Corresponding author.; Department of Computer Science and Engineering, Daffodil International University, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, BangladeshGiven the limited extent of study conducted on the application of ChatGPT in the realm of education, this domain still needs to be explored. Consequently, the primary objective of this study is to evaluate the impact of factors within the extended value-based adoption model (VAM) and to delineate the individual contributions of these factors toward shaping the attitudes of university students regarding the utilization of ChatGPT for instructional purposes. This investigation incorporates dimensions such as social influence, self-efficacy, and personal innovativeness to augment the VAM. This augmentation aims to identify components where a hybrid approach, integrating partial least squares (PLS), artificial neural networks (ANN), deep neural networks (DNN), and classification algorithms, is employed to accurately discern both linear and nonlinear correlations. The data for this study were obtained through an online survey administered to university students, and a purposive sample technique was employed to select 369 valid responses. Following the initial data preparation, the assessment process comprised three successive stages: PLS, ANN, DNN and classification algorithms analysis. Intention is influenced by attitude, which is predicted by perceived usefulness, perceived enjoyment, social influence, self-efficacy, and personal innovativeness. Moreover, personal innovativeness has the maximum contribution to attitude followed by self-efficacy, enjoyment, usefulness, social influence, technicality, and cost. These findings will support the creation and prioritization of student-centered educational services. Additionally, this study can contribute to creating an efficient learning management system to enhance students' academic performance and professional efficiency.http://www.sciencedirect.com/science/article/pii/S2590005624000055AttitudeChatGPTPersonal innovativenessPartial least squareValue-based adoption model |
| spellingShingle | Arif Mahmud Afjal Hossan Sarower Amir Sohel Md Assaduzzaman Touhid Bhuiyan Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach Array Attitude ChatGPT Personal innovativeness Partial least square Value-based adoption model |
| title | Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach |
| title_full | Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach |
| title_fullStr | Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach |
| title_full_unstemmed | Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach |
| title_short | Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach |
| title_sort | adoption of chatgpt by university students for academic purposes partial least square artificial neural network deep neural network and classification algorithms approach |
| topic | Attitude ChatGPT Personal innovativeness Partial least square Value-based adoption model |
| url | http://www.sciencedirect.com/science/article/pii/S2590005624000055 |
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