大学生为学术目的采用 ChatGPT:偏最小二乘法、人工神经网络、深度神经网络和分类算法方法

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2024-03-01 DOI:10.1016/j.array.2024.100339
Arif Mahmud, Afjal Hossan Sarower, Amir Sohel, Md Assaduzzaman, Touhid Bhuiyan
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引用次数: 0

摘要

鉴于有关 ChatGPT 在教育领域应用的研究有限,这一领域仍有待探索。因此,本研究的主要目的是评估扩展的基于价值的采用模型(VAM)中各因素的影响,并界定这些因素对塑造大学生使用 ChatGPT 教学态度的个体贡献。这项调查纳入了社会影响、自我效能和个人创新性等维度,以增强 VAM。这种增强的目的是确定一些组成部分,在这些组成部分中采用混合方法,将偏最小二乘法 (PLS)、人工神经网络 (ANN)、深度神经网络 (DNN) 和分类算法结合起来,以准确辨别线性和非线性相关性。本研究的数据是通过对大学生进行在线调查获得的,采用目的性抽样技术选出了 369 份有效答卷。在初始数据准备之后,评估过程包括三个连续阶段:PLS、ANN、DNN 和分类算法分析。意向受态度的影响,而态度又受感知有用性、感知乐趣、社会影响、自我效能和个人创新性的预测。此外,个人创新能力对态度的影响最大,其次是自我效能感、享受感、有用性、社会影响、技术性和成本。这些研究结果将有助于创建以学生为中心的教育服务并确定其优先次序。此外,本研究还有助于创建一个高效的学习管理系统,以提高学生的学习成绩和专业效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.

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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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