Dataset for: ChatGPT in Academic Learning: Linking Informational Motives to Student Satisfaction

This dataset was compiled for a research study entitled “ChatGPT in Academic Learning: Linking Informational Motives to Student Satisfaction.” This study was motivated by the problem that academic materials in higher education often contain complex concepts, technical terms, or jargon, creating semantic noise among students that causes them to misunderstand academic messages. To prevent this noise, students utilize AI-based communication media, such as ChatGPT, to seek information that can support their academic understanding. Referring to the Uses and Gratifications framework, this study examines: the strength of students’ informational motives in using ChatGPT to understand academic messages, their level of satisfaction with the academic understanding obtained through the use of ChatGPT, and the relationship between such motives and satisfaction. Accordingly, data were collected through the distribution of questionnaires to 100 students from the 2022 cohort at a private university in Indonesia who had used ChatGPT to understand academic messages. All data were collected anonymously and are provided for research transparency and verification purposes. This dataset contains anonymized survey responses from university students. The repository includes raw respondent data in Excel format, SPSS output files for validity, reliability, descriptive statistics, normality, linearity, correlation, and regression analyses, a PDF summary of the statistical results, and a PDF file containing the questionnaire items. The questionnaire was used to measure two variables: informational motives in using ChatGPT to understand academic messages (X) and students' satisfaction with their understanding of academic messages (Y). The questionnaire consists of 12 items, and all items were measured using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). Six items asked respondents to respond to statements related to informational motives in using ChatGPT (X) with various information-need indicators as follows: X1 – “Simple explanations” X2 – “Definitions of technical terms” X3 – “Summaries of materials” X4 – “Practical examples” X5 – “Additional learning resources” X6 – “Rapid comprehension of academic information” The other six items measured students' satisfaction with their understanding of academic messages (Y) using various indicators related to media-use experiences as follows: Y1 – “Clarity of language” Y2 – “Coherence of information flow” Y3 – “Relevance of responses” Y4 – “Response timeliness” Y5 – “Perceived understanding accuracy” Y6 – “Perceived usefulness of information” The analyses conducted include validity and reliability tests to ensure that all questionnaire items are valid and consistent, descriptive statistical analysis to determine the mean and standard deviation of all items (to identify the levels of informational motives and satisfaction), normality and linearity tests as prerequisites for Pearson correlation analysis, correlation analysis to examine the relationship between variables, and regression analysis to determine the effect of variable X on Y, including the direction, significance, and magnitude of the effect.本数据集为一项题为《ChatGPT在学术学习中的应用：将信息动机与学生满意度相挂钩》的研究编制而成。本研究的缘起在于，高等教育阶段的学术材料往往包含复杂概念、专业术语或行话，会在学生群体中引发语义噪声，导致他们对学术信息产生误解。为规避此类噪声，学生常借助基于人工智能的交流媒介，如ChatGPT，以获取能够助力其学术理解的信息。基于使用与满足理论（Uses and Gratifications framework），本研究考察学生使用ChatGPT理解学术信息的信息动机强度、通过ChatGPT获取的学术理解满意度水平，以及此类动机与满意度之间的关联。据此，本研究通过问卷分发的方式，向印度尼西亚某私立大学2022届曾使用ChatGPT辅助学术理解的100名学生采集数据。所有数据均以匿名方式收集，旨在提升研究透明度并可供他人验证。 本数据集包含匿名化的高校学生问卷回复。该仓库包含原始受访者数据（Excel格式）、用于效度、信度、描述性统计、正态性、线性性、相关性及回归分析的SPSS输出文件、统计结果PDF摘要，以及包含问卷题目项的PDF文件。 本问卷用于测量两个变量：使用ChatGPT理解学术信息的信息动机（X），以及学生对其学术信息理解的满意度（Y）。问卷共包含12个题目，所有题目均采用李克特五点量表（five-point Likert scale）进行测量（1=完全不同意，5=完全同意）。其中6个题目用于询问受访者与使用ChatGPT的信息动机（X）相关的表述，涵盖各类信息需求指标，具体如下： X1——“简洁解释” X2——“专业术语定义” X3——“材料概要” X4——“实操案例” X5——“额外学习资源” X6——“快速掌握学术信息” 剩余6个题目则用于测量学生对其学术信息理解的满意度（Y），涵盖各类与媒介使用体验相关的指标，具体如下： Y1——“语言清晰度” Y2——“信息流转连贯性” Y3——“回复相关性” Y4——“回复及时性” Y5——“感知理解准确度” Y6——“感知信息有用性” 本研究开展的分析包括：效度与信度检验，以确保所有问卷题目均具备有效性与一致性；描述性统计分析，用于计算所有题目的均值与标准差（以识别信息动机与满意度水平）；作为皮尔逊相关分析前置条件的正态性与线性性检验；相关性分析，以考察变量间的关联；以及回归分析，用于确定变量X对Y的影响效应，包括影响方向、显著性与效应量。