ChatGPT vs. Google in terms of navigation performance and self-learning success

Research purpose was to compare students' experiences of learning code via ChatGPT and Google in terms of navigation performance and academic success. The independent variables was age, gender, JavaScript experience, and ChatGPT usage, while the dependent variables was Navigation Performance Score (Disorientation, Satisfaction, Cognitive Load) and Test Score. Data collected from 471 fully distance education students from Web Design and Coding program. Navigation performance was measured using the Hyper-MNP scale developed by Firat and Kabakci Yurdakul (2016). The scale consists of a total of 15 items, 7 of which are positive statements and 8 of which are negative statements. The scale has a three-factor structure including disorientation (6 items), satisfaction (5 items), and cognitive load (4 items). The items are 5-point Likert type. Welch's t-test results showed a significant difference in favor of ChatGPT for all dependent variables.本研究旨在从导航表现与学业成就两个维度，对比学生通过ChatGPT（ChatGPT）与谷歌（Google）学习编程的学习体验。本研究的自变量为年龄、性别、JavaScript编程经验以及ChatGPT使用情况，因变量则为导航表现得分（包含迷失方向感、满意度与认知负荷）与测试得分。研究数据取自471名修读网页设计与编程专业的全程远程教育学生。导航表现采用Firat与Kabakci Yurdakul于2016年开发的Hyper-MNP量表进行测量。该量表共计15个条目，其中正向表述条目7项、负向表述条目8项，包含三因子结构：迷失方向感（6个条目）、满意度（5个条目）与认知负荷（4个条目），所有条目均采用5点李克特（Likert）量表计分。韦尔奇t检验（Welch's t-test）结果显示，在所有因变量维度上，ChatGPT组均表现出显著优势。