## 1 Introduction
TEA offers interactive and adaptive testing environments that better simulate real-world digital tasks, providing a more accurate measure of complex problem-solving and higher-order thinking skills [3; 4; 11]. It also enables the assessment of the skills dimension of DC, including high-level cognitive skills, through innovative item formats [12]. However, technical limitations, such as the digital divide and varying familiarity with platforms, may introduce biases that affect the validity of results [13]. Additionally, the design of assessment items is crucial, as performance outcomes are strongly influenced by item format [14; 15]. Poorly designed items can misrepresent examinees' DC levels, especially when assessing complex cognitive skills. While innovative approaches like performance-based tasks (i.e., assessments where individuals demonstrate their skills or knowledge by completing a specific, real-world task rather than answering questions or selecting answers) offer a more authentic measure of DC [16; 17], traditional self-report tools often lead to overestimations due to social desirability bias and the Dunning-Kruger effect [4]. Despite its potential, the high development costs and technical complexity of TEA limit its broader adoption and scalability [18]. Moreover, it is challenging to infer the response processes (RPs) examinees use to complete tasks, making it difficult to confirm if they approached problems as intended or employed alternative strategies. Thus, while TEA represents progress, its effectiveness depends on addressing these limitations and ensuring fair access and user competence. Another key approach involves utilizing TEA to collect detailed data on examinee behavior and performance, such as log data, response time (RT), and click streams [19; 20]. Although some researchers have used these data to enhance score interpretation and validate assessments [21], such studies are rare, particularly in the assessment of DC. Analyzing RP data from assessments can help validate item design by showing that tasks elicit the intended knowledge and skills. However, evaluating whether examinees engage with relevant RPs is often overlooked when considering test validity, and evidence of this is lacking in most DC evaluation tools [4; 22]. The integration of ET data offers new opportunities to analyze cognitive processes during assessments [23]. ET data provide fine-grained insights into examinee behavior at the item level, revealing attention patterns, decision-making processes, and problem-solving strategies [21]. For example, in image-based questions, gaze data can identify the areas participants focus on most, offering a unique view into their problem-solving strategies and pinpointing differences in performance. Such data can help validate DC assessments by providing direct evidence of cognitive engagement and resource allocation [24]. However, the implementation of ET data presents challenges, such as the need for sophisticated technology, complex data interpretation, and potential intrusiveness, which could affect performance [25]. Additionally, the large-scale use of ET remains costly and logistically difficult, making it hard to scale [26]. While innovative item designs and ET data have the potential to improve the accuracy of DC assessments, practical and financial barriers must be overcome to reach widespread application. In short, it is essential to design items that prompt more complex RPs from test-takers, while ensuring that these items trigger the intended knowledge and skills. Relying solely on RT data and item scores is insufficient. This study aims to use gaze data as additional evidence to support RP analysis and enhance the validity of the assessments. This paper builds on our previous research by incorporating gaze data into DC assessment to validate item design and differentiate participants based on their DC levels [27]. We focused on analyzing variations among participants with different DC levels using fixation-based metrics supported by visualization techniques.