## 7 Conclusions
While our study is exploratory in nature and primarily focuses on utilizing ET data to enhance the validity of inferences drawn from test scores, it is essential to situate our findings within the broader context of existing research on DC assessment and the use of process data. Previous studies, such as those by [PERSON] et al. and [PERSON] et al. [34], have highlighted significant gaps in the validity and reliability of DC assessment tools, particularly those relying heavily on self-reported measures. Our findings extend this body of work by demonstrating that ET data can provide detailed insights into the RPs of examinees, thereby addressing some of these validity concerns. Furthermore, our results align with the conclusions of [PERSON] and [PERSON], who emphasized the need for more granular analyses of assessment and learning design strategies [106]. By integrating ET data, we contribute to a more nuanced understanding of how examinees interact with assessment items, corroborating the findings of [PERSON] et al. on the benefits of TEA environments [12]. Additionally, our study contrasts with the work of [PERSON] and [PERSON], who primarily focused on log data and response times [15]; we show that ET data can uncover variations in response strategies that are not captured by these traditional metrics. Overall, our research underscores the potential use of ET as a complementary tool in DC assessment, providing richer and more actionable data compared to conventional methods, and paving the way for future studies to further explore this promising avenue. To effectively integrate and analyze ET data with other process data such as log data and RTs, it would be advisable to use a multi-modal data fusion framework that leverages machine learning algorithms and statistical models. The integration process would begin with data synchronization, where timestamps from different data sources should be aligned to ensure temporal consistency. ET data would be processed alongside log data capturing user interactions and RTs that indicate cognitive load and decision-making speed. For the analysis, certain approaches could be employed, e.g., a combination of Dynamic Time Warping (DTW) for aligning temporal sequences and Principal Component Analysis (PCA) to reduce dimensionality, while preserving key patterns in the data. To explore relationships and predictive capabilities, machine learning models could be utilized, such as Random Forests and Support Vector Machines (SVM) for classification and regression tasks, respectively. Additionally, Hidden Markov Models (HMM) could be used to identify latent states in user behavior. The integration of these data streams could help to provide a comprehensive view of user behavior, improving the understanding of cognitive processes and enhancing predictive analytics in human-computer interaction studies. Our study's findings can be further situated within established theoretical frameworks of cognitive processing and test validity. According to theories of cognitive processing, such as those posited by [PERSON] and [PERSON] and [PERSON] [31; 34], ET data provide a robust method to infer cognitive strategies and processes involved in test-taking. These theories suggest that RPs are crucial for validating the cognitive constructs that assessments aim to measure. The alignment of fixation patterns and scan paths with the hypothesized cognitive processes supports the validity of the test items and the inferences drawn from test scores. Additionally, the use of ET in our study aligns with [PERSON]'s validation theory [30], which emphasizes the necessity of rigorous validation methods to substantiate the intended interpretations of test scores. By examining the detailed eye movement data, we have demonstrated that participants engage with test items in ways that reveal underlying cognitive strategies, thus reinforcing the construct validity of our DC assessment.