Over time, there has been a change in how students acquire and exchange information, with laptops and smartphones becoming increasingly important. The use of technology has evolved from being restrained du to the classroom to being crucial due to the COVID-19 pandemic. As education shifts towards hybrid models, students are now expected to learn at home, which can be challenging as excessive technology usage and a lack of self-discipline can lead to more distractions. This paper examines the effects of the influence of these distractions with the help of two concepts similar to assignments in students' lives: text comprehension & memorization, as well as example-based learning, in which the function of an apparatus was to be tested and described. The results show that distraction does not affect text comprehension but decreases information retention. Additionally, participants required more trials and repetitions to understand schemes in example-based learning when distracted.

Designing user-adaptive search systems necessitates modeling the user's knowledge state during information seeking. Gaze data offers insights into cognitive processes during task-based reading. Despite its potential, cognitive perspectives have been insufficiently explored in the representation of the user's knowledge state when designing search systems. We reanalyzed an eye-tracking dataset and constructed mixed-effects user models to identify which measurements of gaze activities (i.e., gaze metrics captured by eye trackers) are reflective of the user. Our study's findings indicate that there are statistically significant correlations between gaze metrics that measure the variability of saccadic eye movement and search performance. The accuracy of answers has been significantly influenced by the interaction between the control of saccade trajectories, measured by the standard deviation of absolute saccadic directions and the difficulty of the search task. We discuss the implications of these findings for the design of search systems adaptable to the user's state of knowledge.