Recent advances in eye-tracking technology afford the possibility to collect rich data on attentional focus in a wide variety of settings outside the lab. However, apart from anecdotal reports, it is not clear how to maximize the validity of these data and prevent data loss from tracking failures. Particularly helpful in developing these techniques would be to describe the prevalence and causes of tracking failures in authentic environments. To meet this goal, we analyzed video records aligned with eye-tracking data collected from screen-mounted eye trackers employed in a middle-school classroom. Our sample includes records from 35 students recorded during multiple eye-tracking sessions. We compared student head position, body posture, attentiveness, and social interactions for time periods associated with successful and unsuccessful eye tracking. Overall, we observed substantial data loss and found that student inattentiveness, movements toward the eye tracker, and head rotations were the most prevalent factors inducing data loss. In addition, we observed a substantial relationship between data loss and apparent low involvement in the learning task. These data suggest that eye-tracking data loss is an important problem and that it can present a threat to validity because it can bias datasets to overrepresent high involvement behaviors. Based on these findings, we present several recommendations for increasing the proportion of usable data and to counter possible biases that data loss may introduce.