Maintaining students' cognitive engagement in educational settings is crucial to their performance, though quantifying this mental state in real-time for distance learners has not been studied extensively in natural distance learning environments. We record electroencephalographic (EEG) data of students watching online lecture videos and use it to predict engagement rated by human annotators. An evaluation of prior EEG-based engagement metrics that utilize power spectral density (PSD) features is presented. We examine the predictive power of various supervised machine learning approaches with both subject-independent and individualized models when using simple PSD feature functions. Our results show that engagement metrics with few power band variables, including those proposed in prior research, do not produce predictions consistent with human observations. We quantify the performance disparity between cross-subject and per-subject models and demonstrate that individual differences in EEG patterns necessitate a more complex metric for educational engagement assessment in natural distance learning environments.