  Considering learner engagement has a mutual benefit for both learners and
instructors. Instructors can help learners increase their attention,
involvement, motivation, and interest. On the other hand, instructors can
improve their instructional performance by evaluating the cumulative results of
all learners and upgrading their training programs. This paper proposes a
general, lightweight model for selecting and processing features to detect
learners' engagement levels while preserving the sequential temporal
relationship over time. During training and testing, we analyzed the videos
from the publicly available DAiSEE dataset to capture the dynamic essence of
learner engagement. We have also proposed an adaptation policy to find new
labels that utilize the affective states of this dataset related to education,
thereby improving the models' judgment. The suggested model achieves an
accuracy of 68.57\% in a specific implementation and outperforms the studied
state-of-the-art models detecting learners' engagement levels.
