The concept of student engagement has gained popularity in the last years through the progressive understanding of the importance of considering emotional, intellectual, and behavioral factors in the learning process. In addition, various studies pointed out the connection between cognitive learning factors and non-cognitive factors like motivation, curiosity, interest, responsibility, determination, attitude, perseverance, or work habits while achieving improved academic performance, information recall, and exam scores. Therefore, student engagement is an essential process in a student's learning experience since the learning process becomes effective in the presence of collaboration and involvement. It particularly gains shape when educators bring to the discussion and add priorities to their educational strategies or teaching methods that address the behavioral, emotional, and cognitive sides that influence positively or negatively the learning process. However, since most studies mainly evaluate student engagement in onsite, classical learning environments, there needs to be more known about the impact of student engagement in online learning or about effectively measuring it via non-traditional methods. Therefore, the methods proposed in this paper follow the triangle relations between student engagement, their end performance, and their overall self-motivation by identifying the main impact factors in student engagement, studying the impact of engagement on performance, and exploring the role of self-motivation on both engagement and performance. This study collected data from a group of undergraduate students in the Computer Science and Information Technology domain in 2022-2023 during four Programming Engineering onsite and online laboratory work activities in a hybrid e-learning environment via online questionnaires. The laboratory work schedule was available at the start of each week in the academic accounts calendar, and the corresponding virtual whiteboard was attached to each meeting, being open to the students whenever they needed it. Each laboratory session was decomposed according to the micro-learning paradigm into signaled and scored independent units that build up to the final goal. For each learning session of two hours, a milestone of achieving 5/10 score points was imposed to be granted the opportunity to solve the whole task and tackle supplementary bonus points applications until the end of the week. The purpose of this laboratory work planning targeted to increase student engagement, create connections, and create meaningful work while giving the students autonomy to test their self-efficacy and competency. Learning hooks used as markers for each unit were pointed out via word clouds in Mentimeter interactions after each learning session. In addition, Electroencephalogram signals automatically allow the assessment of cognitive load levels based on memory recall, specific tasks, or memory processing tasks via machine learning techniques.

student engagement; e-learning; virtual learning environment; micro-learning; cognitive assessment; emotion assessment;