Student Engagement
Student Engagement - Related Videos
06:37
Artificial Intelligence-Based System for Detecting Attention Levels in Students
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Cited by 6 •
2023
This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more engaging.
14:02
Measuring Engagement of Spectators of Social Digital Games
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2021
We propose a methodology that enables measuring engagement of spectators in a social digital game combining physiological and self-reported data. As this digital game involves a group of freely moving people, the experience is filmed using a synchronizing technique that links physiological data with events in the game.
01:31
Student t Distribution
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2025
The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval. The Student t distribution was developed by William S. Goset (1876–1937) of the...
01:25
Microsoft Excel: Student's t-Test
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2025
Student's t-test in Microsoft Excel is a statistical method used to compare the means of two groups to determine if they are significantly different from each other. It's commonly used to evaluate hypotheses, such as testing whether a treatment has an effect compared to a control group. Excel provides built-in functions to perform t-tests, making it accessible for users needing to conduct basic statistical analysis. To conduct a t-test in Excel, use the T.TEST function or the "Data Analysis...
13:57
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
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Cited by 43 •
2015
This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners’ engagement during a task.
FAQs
Engagement grows through interaction that asks students to attend, collaborate, solve problems, and reflect on their decisions. Instructors provide direction, peers create opportunities to exchange ideas, and structured activities turn learners from passive recipients into active participants. Together, these elements support deeper thinking and can strengthen persistence during challenging engineering work.
Authentic problems connect theoretical principles with practical decisions, giving students a clear reason to apply what they are learning. When learners must consider a problem, choose an approach, and examine the consequences, coursework becomes more relevant and intellectually demanding. This connection can encourage attention, problem solving, reflection, and sustained participation.
A useful view considers participation, motivation, and learning behaviors together. Participation indicates whether students take part in activities, motivation reflects their willingness to invest effort, and learning behaviors show how they approach study, collaboration, problem solving, and reflection. Examining these dimensions together gives educators a fuller basis for improving instruction and supporting persistence.
Instructors can incorporate design projects, laboratory investigations, team-based challenges, and authentic industry problems into instruction. These formats should require attention, collaboration, problem solving, and reflection rather than passive information intake. Linking each activity to theoretical principles and practical decisions helps students recognize the purpose of their work and participate more actively.
Design projects allow students to connect theory with decisions, while laboratory investigations place learning in an investigative setting. Team-based challenges add collaboration, and authentic industry problems provide practical relevance. Used together, these formats address different aspects of learning and help develop communication, creativity, systems thinking, and persistence relevant to engineering practice.
Educators can examine students’ participation, motivation, and learning behaviors to identify how learners respond to instruction and activities. That evidence can guide refinements to course design, collaboration, problem-solving opportunities, and reflective work. It also helps educators support diverse learners more deliberately while preparing students for the communication, creativity, and systems thinking expected in professional engineering practice.