Psych Central News
Student Engagement Includes Attention to Feelings & Thoughts
New research by the University of Pittsburgh gives educators new tools to recognize that engagement means more than showing up and listening in class.
The issue is important as “enhancing student engagement has been identified as the key to addressing problems of low achievement, high levels of student misbehavior, alienation, and high dropout rates, ” said professor Ming-Te Wang, Ph.D.
Although attending class is first, true engagement in the material includes a student’s emotional and cognitive involvement with the course material.
In the study, published online in the journal Learning and Instruction, researchers suggest student engagement is malleable, and can be improved by promoting a positive school environment.
“When we talk about student engagement, we tend to talk only about student behavior,” Wang added. “But my coauthor and I feel like that doesn’t tell us the whole story. Emotion and cognition are also very important.”
In the past, only behavioral measures of student engagement — such as class attendance, turning in homework on time, and classroom participation — had been evaluated when gauging student engagement.
By conducting a study linking students’ perceptions of the school environment with behavior, the authors intended to show the viability of a multidimensional perspective.
For the research a 100-question survey was developed to evaluate emotional and cognitive engagement. Sample survey questions that tested emotional engagement in classes across all subject areas asked students to agree or disagree with statements such as “I find schoolwork interesting” and “I feel excited by the work in school.”
Sample questions concerning cognitive engagement asked students to provide ratings to questions like “How often do you make academic plans for solving problems?” and “How often do you try to relate what you are studying to other things you know about?”
Using the survey, researchers conducted a two-year longitudinal study, tracking approximately 1,200 Maryland students from seventh through eighth grade.
The authors measured students’ perceptions of their environment by asking questions on five areas.
Topics included the clarity of teacher expectations; students’ opportunities to make learning-related decisions; if subject matter was relevant to students’ personal interests and goals; students’ perceptions of the emotional support offered by teachers; and students’ perceptions of how positive their relationships were with fellow students.
Researcher discovered that students who felt that the subject matter being taught and the activities provided by their teachers were meaningful and related to their goals were more emotionally and cognitively engaged than were their peers.
Also among the paper’s main findings is that the school environment can and, indeed, should be changed if it is impeding student engagement.
A positive and supportive school environment is marked, Wang said, by “positive relationships with teachers and peers. Schools must provide opportunities for students to make their own choices. But they also must create a more structured environment so students know what to do, what to expect, from school.”
Wang also noted, however, that there is no “one size fits all” strategy to the problem of student engagement.
“Usually people say, ‘Yes, autonomy is beneficial. We want to provide students with choices in school,’” Wang said. “This is the case for high achievers, but not low achievers. Low achievers want more structure, more guidelines.”
As a result, Wang said, teachers must take into account individual variation among students in order to fulfill the needs of each student.
Source: University of Pittsburgh
Science Fiction? Brain Imaging IDs Particular Emotions
Carnegie Mellon University combined functional magnetic resonance imaging (fMRI) and machine learning to measure brain signals to read emotions in individuals. The findings illustrate how the brain categorizes feelings, giving researchers the first reliable process to analyze emotions.
Until now, research on emotions has been long stymied by the lack of reliable methods to evaluate them, mostly because people are often reluctant to honestly report their feelings. Further complicating matters is that many emotional responses may not be consciously experienced.
Identifying emotions based on neural activity builds on previous studies which used similar techniques to create a computational model that identifies individuals’ thoughts of concrete objects — often dubbed “mind reading.”
“This research introduces a new method with potential to identify emotions without relying on people’s ability to self-report,” said Karim Kassam, Ph.D., lead author of the study.
“It could be used to assess an individual’s emotional response to almost any kind of stimulus, for example, a flag, a brand name or a political candidate.”
One challenge for the research team was find a way to repeatedly and reliably evoke different emotional states from the participants. Traditional approaches, such as showing subjects emotion-inducing film clips, would likely have been unsuccessful because the impact of film clips diminishes with repeated display.
The researchers solved the problem by recruiting actors from CMU’s School of Drama.
“Our big breakthrough was my colleague Karim Kassam’s idea of testing actors, who are experienced at cycling through emotional states,” said researcher George Loewenstein, Ph.D., professor of economics and psychology. “We were fortunate, in that respect, that CMU has a superb drama school.”
For the study, 10 actors were scanned at CMU’s Scientific Imaging & Brain Research Center while viewing the words of nine emotions: anger, disgust, envy, fear, happiness, lust, pride, sadness and shame.
While inside the fMRI scanner, the actors were instructed to enter each of these emotional states multiple times, in random order.
The computer model was able to correctly identify the emotional content of photos being viewed using the brain activity of the viewers.
To identify emotions within the brain, the researchers first used the participants’ neural activation patterns in early scans to identify the emotions experienced by the same participants in later scans.
The computer model achieved a rank accuracy of 0.84. Rank accuracy refers to the percentile rank of the correct emotion in an ordered list of the computer model guesses; random guessing would result in a rank accuracy of 0.50.
Next, the team took the machine learning analysis of the self-induced emotions to guess which emotion the subjects were experiencing when they were exposed to the disgusting photographs.
The computer model achieved a rank accuracy of 0.91. With nine emotions to choose from, the model listed disgust as the most likely emotion 60 percent of the time and as one of its top two guesses 80 percent of the time.
Finally, they applied machine learning analysis of neural activation patterns from all but one of the participants to predict the emotions experienced by the hold-out participant.
This answers an important question: If we took a new individual, put them in the scanner and exposed them to an emotional stimulus, how accurately could we identify their emotional reaction? Here, the model achieved a rank accuracy of 0.71, once again well above the chance guessing level of 0.50.
“Despite manifest differences between people’s psychology, different people tend to neurally encode emotions in remarkably similar ways,” noted Amanda Markey, a graduate student in the Department of Social and Decision Sciences.
A surprising finding from the research was that almost equivalent accuracy levels could be achieved even when the computer model made use of activation patterns in only one of a number of different subsections of the human brain.
“This suggests that emotion signatures aren’t limited to specific brain regions, such as the amygdala, but produce characteristic patterns throughout a number of brain regions,” said Vladimir Cherkassky,Ph.D., senior research programmer in the Psychology Department.
The research team also found that while on average the model ranked the correct emotion highest among its guesses, it was best at identifying happiness and least accurate in identifying envy.
It rarely confused positive and negative emotions, suggesting that these have distinct neural signatures. And, it was least likely to misidentify lust as any other emotion, suggesting that lust produces a pattern of neural activity that is distinct from all other emotional experiences.
According to researcher Marcel Just, Ph.D., “We found that three main organizing factors underpinned the emotion neural signatures, namely the positive or negative valence of the emotion, its intensity — mild or strong, and its sociality — involvement or non-involvement of another person.
“This is how emotions are organized in the brain.”
Source: Carnegie Mellon University
Abstract of human brain photo by shutterstock.
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