What Makes Self-Directed Learning Effective?

In recent years, educators have come to focus more and more on the importance of lab-based experimentation, hands-on participation, student-led inquiry, and the use of “manipulables” in the classroom. The underlying rationale seems to be that students are better able to learn when they can control the flow of their experience, or when their learning is “self-directed.”

While the benefits of self-directed learning are widely acknowledged, the reasons why a sense of control leads to better acquisition of material are poorly understood.

Some researchers have highlighted the motivational component of self-directed learning, arguing that this kind of learning is effective because it makes students more willing and more motivated to learn. But few researchers have examined how self-directed learning might influence cognitive processes, such as those involved in attention and memory.

In an article published in Perspectives on Psychological Science, a journal of the Association for Psychological Science, researchers Todd Gureckis and Douglas Markant of New York University address this gap in understanding by examining the issue of self-directed learning from a cognitive and a computational perspective.

According to Gureckis and Markant, research from cognition offers several explanations that help to account for the advantages of self-directed learning. For example, self-directed learning helps us optimize our educational experience, allowing us to focus effort on useful information that we don’t already possess and exposing us to information that we don’t have access to through passive observation. The active nature of self-directed learning also helps us in encoding information and retaining it over time.

But we’re not always optimal self-directed learners. The many cognitive biases and heuristics that we rely on to help us make decisions can also influence what information we pay attention to and, ultimately, learn.

Gureckis and Markant note that computational models commonly used in machine learning research can provide a framework for studying how people evaluate different sources of information and decide about the information they seek out and attend to. Work in machine learning can also help identify the benefits – and weaknesses – of independent exploration and the situations in which such exploration will confer the greatest benefit for learners.

Drawing together research from cognitive and computational perspectives will provide researchers with a better understanding of the processes that underlie self-directed learning and can help bridge the gap between basic cognitive research and applied educational research. Gureckis and Markant hope that this integration will help researchers to develop assistive training methods that can be used to tailor learning experiences that account for the specific demands of the situation and characteristics of the individual learner.

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Factors to Help Parents and Professionals Recognize Teens in Distress

Suicide is the third-leading cause of death for teens, according to the Centers for Disease Control and Prevention. Now, a University of Missouri public health expert has identified factors that will help parents, medical professionals and educators recognize teens at risk for self injury and suicide.

“For many young people, suicide represents an escape from unbearable situations—problems that seem impossible to solve or negative emotions that feel overwhelming,” said Lindsay Taliaferro, an assistant professor of health sciences at MU. “Adults can help these teens dissect their problems, help them develop healthful coping strategies, and facilitate access to mental health care so their problems don’t seem insurmountable.”

Taliaferro analyzed data from the 2007 Minnesota Student Survey to pinpoint factors associated with self injury. More than 60,000 Minnesota high school students completed the survey that assessed their health behaviors. Of those who completed the survey, more than 4,000 teens (roughly the same size as the student bodies at two large high schools) reported injuring themselves in the past year. Nearly half of those who reported self injury also had attempted suicide.

“Of the teens who engaged in non-suicidal self injury, hopelessness was a prominent factor that differentiated those who attempted suicide from those who did not have a history of suicide attempts,” Taliaferro said.

Parents, teachers and medical professionals sometimes avoid talking to teens about self harm because they aren’t sure how to help, Taliaferro said.

“Adults don’t need to solve all the teens’ problems, but they should let the teens know they have safe persons they can talk to,” Taliaferro said. “Sometimes just talking about their feelings allows young people to articulate what they’re going through and to feel understood, which can provide comfort.”

Taliaferro recommends that parents strengthen connections with their teens and help foster connections between their children and other positive adult influences.

“One of the most important protective factors against teens engaging in self injury was parent connectedness, and, for females, connections with other prosocial adults also were associated with reduced likelihood of engaging in self injury,” Taliaferro said. “Parents are extremely valuable influences in their children’s lives.”

Although parents play influential roles in teens’ lives, Taliaferro said mental health professionals are the best resources for troubled teens. Medical professionals, such as primary care physicians, can also serve crucial roles by identifying teens who self injure and referring them to community support systems and mental health specialists before their behaviors escalate, Taliaferro said.

The Department of Health Sciences is part of the MU School of Health Professions. Taliaferro’s study, “Factors Distinguishing Youth Who Report Self-Injurious Behavior: A Population-Based Sample,” was published in Academic Pediatrics. She collaborated with researchers at the University of Wisconsin-Eau Claire, the University of Minnesota and the Pennsylvania State University.

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New definition of autism in DSM-5 will not exclude most children with autism

Parents should not worry that proposed changes to the medical criteria redefining a diagnosis of autism will leave their children excluded and deemed ineligible for psychiatric and medical care, says a team of researchers led by psychologists at Weill Cornell Medical College.

Their new study, published in the October 1 issue of the American Journal of Psychiatry, is the largest to date that has tried to unpack the differences between the diagnostic criteria for autism spectrum disorders in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the proposed revision in the fifth edition (DSM-5), which is expected to be published in May 2013. These manuals provide diagnostic criteria for people seeking mental-health-related medical services.

"I know that parents worry, but I don't believe there is any substantial reason to fear that children who need to be diagnosed with autism spectrum disorders, and provided with vital services, will not be included in the new criteria in this updated manual," says the study's senior investigator, Dr. Catherine Lord, director of the Center for Autism and the Developing Brain at NewYork-Presbyterian Hospital's Westchester campus, along with its affiliated medical schools Weill Cornell Medical College and Columbia University College of Physicians and Surgeons.

At issue is whether DSM-5 will "capture" the same individuals diagnosed with different forms of autism by the DSM-IV. The DSM-5 proposal redefines autism as a single category -- autism spectrum disorder (ASD) -- whereas DSM-IV had multiple categories and included Autistic Disorder, Asperger's Disorder, and Pervasive Developmental Disorder, Not Otherwise Specified (PDD-NOS).

Critics have particularly worried that among the excluded will be children now diagnosed with PPD-NOS and Asperger's disorder. That isn't the case, says Dr. Lord, who is also a DeWitt Wallace Senior Scholar at Weill Cornell and an attending psychologist at NewYork-Presbyterian Hospital. The study, the largest to date and arguably, the most rigorous, finds that when relying on parent report, 91 percent of the 4,453 children in the sample currently diagnosed with a DSM-IV autism spectrum disorder would be diagnosed with ASD using DSM-V.

Many of the remaining nine percent would likely be reincluded once a clinician can offer input, says Dr. Lord, who is also a member of the American Psychiatric Association's DSM-5 Neurodevelopmental Disorders Work Group.

The study researchers also concluded that DSM-5 has higher specificity than DSM-IV--in their study, DSM-5 criteria resulted in fewer misclassifications.

Improving the Diagnostic Criteria

The study used three large databases to evaluate DSM-5 criteria in groups of children with DSM-IV clinical diagnoses. The analysis, which included a team of independent reviewers led by the study's lead author, Dr. Marisela Huerta of NewYork-Presbyterian/Weill Cornell Medical Center, relied on a standardized 96-item parent report and a clinician-based measure of autism spectrum disorder impairments.

"These two instruments were particularly well-suited for the current study because they include items based on history and current behavior, and they take into account developmental level in their design," says Dr. Huerta, an instructor of psychology at Weill Cornell and a professional associate at NewYork-Presbyterian Hospital. "This is consistent with DSM-5 criteria, which operationalize symptoms differently for individuals of different ages in order to account for the effect of development on ASD symptoms."

The changes proposed by DSM-5 are designed to better identify autism spectrum disorders and distinguish them from other conditions. According to Dr. Huerta, "The criteria for DSM-5 are actually more inclusive." For example, while DSM-IV criteria require evidence of difficulties related to autism prior to age 3, "DSM-5 says that a child has to show examples of unusual behavior in early childhood, with the idea that there is nothing sacrosanct about your third birthday."

Other changes proposed by DSM-5 include defining autism spectrum disorders by two sets of core features -- impaired social communication and social interactions, and restricted and repetitive behavior and interests. DSM-5 reorganizes the symptoms in these domains and includes those not previously included in DSM-IV, such as sensory interests and aversions.

The overall issue with DSM-IV was "not that a lot of people are diagnosed with autism who shouldn't be, but that there is a lot of confusion because the criteria were not very accurate," says Dr. Lord.

"DSM-5 deliberately added and organized things to try to bring in and better address the needs of people with autism spectrum disorders of all developmental levels and ages -- including girls, who were not represented as well as they should be in DSM-IV," Dr. Lord says. "The goal of DSM-5 is to better describe who has ASD in a way that matches up with what we know from research, which predicts who has the disorder and also reflects what clinicians are actually looking at."

Because of the newness of the proposed criteria, only a few studies have attempted to compare the criteria between the two DSM versions. "Our study is much broader, and it is important to note that we get very similar results when looking at three large data sets that were collected for different purposes, with diverse populations, and for various reasons," says Dr. Lord.

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