Curiosity is innate, not learned. Our natural curiosity fuels both learning and connection.
Structured systems can suppress curiosity.
In adulthood, our lost curiosity results in shallower relationships and less collaborative workplaces.
Reigniting curiosity rebuilds trust and connection.
Simon was recently walking through the park with his three-year-old daughter. Autumn had truly arrived, and brown leaves lay scattered across the ground beneath the bare trees. Simon’s daughter saw a small boy playing among the leaves and ran over to see what he was doing. The two quickly formed an unspoken bond as they joined forces, collecting the discarded leaves into piles.
If you have children, you are almost certainly familiar with this scene, or one like it. Children naturally want to understand what’s happening around them, and that curiosity helps them to connect with anyone, or anything, that intrigues them. When there’s something new and exciting to discover, social anxiety is easily forgotten. Connections are easily forged.
How often do you see adults engaging in the same way? We certainly find it easier to bond over something we know we have in common: Witness strangers hugging when their team has scored or singing together at their favourite band’s concert. But beyond those specific settings, we tend to be more reserved when meeting new people, very conscious of the proper social etiquette and careful not to cross boundaries.
What became of the natural curiosity we had when we were young? To that bridge between discovery and connection that opened new worlds and friendships?
Born Curious
Lydia Redman, an early years expert working for the Royal Borough of Greenwich in the UK, regards curiosity as a fundamental trait of young children. “We are born curious,” she said. “Curiosity is naturally innate in young children. I’ve never met a child whose curiosity wasn’t evident. Their brain makes thousands of connections every day.”
The childlike sense of wonder is fundamental to the qualities of effective learners that early childhood educators, like Redman, strive to nurture. The Tickell Review, reporting on the foundational impact of the early years, explains how children learn and outlines three key characteristics:
Redman sees curiosity as “the root of all of those skills that we want children to develop.” The view aligns with research by Jonathan Haidt highlighting the importance of unstructured play in allowing children to freely explore and cultivate their natural curiosity.
Curiosity helps young people develop critical minds and build connections. It’s both a learning tool and a social link.article continues after advertisement
And it is just as important to us in adulthood as it is to children.
Where Does Our Curiosity Go?
In his book The Anxious Generation, Haidt contends that a decline in unstructured play, which began in the 1990s, has contributed to various developmental issues, including an inhibited capacity for curiosity, creativity, and problem-solving. Redman has also observed that as education becomes more formalised, curiosity is impacted.
“We begin to see less emphasis and less allowance for curiosity as people move through later childhood and into their teens. The British education system begins formal learning for children at a very early age. We place a great emphasis on knowledge and output, which can often lead to excessive cramming. It’s not helping our children to be curious and become problem solvers.”
A focus on regurgitating knowledge rather than solving problems, and the subsequent suppression of curiosity, impacts us as we transition from childhood to our teenage years and then into adulthood and the workplace.
We move from playground to classroom, then to lecture hall, and finally to meeting room. At each stage, we feel the weight of structure, hierarchy, and an increasing fear of giving the wrong response.
Our natural impulse to explore and challenge gets suppressed and replaced by a desire to conform and give the expected response.
Curiosity in the Workplace
The inhibition of curiosity impacts the workplace and our relationships. The pressure of deadlines and expectations adds to the suppression of our desire to explore. We concentrate on the agenda and the task in front of us, rather than seeking to learn and develop. Narrow conversations mean we miss the chance to get to know our colleagues better.
Of course, some workplaces promote more curiosity than others. People in the engineering and creative sectors still need to maintain a sense of play to succeed in their roles. However, that doesn’t mean their curiosity influences every aspect of their job.
As leaders, we must take the initiative and rekindle a sense of curiosity among those who work for and with us. Simple steps, such as encouraging more conversations and connections without an agenda, can have a significant impact. Allow time in meetings for small talk and catch-ups, and organise meetings in which you explore what could have been done differently on a recent project to improve outcomes—without assigning blame or finger-pointing.
Curiosity isn’t about knowing the answer; it’s about genuinely listening and seeking understanding. Leaders need to foster a culture in which there are no definitive or expected answers but in which exploration and challenge are embraced. Conversations should include active listening and authentic engagement, as modelled in The Curiosity Cycle..
The Foundation of Connection
Children who start playing together out of shared curiosity and play are not intentionally building relationships; they are discovering them naturally. As adults, many of us have lost that sense of magic and spontaneity. If we can reignite the spirit of exploration, we too can connect more deeply and form stronger relationships and more meaningful bonds, reliving the magic we experienced as children.
References
Dame Clare Tickell. (2011). The Early Years: Foundations for Life, Health and Learning.
Lukianoff, G., & Haidt, J. (2018). The Coddling of the American Mind: How good intentions and bad ideas are setting up a generation for failure. Penguin Books.
Haidt, J. (2024). The Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness. New York, NY: Penguin Press.
About the Author
A specialist in professional relationships and networking for over 25 years, Andy Lopata is an experienced international speaker, podcast host, and the author of six books on the topic.
Have you ever struggled with confidence? If so, have you ever had an experience that helped you learn to believe in yourself more?
Maybe it was learning or getting better at a skill, like photography or soccer. Maybe it was taking on a big responsibility, as in working a job or caring for your family pet. Or perhaps it was succeeding at something that once scared you, such as speaking in front of a crowd or making new friends.
What did you learn from this experience? How did it change you?
Ask Claudia Acuña about Louis Harris and the East Coast chapter of the Black Surfing Association, and she’ll tell you they “saved my kid’s life.”
For the last five years, Ms. Acuña and her son, Daniel Kelley, 14, have been part of a community that Mr. Harris built in Rockaway Beach, Queens.
Mr. Harris, 53, founded the chapter in 2016. The nonprofit, which is funded through donations and corporate sponsorships, offers free surfing lessons on summer weekend mornings and equipment to any child who shows up.
Parents whose children Mr. Harris has taught say his efforts have transformed their children’s lives.
Among them is Daniel. His mother, Ms. Acuña, said her son was depressed in the summer of 2020, dealing with the pandemic and bullying, when Mr. Harris’s surf school provided a lifeline.
The program “completely changed everything,” she said, adding that Daniel blossomed and his confidence soared. “As a mother,” she said, “it’s the most beautiful gift that any mother can have.”
Many children of color are not accustomed to seeing “surfers that look like them, that look like Mr. Lou,” Ms. Acuña said, referring to Mr. Harris, who is Black.
Mr. Harris, she added, had set an example as a positive male role model, noting that he had taught her son about masculinity “with the tenderness and the softness of the water.”
The article continues:
Suzanne Cope said the surf school has taught her son, Rocco, 12, “to fail over and over again” while still wanting “to go back and do the same thing to try and get better.” There are few opportunities for children “to really learn that kind of grit,” she said.
Students, read the entire article and then tell us:
Have you ever had an experience that helped improve your confidence, as learning to surf did for these children? If so, what was it, and how did it change you? If not, does the article inspire you to try something new?
How important are role models like Mr. Harris, the surf coach? Have you ever met someone — or looked up to someone from afar — who helped you believe in yourself? What did you learn from that person?
One mother said that surfing had taught her son “to fail over and over again” while still wanting “to go back and do the same thing to try and get better.” What is the value in learning to fail and then get back up and try again? Have you ever had an experience like that?
How confident of a person are you? If you’re working on building up your confidence, what is one activity or experience you might like to try to help support your self-image? How do you think it would help?
What good things — no matter how small — have happened in your life recently?
Did you turn in a school project you’ve been working on for weeks? Try out a different hairstyle and get compliments? Discover a new favorite snack? Score your first touchdown?
Do you take the time to acknowledge these little milestones and accomplishments enough in your life?
In “Little Victories,” a July edition of The Morning newsletter, Melissa Kirsch argues we should all be celebrating our tiny achievements more often:
This week, I went to a party thrown by a New York City deli to celebrate a specific varietal of herring. I was keen to attend because the concept of a herring party seemed delightful — an occasion for revelry that I’d never considered. I was intrigued to learn that in the Netherlands, this particular herring is traditionally fished for only a few months, when the herring’s body fat reaches at least 16 percent, for maximum flavor. The Dutch even have an annual festival, Flag Day, to honor the opening of herring season.
I had never celebrated herring before, but, then again, I haven’t celebrated most things. We tend to confine our parties to milestones (birthdays, holidays, housewarmings, weddings) and cultural events (the Oscars, the Super Bowl). Why must it be this way? Sure, if every day is a special occasion, then no day is, but it seems unnecessary to let the calendar totally dictate when we raise a glass or kick up our heels. Also, it’s sort of boring to glorify the same things year after year, when there’s so much else out there that’s worthy.
Once you begin considering all the micro-occasions deserving of a rager or at least an intimate soiree, you realize you’ve been letting so many opportunities for merrymaking just sail right by. A New Haircut Party sounds fun (you tried a new style, you look great) as does a My Back Pain Finally Went Away Party (has there ever been a more profound reason to exult?). New tattoo, old tattoo removal; the puppy spent a full night in the crate; no cavities — let’s rejoice!
Students, read the entire article and then tell us:
What is your reaction to the article? Do you agree with Ms. Hirsch that it’s important to acknowledge even the smallest of good things in our lives? Why, or why not?
What is one recent little victory that you’d like to celebrate? Why do you want to honor this moment or achievement?
How would you want to mark this occasion? Would you throw a party? Have a solo dance session in your living room? Go out for ice cream? Describe your ideal celebration.
Now that you’ve taken stock of at least one of your micro-joys, how do you feel? Is this something you do enough? Would you like to do it more often? How do you think it would affect your life if you did?
About 15 percent of young people in middle and high school (ages 11 to 18) become disconnected—that is, not engaging in school, training, or work—by ages 18 to 24.
Youth who eventually become disconnected are different from their connected peers across several dimensions at baseline: They are more likely to report symptoms of depression, use substances, engage in delinquent activities, and have weaker social support structures.
Suspension in school is a risk factor for later disconnection for males, even after accounting for other observed family and school characteristics.
Early pregnancy (before age 18) is a risk factor for disconnection for females, even after accounting for other observed family and school characteristics.
Young people who are neither in school nor working, often called disconnected youth or opportunity youth, face challenges that can lead to lower lifetime earnings, poorer health, and lower socioeconomic outcomes (Belfield, Levin, and Rosen, 2012; MaCurdy et al., 2006; Hair et al., 2009; Lewis, 2021). Disconnection also generates broader social costs through lost productivity and higher social spending. Effective policy solutions require a clear understanding of the factors that lead some youth to become disconnected.
Existing research points to a variety of potential factors that might influence disconnection in young adulthood, including family environment, mental health, educational attainment, and behavioral factors, among others (Cohen and Wills, 1985; Currie and Thomas, 2001; Furstenberg and Hughes, 1995; Heckman, Stixrud, and Urzua, 2006; Hanushek and Woessmann, 2008). However, data on these measures is limited and researchers are rarely able to follow young people prior to and across spells of disconnection.
In this paper, we use data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine who disconnected youth are and what factors precede disconnection (Harris and Udry, 2022). Add Health is a representative sample of adolescents who were in grades 7 through 12 during the 1994–1995 school year and have been followed through 2018. Our sample of disconnected youth consists of individuals aged 18 to 24 who were not in school and not working at the time of the third wave of the Add Health survey (conducted between 2001 and 2002).[1] Nearly all (99.7 percent) of our respondents were connected at baseline, given that the initial sampling was restricted to students in middle and high school.[2]
Overall, our findings highlight both the complexity of the pathways leading to disconnection and the potential for early targeted interventions to alter these trajectories. This paper will be of interest to researchers, policymakers, and practitioners who are developing programs that reconnect youth with education, training, or employment.
Who Is Disconnected?
We first examine the demographic characteristics of those who are disconnected. Overall, 15 percent of youth in our sample are disconnected. Disconnected youth are more likely to be female and twice as likely as connected youth to be a parent (Table 1).[3] This appears to be driven by women with children: 32 percent of women with children are disconnected compared with 12 percent of women without children. Men with children have slightly higher rates of disconnection than do men without children (18 percent versus 13 percent) but much lower than the rate for women with children.
Disconnected youth have lower levels of education than do connected youth (Table 1): They are three times as likely to not complete high school and 50 percent more likely to have only a high school degree. They are far less likely to have attended college. Similar to other observational studies, we find that disconnected youth are more likely than connected youth to be Black and less likely to be White. Being Hispanic, Native American, Asian or Pacific Islander, or another race or ethnicity does not predict disconnection in this sample. Rates of disconnection are similar among those who were born in the United States and those who were not.[4]
Table 1. Means of Demographic Characteristics, by Disconnected Status
Demographic Characteristic
Connected
Disconnected
Difference
Female
0.49
0.55
0.06*
Parent
0.15
0.31
0.16***
Less than high school education
0.11
0.33
0.21***
Completed high school
0.29
0.46
0.16***
Some college education or more
0.60
0.22
–0.38***
Hispanic
0.11
0.12
0.01
White
0.77
0.63
–0.14***
Black
0.14
0.28
0.14***
Native American
0.04
0.04
0.01
Asian or Pacific Islander
0.04
0.03
–0.01
Other race
0.06
0.06
0.00
Born in the United States
0.93
0.95
0.02
SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year, and have been followed through 2018.
NOTE: We use individual (person) weights to create statistics that are representative of the U.S. population. Asterisks denote statistically significant differences at the 10 percent (*), 5 percent (**), and 1 percent (***) levels, respectively.
What Early-Life Factors Precede Disconnection?
Beyond examining who is disconnected, we also use the panel nature of the data to examine early-life characteristics that precede—and might explain—disconnection.[5] Using the baseline wave that surveyed respondents when they were in school (ages 11 through 18), we construct baseline measures of explanatory variables that are thought to influence disconnection later in life (Table 2).[6]
Table 2. Variable Descriptions
Domain
Variables
Home environment
Parental education and household income
Academic performance and engagement
English language arts (ELA) and math test scores and a binary indicator for suspension at baseline
Mental health
Composite measure of mental health using the Center for Epidemiologic Studies Depression (CES-D) scale
Substance use
Indicators of substance initiation, including alcohol, cigarette, and drug use
Delinquency
Index of delinquency, based on a set of survey measures
Social support
Index of perceived social support from peers, family, and teachers, based on a set of survey measures
SOURCE: Features information from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022).
Home Environment
The data reveal several patterns (Figure 1). First, household and parental factors appear to be important for disconnection, consistent with the literature on parental factors and child socioeconomic outcomes (for examples of reviews, see Haveman and Wolfe, 1995; Duncan and Murnane, 2011). Students from below the top quartile of income and parental education are nearly twice as likely to be disconnected in young adulthood. Finally, females who had ever been pregnant at baseline (by ages 11 through 18) are significantly more likely to be disconnected later in life.
Figure 1. Early-Life Correlates of Disconnection
School: Academic Performance and Engagement
Students scoring within the top quartile in math and ELA test scores are less likely to become disconnected. This is consistent with a large body of research that suggests that academic performance predicts future labor market success (see Currie and Thomas, 2001; Heckman, Stixrud, and Urzua, 2006; Hanushek and Woessmann, 2008; among others). An indicator for whether a student was ever suspended at baseline (“ever suspended”) is significantly correlated with disconnection. This could reflect both behavioral issues and disengagement with school.
Mental Health
Disconnection is correlated with a higher likelihood of being clinically depressed at baseline, as measured by the CES-D scale (Radloff, 1977).[7] This result masks substantial heterogeneity by gender. Young women who report symptoms of depression are significantly more likely to become disconnected. There are no significant differences in depression rates between disconnected versus connected males. As alternative indicators for mental health, we also examine self-reported suicidal ideation and suicide attempts. Both indicators are also higher for disconnected youth overall. Disconnected females have significantly higher suicidal ideation than connected females; disconnected males have higher (although not significantly different) suicide attempts than connected males. These results suggest that disconnected youth are more likely to have faced mental health challenges of some kind during high school.
Behavioral Risk Factors: Substance Use and Delinquency
A body of research links child conduct problems and substance use to poorer adult outcomes across education, employment, and health (Balsa, Giuliano, and French, 2011; Farrington, 2005; Fergusson, Horwood, and Ridder, 2005). We use a series of variables on substance use during high school (including alcohol, cigarettes, and illegal drugs) to examine how substance initiation correlates with disconnection. We also use a set of survey questions on delinquency to create an index for child conduct issues.[8] This includes self-reported answers to questions asking whether the child has painted graffiti, damaged property, lied to parents or guardians about activities, stolen items, taken part in violence, or sold drugs at baseline. Disconnected youth have significantly higher reported cigarette and drug use during high school (the time of the baseline survey) than connected youth. Disconnected youth are 10 percent more likely to have ever smoked cigarettes during high school and nearly 20 percent more likely to have ever used drugs.[9] There is no significant difference in alcohol use between the two groups. This pattern of results is similar across gender. Disconnected youth score significantly higher on the baseline delinquency index, suggesting that behavioral and conduct issues might be an important risk factor for disconnection. This is consistent with the higher likelihood of suspension discussed above.
Social Support
Finally, social support, whether from family, peers, or community, might be one mitigating factor that helps individuals navigate challenges during adolescence and ultimately reduce the risk of disconnection. Strong social ties have been linked to better adult outcomes, including educational attainment, labor market attachment, and psychological well-being (Cohen and Wills, 1985; Furstenberg and Hughes, 1995; Crosnoe and Elder, 2004). We constructed a social support index from a module measuring respondents’ reported feelings of being supported and understood by parents, teachers, and friends. Consistent with the literature, higher social support during high school is correlated with a reduction in disconnection in early adulthood.
Suspension for Males and Early Pregnancy for Females Strongly Correlate with Disconnected Status, All Else Being Equal
Our results so far have examined differences in means across disconnected and connected populations. However, these relationships might be picking up spurious correlation with other factors. As our final analysis, we use multivariate regression analysis to examine all factors together, both overall and separately by gender (Figure 2).[10] This allows us to examine the relationship between each variable and disconnection, holding constant the influence of other factors. “Ever pregnant” and “ever suspended” remain highly positively correlated with disconnection. Examining the data separately by gender, “ever suspended” is only a significant correlate for males.[11] “Ever pregnant” at baseline is, by definition, only a significant correlate for females.
Suspension and early pregnancy might be mediating factors through which the other covariates affect disconnection. We examine correlates of suspension for males and correlates of early pregnancy for females.[12] For males, substance use (drugs and, to some extent, alcohol), high scores on the delinquency index, and being Black are positively correlated with suspension. Higher parental education, household income, ELA scores, and math scores are negatively correlated with suspension. These results are consistent with literature that suggests that school discipline, particularly suspension, is disproportionately applied to males and Black students (Okonofua and Eberhardt, 2015; Skiba et al., 2011). These practices are linked to worse academic and longer-term outcomes (Bacher-Hicks, Billings, and Deming, 2024; Perry and Morris, 2014). Alternative methods, such a restorative justice, have been shown to reduce suspensions, but they may need to be paired with academic supports to sustain achievement (Augustine et al., 2018; Gregory et al., 2016). Such measures may (or may not) serve to reduce eventual disconnection. For females, substance use (drugs), depression, and being Black are correlated with early pregnancy. These are purely correlational, but might shed light on risk factors that precede both early pregnancy and suspension and later disconnection.
Figure 2. Correlates of Disconnection, Regression-Adjusted
Probability of disconnection (with 95% CI)A dot plot comparing the probability of disconnection across many groups like female, household income, parent education, and Hispanic, for example.−0.2−0.100.10.20.30.4Female−0.0080.0470.101Household income (top quartile)−0.063−0.020.023Parent education (top quartile)−0.09−0.0420.006Parent married−0.085−0.0070.071Ever pregnant0.0860.2310.375ELA score (top quartile)−0.082−0.0270.028Math score (top quartile)−0.056−0.0080.041Ever suspended0.0660.1140.163Depression (CES-D)−0.067−0.0110.045Substance use: alcohol−0.089−0.0390.011Substance use: cigarettes−0.072−0.0160.041Substance use: drugs−0.0490.0760.202Delinquency Index−0.044−0.0150.013Social Support Index−0.043−0.024−0.005Hispanic−0.103−0.0210.061Black−0.0230.0810.184Native American−0.142−0.0390.064Asian/Pacific Islander−0.080.0070.093Other race−0.1250.0180.161Born in the United States−0.0870.0240.136
95% confidence intervals in brackets
Female = 0.0465 [-0.00783t to 0.101]
Household Income (Upper Quartile) = -0.0199 [-0.0625 to 0.0228]
Parent Education (Upper Quartile) = -0.0422 [-0.0902 to 0.00581]
Parent Married = -0.00722 [-0.0853 to 0.0708]
Ever Pregnant = 0.231 (p<0.01) [0.0862 to 0.375]
ELA Score (Upper Quartile) = -0.0271 [0.0821 to 0.0279]
Math Score (Upper Quartile) = -0.00765 [-0.0560 to 0.0407]
Ever Suspended = 0.114 (p<0.001) [0.0655 to 0.163]
Depression (CES-D) = -0.0110 [-0.0673 to 0.0453]
Substance Use: Alcohol = -0.0388 [-0.0888 to 0.0112]
Substance Use: Cigarettes = -0.0155 [-0.0718 to 0.0408]
Substance Use: Drugs = 0.0762 [-0.0492 to 0.202]
Delinquency Index = -0.0154 [-0.0435 to 0.0128]
Social Support Index = -0.0244 (p<0.05) [-0.0434 to -0.00547]
Hispanic = -0.0213 [-0.103 to 0.0608]
Black = 0.0805 [-0.0230 to 0.184]
Native American = -0.0388 [-0.142 to 0.0642]
Asian/Pacific Islander = 0.00660 [-0.0800 to 0.0932]
Other race = 0.0180 [-0.125 to 0.161]
Born in the United States = 0.0244 [-0.0869 to 0.136]
Constant = 0.127 (p<0.05) [0.00295 to 0.250]
Observations = 1598
SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year, and have been followed through 2018.
NOTE: This figure plots the coefficients from a linear regression of disconnection on the set of covariates. “White” is the excluded racial category. “Ever pregnant” is coded as 0 if the respondent was male. The upper and lower bounds show the 95-percent CI for each covariate. We use individual (person) weights to create statistics that are representative of the U.S. population.
The Path Forward
We provide preliminary evidence on the predictive factors that are associated with disconnection, including some of the first analyses using Add Health data to explore these relationships. Even in the years before disconnection emerges, youth who eventually become disconnected are different from their connected peers across several dimensions. They are more likely to report symptoms of depression, experience an early pregnancy, use substances, engage in delinquent activities, have weaker social support structures, and be suspended from school. Early pregnancy and school suspension, in particular, may serve as pathways through which other risk factors translate into later disconnection.
These estimates are descriptive rather than causal, and unobserved factors correlated with our explanatory variables may drive both early-life risks and eventual disconnection. However, this analysis highlights a set of risk factors that are likely to shape longer-run outcomes and provides a foundation for future research.
This work points to several implications for policymakers. First, early identification and prevention of risk factors associated with disconnection is critical. Eventual disconnection is correlated with academic performance, school suspension, early pregnancy, and substance use; this suggests that risks emerge early on. Preventative policies that reduce these risk factors and improve academic performance may reduce the likelihood of later disconnection.
Second, the links between suspension and disconnection suggest that restorative justice programs may not only be effective in reducing suspensions but also in reducing later disconnections. Restorative approaches emphasize repairing harm, relationship-building, and prevention, rather than excluding children from school. Evidence shows that restorative justice practices are effective in reducing suspension rates (Augustine et al., 2018; Gregory et al., 2016). This in turn may reduce the likelihood of youth disconnection by keeping students engaged in school.
Finally, evidence gaps remain. Evaluations of programmatic and policy interventions targeting these explanatory variables might help clarify mechanisms that lead to disconnection. For instance, evaluations of school-based programs that reduce suspensions or evaluations of interventions that delay early pregnancy could shed light on whether addressing these risk factors reduces disconnection. Importantly, future research should explicitly consider disconnection itself as a key outcome, assessing not only whether interventions affect intermediate risk factors but also whether they ultimately reduce the likelihood of youth becoming disconnected.
Appendix. Additional Figures
View Appendix
This appendix presents results from supplementary analyses referenced in the paper. Figure A.1 shows correlates of disconnection disaggregated by gender, Figure A.2 shows correlates of “ever suspended” among males, and Figure A.3 shows correlates of “ever pregnant” among females.
Figure A.1. Correlates of Disconnection: Male Versus Female, Regression-Adjusted
Probability of disconnection (with 95% CI)A dot plot comparing probability of disconnectedness of male versus female across various features like household income, parent education, ELA score, and math score, for example.Household income (upper quartile)−0.500.5Male−0.076−0.0320.013Female−0.1120.0210.154Parent education (upper quartile)−0.500.5Male−0.085−0.0370.011Female−0.164−0.0480.068ELA score (upper quartile)−0.500.5Male−0.067−0.0060.055Female−0.167−0.0770.013Math score (upper quartile)−0.500.5Male−0.074−0.0170.041Female−0.0980.0010.099Ever suspended−0.500.5Male0.0530.110.167Female−0.0290.1070.242Depression (CES-D)−0.500.5Male−0.124−0.0520.019Female−0.0090.0740.158Substance use: alcohol−0.500.5Male−0.104−0.0460.012Female−0.184−0.070.045Substance use: cigarettes−0.500.5Male−0.0510.0040.059Female−0.253−0.1060.041Substance use: drugs−0.500.5Male−0.0280.1180.265Female−0.1920.0420.276Delinquency Index−0.500.5Male−0.037−0.0070.023Female−0.114−0.0560.002Social Support Index−0.500.5Male−0.058−0.035−0.012Female−0.0340.0070.048Hispanic−0.500.5Male−0.109−0.0340.041Female−0.2040.0410.287Black−0.500.5Male−0.0070.1090.226Female−0.157−0.0290.098Native American−0.500.5Male−0.173−0.0810.012Female−0.2180.0990.417Asian/Pacific Islander−0.500.5Male−0.102−0.0230.056Female−0.1210.1640.449Other race−0.500.5Male−0.0740.0610.196Female−0.502−0.1510.2Born in the United States−0.500.5Male−0.090.0170.125Female−0.331−0.0070.317Ever pregnant−0.500.5MaleFemale0.0620.2160.369
95% confidence intervals in brackets
Household Income (Upper Quartile)
Male = -0.0317 [-0.0763 to 0.0129]
Female = 0.021 [-0.112 to 0.154]
Parent Education (Upper Quartile)
Male = -0.0371 [-0.0849 to 0.0108]
Female = -0.0476 [-0.164 to 0.0684]
ELA Score (Upper Quartile)
Male = -0.00632 [-0.0672 to 0.0545]
Female = -0.0772 [-0.167 to 0.0128]
Math Score (Upper Quartile)
Male = -0.0169 [-0.0744 to 0.0406]
Female = 0.00071 [-0.0980 to 0.0994]
Ever Suspended
Male = 0.110 (p<0.001) [0.0531 to 0.167]
Female = 0.107 [-0.0288 to 0.242]
Depression (CES-D)
Male = -0.0524 [-0.124 to 0.0190]
Female = 0.0743 [-0.00944 to 0.158]
Substance Use: Alcohol
Male = -0.0461 [-0.104 to 0.0119]
Female = -0.0695 [-0.184 to 0.0454]
Substance Use: Cigarettes
Male = 0.00379 [-0.0513 to 0.0589]
Female = -0.106 [-0.253 to 0.0410]
Substance Use: Drugs
Male = 0.118 [-0.0281 to 0.265]
Female = 0.0422 [-0.192 to 0.276]
Delinquency Index
Male = -0.00701 [-0.0366 to 0.0226]
Female = -0.0558 [-0.114 to 0.00225]
Social Support Index
Male = -0.0352 (p<0.01) [-0.0581 to -0.0123]
Female = 0.00696 [-0.0341 to 0.0481]
Hispanic
Male = -0.0339 [-0.109 to 0.0413]
Female = 0.0412 [-0.204 to 0.287]
Black
Male = 0.109 [-0.00726 to 0.226]
Female = -0.0293 [-0.157 to 0.0984]
Native American
Male = -0.0809 [-0.173 to 0.0115]
Female = 0.0992 [-0.218 to 0.417]
Asian/Pacific Islander
Male = -0.0228 [-0.102 to 0.0560]
Female = 0.164 [-0.121 to 0.449]
Other race
Male = 0.0609 [-0.0742 to 0.196]
Female = -0.151 [-0.502 to 0.200]
Born in the United States
Male = 0.0173 [-0.0902 to 0.125]
Female = -0.00718 [-0.331 to 0.317]
Ever Pregnant
Female = 0.216 (p<0.01) [0.0624 to 0.369]
Constant
Male = 0.127 (p<0.05) [0.0127 to 0.240]
Female = 0.306 [-0.0407 to 0.653]
Observations
Male = 1187
Female = 414
SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year, and have been followed through 2018.
NOTE: This figure plots the coefficients from a linear regression of disconnection on the set of covariates conducted separately for males and females. “White” is the excluded racial category. The upper and lower bounds show the 95-percent CIs for each covariate. We use individual (person) weights to create statistics that are representative of the U.S. population.
Figure A.2. Correlates of Ever Suspended for Males
Probability of suspension: Males (with 95% CI)A dot plot comparing probability of suspension of males across various features like household income, parent education, ELA score, and math score, for example.−0.2−0.100.10.20.30.4Household income (top quartile)−0.083−0.046−0.008Parent education (top quartile)−0.158−0.123−0.089ELA score (top quartile)−0.15−0.112−0.075Math score (top quartile)−0.091−0.057−0.024Depression (CES-D)−0.0390.0070.054Substance use: alcohol0.0020.0420.083Substance use: cigarettes−0.0260.0140.054Substance use: drugs0.0730.190.306Delinquency Index0.0840.1110.138Social Support Index−0.0140.0050.025Hispanic−0.0760.0080.092Black0.170.2350.3Native American−0.0440.0610.166Asian/Pacific Islander−0.0290.0620.152Other race−0.0870.020.128Born in the United States−0.163−0.087−0.011
95% confidence intervals in brackets
Household Income (Upper Quartile) = -0.0457 (p<0.05) [-0.0829 to -0.00845]
Parent Education (Upper Quartile) = -0.123 (p<0.001) [-0.158 to -0.0885]
ELA Score (Upper Quartile) = -0.112 (p<0.001) [-0.150 to -0.0748]
Math Score (Upper Quartile) = -0.0574 (p<0.001) [-0.0906 to -0.0242]
Depression (CES-D) = 0.00733 [-0.0391 to 0.0538]
Substance Use: Alcohol = 0.0424 (p<0.05) [0.00159 to 0.0832]
Substance Use: Cigarettes = 0.0143 [-0.0257 to 0.0543]
Substance Use: Drugs = 0.190 (p<0.01) [0.0730 to 0.306]
Delinquency Index = 0.111 (p<0.001) [0.0841 to 0.138]
Social Support Index = 0.00533 [-0.0140 to 0.0247]
Hispanic = 0.00800 [-0.0761 to 0.0921]
Black = 0.235 (p<0.001) [0.170 to 0.300]
Native American = 0.0614 [-0.0435 to 0.166]
Asian/Pacific Islander = 0.0617 [-0.0290 to 0.152]
Other race = 0.0202 [-0.0872 to 0.128]
Born in the United States = -0.0873 (p<0.05) [-0.163 to -0.0114]
Constant = 0.356 (p<0.001) [0.262 to 0.450]
Observations = 2545
SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year and have been followed through 2018.
NOTE: This figure plots the coefficients from a linear regression of suspension on the set of covariates conducted separately for males. “White” is the excluded racial category. The upper and lower bounds show the 95-percent CIs for each covariate. We use individual (person) weights to create statistics that are representative of the U.S. population.
Figure A.3. Correlates of Early Pregnancy for Females
Probability of early pregnancy: Females (with 95% CI)A dot plot comparing probability of early pregnancy across various features like household income, parent education, ELA score, and math score, for example.−0.1−0.0500.050.10.15Household income (top quartile)−0.020.0030.025Parent education (top quartile)−0.028−0.0110.005ELA score (top quartile)−0.0140.0090.033Math score (top quartile)−0.029−0.0090.01Depression (CES-D)0.0270.0540.08Substance use: alcohol−0.027−0.0020.023Substance use: cigarettes−0.0140.0060.025Substance use: drugs0.0290.0910.153Delinquency Index−0.02−0.0090.002Social Support Index−0.0040.0020.009Hispanic−0.0280.0240.076Black0.0030.0370.072Native American−0.052−0.0240.004Asian/Pacific Islander−0.0150.0260.067Other race−0.08−0.0280.025Born in the United States−0.041−0.0010.039
95% confidence intervals in brackets
Household Income (Upper Quartile) = 0.00262 [-0.0195 to 0.0247]
Parent Education (Upper Quartile) = -0.0114 [-0.0280 to 0.00521]
ELA Score (Upper Quartile) = 0.00915 [-0.0144 to 0.0327]
Math Score (Upper Quartile) = -0.00921 [-0.0287 to 0.0103]
Depression (CES-D) = 0.0538 (p<0.001) [0.0274 to 0.0802]
Substance Use: Alcohol = -0.00216 [-0.0270 to 0.0227]
Substance Use: Cigarettes = 0.00576 [-0.0135 to 0.0251]
Substance Use: Drugs = 0.0908 (p<0.01) [0.0286 to 0.153]
Delinquency Index = -0.00895 [-0.0197 to 0.00177]
Social Support Index = 0.00241 [-0.00443 to 0.00924]
Hispanic = 0.0238 [-0.0282 to 0.0758]
Black = 0.0371 (p<0.05) [0.00276 to 0.0715]
Native American = -0.0238 [-0.0518 to 0.00415]
Asian/Pacific Islander = 0.0262 [-0.0150 to 0.0674]
Other race = -0.0277 [-0.0801 to 0.0247]
Born in the United States = -0.000757 [-0.0409 to 0.0394]
Constant = 0.00562 [-0.0416 to 0.0528]
Observations = 1603
SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year and have been followed through 2018.
NOTE: This figure plots the coefficients from a linear regression of early pregnancy on the set of covariates conducted separately for females. “White” is the excluded racial category. The upper and lower bounds show the 95-percent CIs for each covariate. We use individual (person) weights to create statistics that are representative of the U.S. population.
Acknowledgments
We are grateful for the contributions and support of our colleagues Andrew Hoehn, Heather Schwartz, and Jennifer Kondo. We thank Ben Master and Christine Mulhern for their careful reviews. We are grateful to Monette Velasco, Libby Sweeney, Stephanie Lonsinger, and Mirka Vuollo for their assistance with editing and the publication process.
Notes
Employment is measured as working for pay for at least ten hours per week. Therefore, anyone who is working less than ten hours per week or not working for pay (and otherwise not in school) is counted as disconnected by this measure.We examine the period of disconnection during Wave 3; at this point, respondents were 18 to 28 years old, with a median age of 22. Waves 1 and 2 were collected using a sample of adolescents who were in school at the time of the survey, thus disconnection during these waves is, by design, close to 0 percent. Respondents in Wave 4 were aged 25 through 33, and thus outside the age range of disconnected youth, although we can eventually use this sample to examine longer run outcomes of disconnection.For our analysis, we use the Add Health public-use sample, which is one-third of the size of the full restricted-use sample. Future planned analysis will use the full sample. Return to content⤴
This means that our sample differs from the overall population of disconnected youth and those who were already disconnected at the time of high school; examining out-of-school students might reveal different characteristics that correlate with disconnection. Return to content⤴
Seventeen percent of females are disconnected whereas 14 percent of males are disconnected. Return to content⤴
These results are broadly similar to those found in Wenger and Bonds (2025). The differences likely stem from the different periods (Wenger and Bonds use data from 2019–2023) and perhaps from the different sampling frames (Add Health forms a sample from connected youth in high school). Return to content⤴
We note that these results are exploratory and not a comprehensive set of all possible covariates. Future planned work will use the Add Health restricted data, which have the full sample of respondents and a richer set of data, allowing us to calculate disconnection at a more granular level, examine geographic variation, and examine a more comprehensive set of covariates. Return to content⤴
We also examine a binary indicator for physical disability. Physical disability is correlated with disconnection but the sample of those disabled is extremely small (less than 1 percent) and thus our estimate is very imprecise. In future analyses, we will re-estimate this using the restricted-use sample which has three times the number of observations. Return to content⤴
The CES-D scale typically has 20 questions, with a score of 16 or above indicating clinical depression (Radloff, 1977). The version in the Add Health has 19 questions, and so we code 15 or above as clinical depression. Results are robust to an alternative coding of 16 and above. Return to content⤴
We create a means effect index of all 15 questions in the delinquency module to create our delinquency index. Return to content⤴
Illegal drug use includes significantly higher marijuana, cocaine, and “other” drug use; there was no significant difference in inhalant use. Marijuana use was about 30 percent higher in the disconnected sample (from 25 to 33 percent) and cocaine use was about 60 percent higher (from 3 to 5 percent). Both of these differences are statistically significant. “Other” drug use (other drugs not included in marijuana, cocaine, and inhalants) was 12 percent higher in the disconnected sample (from 8 to 9 percent) but not statistically significant. Return to content⤴
We estimated both a linear probability model and a logistic regression. Results are similar across models and so we show results from the linear model here. Return to content⤴
See Figures A.2. and A.3. in the appendix for these results. Return to content⤴
References
Augustine, Catherine H., John Engberg, Geoffrey E. Grimm, Emma Lee, Elaine Lin Wang, Karen Christianson, and Andrea A. Joseph, Can Restorative Practices Improve School Climate and Curb Suspensions:An Evaluation of the Impact of Restorative Practices in a Mid-Sized Urban School District, RAND Corporation, RR-2840-DOJ, 2018. As of September 30, 2025: https://www.rand.org/pubs/research_reports/RR2840.html
Bacher-Hicks, Andrew, Stephen B. Billings, and David J. Deming, “The School to Prison Pipeline: Long-Run Impacts of School Suspensions on Adult Crime,” American Economic Journal: Economic Policy, Vol. 16, No. 4, November 2024.
Balsa, Ana I., Laura M. Giuliano, and Michael T. French, “The Effects of Alcohol Use on Academic Achievement in High School,” Economics of Education Review, Vol. 30, No. 1, 2011.
Belfield, Clive R., Henry M. Levin, and Rachel Rosen, The Economic Value of Opportunity Youth, Corporation for National and Community Service, 2012.
Cohen, Sheldon, and Thomas A. Wills, “Stress, Social Support, and the Buffering Hypothesis,” Psychological Bulletin, Vol. 98, No. 2, 1985.
Crosnoe, Robert, and Glen H. Elder, Jr., “Family Dynamics, Supportive Relationships, and Educational Resilience During Adolescence,” Journal of Family Issues, Vol. 25, No. 5, 2004.
Currie, Janet, and Duncan Thomas, “Early Test Scores, School Quality and SES: Longrun Effects on Wage and Employment Outcomes,” in Solomon Polachek, ed., Worker Wellbeing in a Changing Labor Market, Emerald Group Publishing Limited, 2001.
Duncan, Greg J., and Richard J. Murnane, eds., Whither Opportunity? Rising Inequality, Schools, and Children’s Life Chances, Russell Sage Foundation, 2011.
Farrington, David P., “Childhood Origins of Antisocial Behavior,” Clinical Psychology & Psychotherapy, Vol. 12, No. 3, 2005.
Fergusson, David M., L. John Horwood, and Elizabeth M. Ridder, “Show Me the Child at Seven: The Consequences of Conduct Problems in Childhood for Psychosocial Functioning in Adulthood,” Journal of Child Psychology and Psychiatry, Vol. 46, No. 8, 2005.
Furstenberg, Frank F., Jr., and Mary Elizabeth Hughes, “Social Capital and Successful Development Among At-Risk Youth,” Journal of Marriage and the Family, Vol. 57, No. 3, 1995.
Gregory, Anne, Kathleen Clawson, Alycia Davis, and Jennifer Gerewitz, “The Promise of Restorative Practices to Transform Teacher-Student Relationships and Achieve Equity in School Discipline,” Journal of Educational and Psychological Consultation, Vol. 26, No. 4, 2016.
Hair, Elizabeth C., Kristin A. Moore, Thomson J. Ling, Cameron McPhee-Baker, and Brett V. Brown, “Youth Who Are ‘Disconnected’ and Those Who Then Reconnect: Assessing the Influence of Family, Programs, Peers and Communities,” Child Trends, Vol. 37, July 2009.
Hanushek, Eric A., and Ludger Woessmann, “The Role of Cognitive Skills in Economic Development,” Journal of Economic Literature, Vol. 46, No. 3, 2008.
Harris, Kathleen Mullan, and J. Richard Udry, “National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994–2018 [Public Use],” dataset, version 25, Carolina Population Center, University of North Carolina–Chapel Hill, Inter-university Consortium for Political and Social Research, August 9, 2022. As of October 7, 2025: https://doi.org/10.3886/ICPSR21600.v25
Haveman, Robert, and Barbara Wolfe, “The Determinants of Children’s Attainments: A Review of Methods and Findings,” Journal of Economic Literature, Vol. 33, No. 4, 1995.
Heckman, James J., Jora Stixrud, and Sergio Urzua, “The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior,” Journal of Labor Economics, Vol. 24, No. 3, 2006.
Lewis, Kristen, A Decade Undone: 2021 Update, Measure of America, July 29, 2021.
MaCurdy, Thomas, Bryan Keating, Sriniketh Suryasesha Nagavarapu, and David Glick, “Reprint of: Profiling the Plight of Disconnected Youth in America,” Journal of Econometrics, Vol. 243, No.1–2, July 2024.
Okonofua, Jason A., and Jennifer L. Eberhardt, “Two Strikes: Race and the Disciplining of Young Students,” Psychological Science, Vol. 26, No. 5, 2015.
Perry, Brea L., and Edward W. Morris, “Suspending Progress: Collateral Consequences of Exclusionary Punishment in Public Schools,” American Sociological Review, Vol. 79, No. 6, 2014.
Radloff, Lenore Sawyer, “The CES-D Scale: A Self-Report Depression Scale for Research in the General Population,” Applied Psychological Measurement, Vol. 1, No. 3, 1997.
Skiba, Russell J., Robert H. Horner, Choong-Geun Chung, M. Karega Rausch, Seth L. May, and Tary Tobin, “Race is Not Neutral: A National Investigation of African American and Latino Disproportionality in School Discipline,” School Psychology Review, Vol. 40, No. 1, 2011.
The Partnership for Student Success (PSS) today announced the publication of a new report, Are K-12 Students Getting the Evidence-Based Supports They Need? Progress and Challenges Four Years After the Pandemic. The report, authored by Dr. Robert Balfanz and Vaughan Byrnes of the Everyone Graduates Center at the Johns Hopkins University School of Education, analyzes findings from a third annual nationally representative survey of K-12 public school principals, fielded by the RAND Corporation in partnership with PSS, to examine the deployment of evidence-based student supports and evolving student need.
The report concludes that four years after the height of the pandemic, there is widespread use of evidence-based and people-powered student supports–such as high-intensity tutoring, mentoring, student success coaching, postsecondary transition coaching, and wraparound supports–in public schools across the United States. But, public school principals indicate that continued growth in these interventions is needed to meet the scale of student needs.
Key findings from the report include:
High-intensity tutoring, mentoring, and wraparound supports are each provided in about half of the nation’s K-12 public schools, and in about two-thirds of high-poverty schools, with most schools offering these services providing them to 20% or fewer of their students.
Over the past three school years, an estimated 400,000 additional adults have stepped up to support K-12 students in public schools as tutors, mentors, postsecondary advisors, and wraparound support providers.
Four years after the height of the pandemic, public school principals report no let-up in student need with 30% to 40% reporting an increase in the number of students needing high-intensity tutoring, mentoring, or wraparound supports.
The report emphasizes that while implementation barriers exist to expanding evidence-based programs, there is a subset of schools that are proving that serving students at scale is possible, and outlines a range of resources and opportunities to support expansion of high-quality programs.
About the Partnership for Student Success: Based at the Everyone Graduates Center at the Johns Hopkins University School of Education, the Partnership for Student Success is a national coalition dedicated to expanding evidence-based and people-powered student supports for all K-12 students in the United States, with a particular focus on high-impact tutoring, mentoring, student success coaching, postsecondary transition coaching and wraparound/integrated student supports.
More suspensions went to students who are Hispanic, learning English or in special education
Montgomery County schools saw an increase in student suspensions at the start of this school year.
During the first five weeks of classes, the district recorded 296 out-of-school suspensions compared to 230 during the same period last school year.
The 29% increase was driven by more suspensions handed down to Hispanic students, children who are learning English and those who receive special education services.
Districts across the nation are confronting the consequences of lower birth rates and shifting demographic patterns
Montgomery County Public Schools enrollment dropped to a 10-year low.
Roughly 156,540 students attend county schools — a decrease of more than 2,600 kids since last year, according to preliminary data. While that’s less than a 2% drop, it’s part of a larger pattern of decline since the district’s peak enrollment in 2019.
MCPS predicts a more dramatic dive: a 9% dip from its 2019 peak to the enrollment forecast for 2031.
“This is an uncomfortable conversation for Montgomery County, because this has not been our experience for much of the past few decades,” MCPS Superintendent Thomas Taylor said.
The enrollment dive is part and parcel of big changes planned for Maryland’s largest district. The school system is in the midst of a boundary study for secondary campuses, and Taylor said he wants to expand that effort to elementary schools next.
Montgomery County is not alone in confronting enrollment declines. Districts across the country are confronting the consequences of lower birth rates and shifting demographic patterns.
“We’ve added a lot of housing, and we’ve added a lot of people, and we’ve grown very fast, but something else has changed,” Taylor said. “The percentage of households that have children has dramatically reduced.”
Fewer 5-year-olds
Montgomery County’s declines are driven by drops in kindergarten enrollment and international students.
The kindergarten data is straightforward to explain: Fewer children have been born in the county over the past several years.
The story of international student enrollment is more complex, with Taylor gesturing toward Washington but not going so far as to draw a line to the Trump administration’s aggressive approach to immigration.
“It may be causation, but it’s definitely correlation,” the superintendent said.
Maryland schools are funded based on enrollment, so fewer students in seats means less money flowing toward Montgomery County campuses.
In several cities across the country, school leaders have responded to enrollment drops by closing campuses — a painful choice that often devastates neighborhood families.
Taylor said it’s too soon to tell if those tough choices are in the county’s future.
“Not based on the projections that I have right now,” he said.
By 2031, the district anticipates enrollment dropping just below 150,000 students.
Talia Richman is the Montgomery County education reporter at The Banner. She previously covered schools for The Dallas Morning News. The Education Writers Association has recognized Talia as among the best education beat reporters in the nation. Before her time in Texas, she covered schools and City Hall for The Baltimore Sun.
This month, my three science of learning insights revolve around metacognition, an often misunderstood aspect of learning.
1. Metacognition requires concentration and quiet because attention is a filter, not a spotlight.
There’s a lot of district-level focus on metacognition: planning, monitoring, and reflecting on one’s thinking as a way to move students through productive struggle. Here’s a quick point to understand about metacognition and students’ attention: Noisy classrooms that boast high engagement might actually be counter-productive to metacognition. Carl Hendricks, professor of Applied Learning Sciences at Academia University in the Netherlands, points out that students rarely suffer from boredom or attention shortages, they suffer from attention surplus – too much stimuli that has to be actively filtered out. Why does this matter? Because attention is not limitless. That filter has a narrow bandwidth, easily clogged by noise and distraction. If classroom talk is not purposeful, it can be the most powerful distractor of all. The job of teaching is not just to provide something worth noticing, but to protect students from everything that isn’t worth their attention at the moment.
So, how can you use this information? The solution isn’t to eliminate all talk or stimulation, it’s to be intentional about what demands attention. Teachers whose instruction is grounded in the science of learning create what we might call “attentional architecture”: environments where what is important stands out precisely because what’s unimportant is de-emphasised through well-designed instruction. Read more about what Dr. Hendricks says about it here.
2. Metacognition requires both awareness and an “attack plan.”
When we keep in mind that the main work of metacognition is task analysis, the “attack plan” begins with the move I call Size It Up/Break It Down in Rebuilding Students’ Learning Power. Third grade teachers and researchers, Grace Douglas, Alison Hardy, Katie MacLean, and Sarah Powell (University of Texas at Austin) say that word problems are notoriously challenging for students who have difficulty with mathematics. According to one analysis, word problems make up over 90 percent of the items on high-stakes math tests, so figuring out how to help students improve is an important instructional goal. What students find most difficult in word problems isn’t just the words, but grasping what’s going on in each problem, then translating the scenario into computation, and keeping all this in working memory as they solve the problem slows down computing the answer. “When we reinforce the ineffective keyword strategy,” say Douglas, Hardy, MacLean, and Powell from the study, “we are setting students up for failure, particularly with inconsistent problems.” What’s a better way to solve word problems? Rather than beginning with circling key words, the authors recommend beginning with a metacognitive attack strategy, like this:First, you have to understand the problem. After understanding, make a plan (convert to a computation problem).Carry out the plan. Look back at your work. How could it be better?How to use this information? There are variations on developing an “attack” strategy, but they have one thing in common: the first step is urging students to take the time to just understand what’s going on, because most students skip this stage. Make this an activity where students talk to each other about what they think is going on in the word problem. This is especially helpful in supporting multilingual students. The second step is choosing a simple attack strategy, ideally with a catchy mnemonic that students can remember. But it all begins with metacognition. Check out the article here.
3. Instructional Illusions: The Illusion of Being Student-Centered
In his new book, Instructional Illusions, Carl Hendricks outlines ten common traps we fall into when it comes to teaching in ways that go against the science of learning. One he points out is the illusion of being student-centered when in reality we structure instruction around teacher-led processing masquerading as student-centered instruction. The most evident sign that we’ve fallen into this trap is over-scaffolding where the teacher is doing most of the cognitive work in the classroom to move students through a lesson. Hendricks suggests that to shift toward an authentically student-centered classroom, we have to help students develop metacognitive awareness of what they already know about a subject, no matter how unrelated it might seem. Why is this a key to a student-centered classroom? Because, as Hendricks points out, there are two principles of learning: Learners come to understand new information in reference to what they already know. My simple way of saying it is: all new learning must be coupled with existing knowledge. Like LEGOs. If a student doesn’t practice metacognitive awareness of what they know, their brain’s synapses aren’t prepped for incorporating new information. How to use this information? Rather than front-loading information we call “background knowledge,” give students time to surface all the ways they might know something about the topic from their everyday lives – movies, songs, memes. Help them make their schema explicit by starting with metacognitive awareness.
2 Quotes: Thinking About Metacognition
Metacognition has many facets, from self-awareness and reflection to understanding our own learning processes and thought patterns. Here are two quotes indirectly related to the topic. “We cannot solve our problems with the same thinking we used when we created them.” — Albert Einstein Einstein reminds us that innovation begins with being aware of our current thinking. “The real voyage of discovery consists not in seeking new landscapes, but in having new eyes.” — Marcel Proust, Remembrance of Things Past This quote captures Proust’s insight that true discovery and understanding come from deepening our awareness of what’s already available around us.
1 Juicy Resource
Here is a great resource: the How Learning and Teaching Happen website, hosted by Academia University of Applied Sciences. There, you’ll find several free resources that can help you authentically begin incorporating the science of learning. Start with this super helpful, free How Learning and Teaching Happens Implementation Guide. It’s really juicy.
Yet the plan covers only about half the district’s needs, Superintendent Thomas Taylor acknowledged
Montgomery County schools Superintendent Thomas Taylor wants to replace Eastern Middle School, renovate Sligo Middle School and close Silver Spring International Middle School as part of a major plan to tackle the district’s aging facilities.
If his proposal is approved, several elementary school buildings could also be replaced by 2031, as could Damascus High School.
Many other campuses should also be on the list for major renovations, the superintendent said, but there just isn’t enough money to cover everything.
“There’s hard truths coming,” Taylor said during a Monday presentation in which he ran through the data that guided his capital improvements program recommendations.
Taylor is asking for a $2.7 billion investment in campus infrastructure over six years. He warned that the enormous figure covers only half of the district’s actual facility needs.
His recommendations are far from a done deal. The school board must sign off on it, and then county government.
As Taylor ran through the numbers, Council Vice President Will Jawando listened near the front of the district’s Rockville boardroom. He said the county must balance competing interests for funds.
Taylor’s presentation was “realistic about the need,” Jawando said. “As far as what we can afford, that’s a different question, right?”
Complaints about campus infrastructure have long dogged Montgomery County Public Schools leaders. The district operates more than 230 buildings, some built more than a half century ago.
“This is a big ask,” Taylor said. “It’s a big ask because our needs are really significant.”
Earlier this year, staff members at a school board meeting decried building conditions they said made them sick. Parents regularly tell board members about problems at their kids’ campuses, from broken HVAC systems to mold.
The school district has more than $740 million worth of overdue HVAC projects alone, Taylor said.
The school district’s old strategy for dealing with facility issues was burying its head in the sand, said Taylor, who has led the district since 2024. That led to a system in which the majority of county public schools need repairs or are functionally unreliable, according to a recent report from the comptroller’s office.
“It took us two decades to get into this mess. It’s going to take two decades to get out of this mess,” Taylor said.
Those who will be stuck waiting on their campus’ turn for renovations are sure to feel frustrated.
“I would love to include them if funds would allow for that,” Taylor said. “They don’t.”
Brigid Howe, president of the Montgomery County Council of PTAs, said she feels for students at schools on the cusp, like Magruder and Wootton high schools, which need repair but don’t make it onto the high-priority list.
Those campuses “have real needs,” she said, “and are having to wait yet again.”
Other changes could be coming too. Taylor said he wants the district to do an elementary school boundary study after it finishes the process at the secondary level.
Shortly before the meeting, Taylor spent 15 minutes on a Zoom call with Silver Spring International Middle School families to give them a heads-up about his proposal.
He rattled through the campus’ myriad problems: poorly designed staircases, subpar bathrooms and other “really scary” issues. He said it’s no longer a tenable learning environment.
“All of that being said, it’s still not the worst facility in Montgomery County Public Schools,” Taylor said.
The superintendent said his plan to close the school will not affect current SSIMS students.
School parents asked how the district will keep their children safe on campus until then. They’ve raised concerns about mold, leaky ceilings, broken railings and other problems that have lingered for years.
“We will continue to make improvements to SSIMS as a building, but just know that it won’t be sweeping, big changes” because it’s not worth pumping that much money into the building and needs are even greater elsewhere, Taylor told them.
The building could eventually be used as a “holding school” for students while their own campuses are undergoing renovations.
Silver Spring International Middle School operates out of what was the original Montgomery Blair High School building, constructed in the 1930s.
That history now “haunts” the facility, Taylor said.
The school board will host community listening sessions over the next several weeks.
The global rise in unhappiness over the past decade has been well-documented, yet many leaders have overlooked it because they rely on economic indicators while ignoring daily emotional health.
This oversight matters because negative emotions do not just reflect distress; they narrow people’s focus and erode their coping capacity. When these feelings become chronic, they leave individuals and societies more vulnerable to instability.
As the world’s mood has soured, it has also become less stable, with rising political unrest, more conflicts and higher death tolls.
The Global Peace Index, which tracks the absence of violence and conflict across 163 countries, shows riots, strikes and antigovernment demonstrations rose 244% from 2011 to 2019 — notably, even before the pandemic.
In 2024, adults worldwide reported high levels of daily distress:
39% felt a lot of worry, 37% felt stress, 32% experienced physical pain, 26% felt sadness 22% felt anger.
All are higher than they were a decade ago. Gallup’s inaugural State of the World’s Emotional Health 2025 report, based on 145,000 interviews across 144 countries and areas, reveals that daily distress may serve as an early-warning signal of fragility, with direct implications for health systems, stability and global development.
The report reveals:
Negative emotions remain elevated. Worry, stress, physical pain, sadness and anger are all higher than they were a decade ago.
Positive emotions are steady. Daily experiences of laughter, enjoyment and feeling well-rested held at long-term averages.
Peace shapes emotions. High levels of anger and sadness go hand in hand with weaker peace on the Global Peace Index, which tracks conflict, and the Positive Peace Index, which gauges the institutions that sustain stability.
Explore the full report to see where distress is deepening, where wellbeing persists and what it could mean for global stability, offering leaders a new way to read risk and to build more stable, healthier societies.
Posts by Paul Costello1
