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Promoting Grit, Tenacity, and Perseverance - U.S. Department of ...

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Draft<br />

Methodological trade<strong>of</strong>fs. Using classroom observation protocols <strong>and</strong> reports from teachers or<br />

other pr<strong>of</strong>essionals addresses concerns regarding bias in self-report, <strong>and</strong> informant reports can be<br />

particularly helpful for ascertaining intrapersonal competencies in young children. If observers<br />

are able to consistently judge behaviors <strong>and</strong> interactions, observation protocols can be valuable<br />

for evaluating the characteristics <strong>of</strong> exchanges among students <strong>and</strong> teachers, how students<br />

interact with tools, student affect, or whether they are applying learning strategies or sticking<br />

with a task. Collecting <strong>and</strong> analyzing enough observation data to draw generalizable conclusions<br />

is resource intensive as systematic analyses <strong>of</strong> observation data may require video recording,<br />

training <strong>of</strong> observers to evaluate behaviors <strong>and</strong> interactions consistently, <strong>and</strong> time to complete<br />

the observations <strong>and</strong> coding. Observational approaches also can present challenges in capturing<br />

information about an individual’s mindset, which is less directly observable, <strong>and</strong> affect, which<br />

can be difficult to judge.<br />

School Records<br />

Methods. Another source <strong>of</strong> data about students’ perseverance is school records about grades,<br />

st<strong>and</strong>ardized test scores, attendance, dropping-out, discipline problems, social services used, <strong>and</strong><br />

so on. The Exp<strong>and</strong>ing Evidence report points to important trends in the availability <strong>and</strong><br />

application <strong>of</strong> technology-supported institutional-level data for supporting at-risk students (U.S.<br />

<strong>Department</strong> <strong>of</strong> Education Office <strong>of</strong> Educational Technology, 2013). Data at the institutional level<br />

is becoming increasingly streamlined <strong>and</strong> cross-referenced, improving the capacity to link<br />

student data within <strong>and</strong> across systems.<br />

Some example measures. One example is the Youth Data Archive (YDA) at the Gardner Center<br />

at Stanford University, which links data across systems—school, social services, foster care,<br />

youth development programming, juvenile justice—to provide actionable integrated student<br />

pr<strong>of</strong>iles to educators. Another example is the use <strong>of</strong> interest-driven badging systems, for<br />

example, Mozilla Open Badges (http://www.openbadges.org), to recognize in <strong>and</strong> out <strong>of</strong> school<br />

learning <strong>and</strong> accomplishments.<br />

Methodological trade<strong>of</strong>fs. Data from school records provides new possibilities for rich<br />

longitudinal analyses <strong>of</strong> educational impacts, as well as for informing early warning systems that<br />

can be used to identify students who are not managing to persevere in the face <strong>of</strong> all <strong>of</strong> the<br />

challenges <strong>of</strong> schooling. These records, however, are only broad indicators <strong>of</strong> perseverance <strong>and</strong><br />

do not tell the richer story <strong>of</strong> an individual’s characteristics or how an individual’s interactions<br />

with features <strong>of</strong> the learning environment contribute to these outcomes.<br />

40

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