How to improve student educational outcomes: New insights from data
analytics
By
applying advanced analytics and machine learning, we have identified factors
that play a critical role in student achievement.
A well-educated citizenry is an economic and social necessity. Policy
makers, educators, and parents all over the world want students to understand
and be able to apply their knowledge of math, reading, and science. Yet
improving educational outcomes has proved elusive. Some countries, states and
municipalities have made great strides, but many continue to struggle.
Educators continue to debate what matters and what works.
In this series of reports, we take a
data-driven approach to consider a few of the most active debates: Do mindsets
matter? If so, to what extent? What teaching practices work best? Does
technology help? Our data comes from the Program for International Student
Assessment (PISA), administered by the Organisation for Economic Co-operation
and Development (OECD). Broad in scale and scope, PISA covered more than half a
million students in 72 countries in 2015. What makes PISA so powerful is that
it goes beyond the numbers, asking students, principals, teachers, and parents
a series of questions about their practice, attitudes, behaviors, and
resources.
By applying advanced analytics and machine
learning, we have identified factors that play a critical role in student
achievement. We will be publishing five regional reports to share these
findings: on Asia–Pacific; Europe; Latin America; the Middle East and North
Africa (MENA); and North America. Here we summarize the findings that were most
consistent across all five regions: on the topics of mindsets and teaching
practices.
Finding
1: Having the right mindsets matters much more than socioeconomic background.
It is hardly news that students’ attitudes
and beliefs—what we term their “mindsets”—influence their academic performance.
But how much? To answer that question, we identified the 100 most predictive
variables from the PISA survey (out of more than 1,000). We then sorted these
into the following categories: home environment, school resources and
leadership, teachers and teaching, and student mindsets and behaviors.
Our conclusion: after controlling for all
other factors, student mindsets are twice as predictive of students’ PISA
scores than even their home environment and demographics). This finding, and
its magnitude, is consistent across all five regions, which amplifies its
importance.
Several mindsets emerged as highly
predictive of performance. Top of the list was the ability to identify what
motivation looks like in day-to-day life, what we call “motivation
calibration.” Students who can recognize that motivated students prepare for
class, do more than expected, and work to perfection outperform those who do
not by between 12 and 15 percent depending on their region. Similarly, students
with a “growth mindset”—those who believe they can succeed if they work
hard—performed 9 to 17 percent better than those with a “fixed mindset”—those
who believe their capabilities are static.
It was particularly striking that several
of these mindsets made the most difference for students either in low performing
schools or in lower socioeconomic quartiles. In fact, for students in schools
with low outcomes, having a well-calibrated motivation mindset is equivalent to
vaulting into a higher socioeconomic class. This result was consistent across
all regions.
Mindsets, of course, are not everything.
They cannot compensate for all economic and social disparities, and, in
general, being richer rather than poorer remains a great educational advantage.
But the PISA evidence shows that mindsets matter a great deal, particularly for
those living in the most challenging circumstances. So far, the research on
this subject is both nascent and predominantly US-based. Considering its
importance, establishing how mindsets can be shifted in a positive direction
should be a priority globally.
Finding
2: Students who receive a blend of teacher-directed and inquiry-based
instruction have the best outcomes.
There are two dominant types of teaching
practices. The first is “teacher-directed instruction,” in which the teacher explains
and demonstrates ideas, considers questions, and leads classroom discussions.
The second is “inquiry-based teaching,” in which students are given a more
prominent role in their own learning—for example, by developing their own
hypotheses and experiments.
We analyzed the PISA results to understand
the relative impact of each of these practices. In all five regions, when
teachers took the lead, scores were generally higher, and the more
inquiry-based learning, the lower the scores. That sounds damning for
inquiry-based learning at first glance, but by digging deeper into the data, a
more interesting story is revealed: what works best is when the two styles work
together—specifically, with teacher-directed instruction in most or almost all
classes, and inquiry-based learning in some. This “sweet spot” is the same in
all five regions, suggesting there is something akin to a universal learning
style.
What differs across regions is the
expected benefit from moving to the sweet spot from a purely teacher-directed
approach. In developed school systems with strong performance on PISA overall,
there is substantial benefit (
In developing school systems with weaker
performance, the benefit is much smaller, and these systems may be better off
initially focusing on consistent quality teacher-directed instruction.
Across all systems, we conclude that it is
only when students master enough content—usually through teacher-led
classes—that they can fully benefit from inquiry-based learning.
Even a survey as large and rigorous as the
PISA assessment provides only some of the answers. Nevertheless, we believe
that our findings provide useful insights to guide policy makers as they make
their way to their ultimate destination—improving the education and thus the
lives of students all over the world.
By Mona Mourshed, Marc Krawitz, and Emma Dorn September 2017
https://www.mckinsey.com/industries/social-sector/our-insights/how-to-improve-student-educational-outcomes-new-insights-from-data-analytics?cid=other-eml-alt-mip-mck-oth-1709&hlkid=ce1002b9ec5444f29d56ee3e815b65ed&hctky=1627601&hdpid=8e669367-12fc-444f-8e5b-30f91747a612
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