5700 A2
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School
Columbia University *
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Course
5700
Subject
Arts Humanities
Date
Dec 6, 2023
Type
docx
Pages
3
Uploaded by GeneralKnowledge1553
1.
The two main objectives of TFA are recruiting high quality college
graduates to serve as teachers in underprivileged schools and reducing
the educational inequality.
TFA has a significant emphasis on data which can improve the efficiency
of the recruitment process and the offer accepted rate. TFA also have
large supportive alumni network which can helped further TFA’s mission.
However, there are also some factors that hindered TFA’s success: TFA
faced a decline in application and also the TFA recruiters can effectively
allocating their time among a large number of potential applicants. Due to
the short time window to the next round of applications, the senior
manager Metzger in RT/AT Strategy team needs time to train TFA’s non-
technical employees to interpret data.
2.
Technical Aspects: TFA first transited to CRM which allowed for more
efficient and accurate data management instead of manual data tracking.
Then they utilized these data collecting through recruitment and admission
processes to build predictive analytics models to access candidate quality,
retention risks and so on.
People Aspects: TFA had historically placed a high level of importance on
data. After Elissa Kim joined TFA, even thought she did not have technical
background, she recognized the importance of tracking information. She
even teched herself and guide her team to transit to digital CRM system.
Then Matt Kramer helped TFA to grow an analytical mind on how to use
data to answer the critical questions during the recruiting process. Later
Metzger and Even launched a predictive analytics team to discussing how
a modeling approach could be used to improve TFA’s recruitment process.
Process Aspects: TFA initially built models based on education research to
assess candidate quality. Over time, they refined these models using
available data to identify strong applicants and improve the selection
process. TFA explored different approaches to measure the success of
corps members in the classroom. This included using external
assessments and surveys to gauge the impact on students' achievement.
3.
A.
RM/RA team needs to focus on the candidates who has higher GPA,
undergrad major in humanities, undergrad minor in education/humanities
or don’t have undergrad minor, whose graduating school is least selective,
more and most elective on annual university rankings.
B.
5000*(20%-15%)=250
If RM/RA can decrease the number of applicants who withdraw from the
process from 20% to 15% follow my guidance then it will result 250 of
applicants completed the process.
C.
I would choose attendevent variable to examine. This variable
indicating whether an applicant attended a TFA event can be a strong
indicator of their interest and commitment. Candidates who attended
events may be more likely to complete the process. Investigating the
impact of event attendance on completion rates is crucial for fine-tuning
the targeting process.
D.
These prioritized candidates will require additional attention from
RM/RA. RM/RA should consider send out additional email to notify these
people to complete their application. Or to simplify the application
requirement for these prioritized candidates.
E.
Firstly, I'd appeal to their rational "rider" side by explaining that no
model is flawless, and there will always be some exceptions. Just as a
rider must adapt to unexpected obstacles on the path, our model is
designed to capture trends, not individual cases. Subsequently, I'd connect
with their emotional "elephant" side by emphasizing that the model's
primary purpose is to help us identify and support those who need it most.
It's akin to guiding the elephant towards a safer path. While a few low-risk
applicants may withdraw, our focus is on significantly reducing the overall
withdrawal rate, which ultimately benefits our recruitment efforts. In this
manner, I would reassure them that the model remains a valuable tool for
enhancing our decision-making and resource allocation, all while
acknowledging the necessity of human judgment in exceptional cases.
F.
Wendy, I want to give you a quick update on our project at TFA. We're
making strides in optimizing our recruitment process through data
analysis. We're using a logistic regression model to pinpoint high-risk
applicants who might not complete the application process. Our goal is to
provide targeted support and reduce dropout rates. We've also addressed
concerns from some recruiters about the model's accuracy, especially
when a few low-risk applicants withdrew. We explained it using the
metaphor of the rider and the elephant, emphasizing that while exceptions
occur, the model guides us toward a safer path overall. Our team is
excited about the potential of this approach to boost successful
applications and streamline our recruitment. By blending data insights with
a personal touch, we're confident in enhancing TFA's mission to combat
educational inequality. Your continued support and guidance mean a lot as
we refine and implement this strategy.
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