portfolio

Bloom Institute of Technology

2020 - 2021

BloomTech (formerly Lambda School) is an online web development and data science program that embeds real-world projects and career readiness into the curriculum, ensuring students graduate ready to secure a job in their field. Students have the option to pay nothing up front, only repaying tuition interest-free after they have secured a job.

Founded in 2017, I joined the team in March 2019, shortly after they raised Series B. I led career and professional development program operations, overseeing the only function in the school that supported students from admission through post-graduation.


Results snapshot

Nearly 4x increase in students completing content over ~4 months

87% of learners rated the content as “very helpful” or “extremely helpful”

Reduced time to job search-ready from 4 weeks to <1 week post-graduation

Project description

Redesign career curriculum model to more closely align with technical curriculum, uphold greater accountability for students, provide greater insight into student career readiness, and ensure all students graduate ready to job search.

9-month project involved conducting user research, forecasting and allocating resources, creating learner content, building SOPs, training instructors, presenting project updates to leadership, and overseeing program delivery after launch. Success metrics (shared above) exceeded program goals.

Goals

  • Embed career curriculum as a required part of the student learning journey

  • Ensure 100% of graduates are job search-ready at graduation

  • Track student engagement in career curriculum

  • As a result of the above, shorten time-to-hire for graduates

Audience

100+ new students per month, to be supported over full length of the program (totaling 1000+ concurrent students at any given time). Students were located across the United States, and some internationally. Students represented all age groups and career backgrounds.

Challenges

  • Operational complexity: At the time, BloomTech practiced a synchronous learning model that allowed for flexibility if students needed to repeat a section of the curriculum. As a result, learning cohorts were in constant flux, making it difficult to predict size of upcoming classes to appropriate allocate resources. Further, there was no way to implement changes for cohorts that started before the career curriculum updates, so we planned for launch starting with new cohorts after a certain date. The flexible cohort model meant dozens of previously-enrolled students joined new cohorts learning under the new career curriculum model, causing student confusion.

  • Scalability: Evaluating a student’s mastery of career-related curriculum is more nuanced than assessing technical content mastery, which has multiple options for automated grading. Large student:instructor ratios made it difficult to thoroughly assess students’ knowledge, requiring reliance on imperfect AI tools and quizzes.

  • Data infrastructure: Data tracking was dispersed, and existing tools did not have the features needed to track desired metrics. Limited engineering and product resources required stopgap and imperfect data tracking solutions, increasing technical debt.

  • Student diversity and resultant buy-in: Student career readiness varied greatly, and students who brought years of professional experience and career knowledge were, understandably, frustrated by regressive requirements.

Cross-functional partners

Career coaches, instructors, Student Services, Outcomes, product, engineering

Learning
modalities

Written curriculum, prerecorded visual content with audio overlay, live video instruction, automated assessments, human-graded assessments

This project started more than a year into my tenure, so I approached it with a robust toolkit of feedback, institutional knowledge, and experimentation under my belt already.

When approaching projects, I generally prefer to start with smaller experiments to test ideas before investing significant time and resources into large-scale execution. However, my team’s ability to segment students into test groups was limited by program structure and complexity; too many variables impacted student progression to derive meaningful insights from career program variables, and lack of automation meant that any test groups would have to be manually tracked.

As a result, it was not practical on a time or technical level to test multiple hypothesis through small-scale experiments. Instead, we had to take a “big bet” approach by focusing on one very well-informed hypothesis, and making the resultant program as modular and flexible as possible should major changes be needed later on.

Given the weight of the decision, I invested ample time in the research and exploration phase. This was also critical to gain buy-in of key stakeholders later on. I started with guided brainstorming sessions with career coaches, Outcomes, leadership, instructors, and current students. This helped me refine program goals, blockers, and potential solutions.

From there, I compiled feedback into three potential solutions, none of which were perfect, but all of which balanced input from stakeholders, goals, and logistical constraints. I presented these three solutions to leadership and stakeholders, facilitating dialogue between groups and collecting feedback. At the end of the feedback session, I made my recommendation for the chosen solution, which aligned with leadership and most stakeholders' recommendation.

Once a solution was agreed upon, the design process began. Using a target launch date in Q4, I planned backwards to create a detailed project plan that outlined every action item, due date, and owner. I worked closely with a curriculum developer on my team to partner on creating curriculum and content, and simultaneously created internal trainings, SOPs, and data collection processes.

We launched the first unit to the first cohort of students while later units were still in draft phase; this allowed us the flexibility to learn as we went and reflect those changes while authoring future units. Incorporating feedback during the authoring progress allowed us to avoid potentially needing to duplicate large amounts of work, which saved valuable time in a project of this scale.

Process

Stage I - early Q3 2020

  • Establish learning objectives and key success indicators

  • Meet individually with stakeholders to get feedback and brainstorm solutions

  • Develop and propose three potential curriculum structures

Stage II - mid Q3 2020

  • Get stakeholder feedback and formally propose agreed-upon curriculum structure

  • Identify support needs and confirm resource availability

  • Set timeline for development and launch date

Stage III - late Q3 - Q4 2020

  • Write curriculum based on learning objectives

  • Collect continuous feedback during curriculum development process and refine

  • Create SOPs outlining support needs

  • Establish data tracking systems/processes

  • Train instructors and support staff to prepare for late Q4 2020 launch

Stage IV - late Q4 2020

  • Launch to first cohorts of students (~100 students in first month)

  • Collect and organize quantitative and qualitative feedback from students and instructors

  • Maintain ongoing documentation for recommended changes

  • Continue writing curriculum for upcoming instructional units

Stage V - Q1 2021

  • Finalize curriculum as students matriculate into final units

  • Assess and communicate results of initial rollout company-wide

  • Continuously implement changes based on ongoing student feedback

My team brainstormed, proposed, authored, launched, and assessed a comprehensive overhaul of the previous career curriculum structure over a span of 9 months. The curriculum updates included prerecorded content, written supplements, lesson plans, and assessments spanning six program units. Support functions, like instructor and student success SOPs, data collection and reporting, and instructor trainings were also required.

Metrics:

  • 293% increase in student participation in career readiness content, with 87% of students reporting the curriculum was very helpful or extremely helpful

  • 100% of students were job search-ready within a week of graduation, vs prior baseline of ~90% job search-ready one month after graduation

  • Standardized student learning experience across technical and career readiness curriculum; streamlined information-sharing among students, reduced usage of 1:1 appointments, and clearer communication to students around expectations.

Results