Your students already carry the most valuable data in the neighborhood.
We teach them to be its scientists.
Remembrance Lab is an afterschool STEM program for grades 6 to 8. Students learn the physics of sound, data science and engineering design by recording, archiving and analyzing the memory of their own neighborhood. They finish with a listening wall the whole school can tap with a phone.
Taught as a pre-apprentice track in Washington, New York City and New Jersey. STEM skills with a purpose, started young.
Already serving DC schools through our partner Togetherhood, so your school can book one vetted program on one invoice.
Of hands-on STEM in the full 12-week lab, mapped to NGSS, CSTA and Common Core math.
A neighborhood’s memory is a dataset. The students are its scientists.
Every elder interview is three kinds of STEM data at once: a sound wave (physics), a record with metadata (information science), and a source of patterns students can count and chart (data science).
Here is how we explain it to a sixth grader. Your grandmother remembers what this block looked like before you were born. That memory lives in one place: her. If nobody records it, it is gone.
So you learn how sound turns into numbers a computer can save. You learn how to ask a good question and then stay quiet. You record an elder, label the file so anyone can find it in fifty years, and test whether a computer can type out her words correctly. Then you count what all the stories have in common and draw it as a chart.
“A crime lab, except the evidence is love.”
Students collect samples (recordings), log them so the chain of custody is clean (metadata and consent), run them through instruments (audio software and AI), and present findings to a jury (families and elders).
The school hears a STEM lab. The students build remembrance. Both are true at the same time.
STEM first. Remembrance is the reason.
History is the dataset, not the subject. Every answer below comes with something a student produces that you can put in a file.
Is this STEM or social studies?
Students study sound waves, digital encoding, data cleaning, statistics and engineering design.
Proof: waveform lab, clean dataset, chart, prototypeWhere is the math?
Word error rate (a percentage), frequency counts, mean and median, sample rates and decibels.
Proof: a computed accuracy score for an AI transcriptWhere is the technology?
Digital audio editing, speech-to-text AI, a tagged database, and NFC tags programmed to play audio.
Proof: archive records and a working listening wallIs there AI in it?
Yes, and students test it rather than trust it. They measure how accurately AI transcribes elders’ speech and talk through why it misses.
Proof: a bias finding in their own wordsHow are kids and elders protected?
A signed consent covenant for every recording, guardian co-signature, four consent levels, and transcription run locally so audio never goes to a cloud service.
Proof: signed covenants, nothing public by defaultHow will we know it worked?
A pre and post STEM-identity survey, a scored portfolio, attendance, and a public showcase for families.
Proof: rubric results and survey changeQuestion. Capture. Analyze. Ship.
Four phases of three weeks each. Every week ends with something a student can hold up.
| Week | Phase | STEM focus | What students build |
|---|---|---|---|
| 1 | Question | Memory as data: what counts as a data point | A map of their block with three questions for an elder |
| 2 | Question | Physics of sound: waves, frequency, amplitude, sample rate | Their own voice as a waveform, measured in hertz and decibels |
| 3 | Question | Research design: question protocol, consent, bias | An interview guide and a signed practice consent covenant |
| 4 | Capture | Field recording: mic placement, levels, room noise | A clean two-minute test recording |
| 5 | Capture | Elder sittings, round one | One recorded interview per pair, consent signed |
| 6 | Capture | Information science: metadata, file naming, databases | Each interview logged as an archive record with tags |
| 7 | Analyze | AI testing: speech-to-text and word error rate | An accuracy score for the AI transcript, and why it missed |
| 8 | Analyze | Coding stories into data: themes, counts, mean and median | A shared dataset built from every interview |
| 9 | Analyze | Data visualization in the style of W.E.B. Du Bois | A hand-designed chart of the class’s findings |
| 10 | Ship | Signal processing: noise reduction, EQ, loudness | A two to three minute edited audio segment |
| 11 | Ship | Engineering design: prototype, test, iterate | An NFC or QR listening wall, tested by another class |
| 12 | Ship | Communicating findings | A showcase for families and elders; the archive is deposited |
Every 90-minute session runs the same five blocks.
- Lab open · 10 minSnack and the question of the day.
- Mini-lesson · 15 minOne STEM concept, taught with a real recording or dataset.
- Build time · 45 minPairs rotate roles: Sound Engineer, Data Scientist, Archivist, Producer.
- Teach-back · 15 minOne pair explains what they found, in plain words.
- Lab log · 5 minOne line each: what I measured, what surprised me.
Why it counts as STEM.
The lab hits middle-school science, computer science and math standards with evidence you can file. We align to your district’s adopted frameworks during planning.
| Standard | What it asks | Where it happens |
|---|---|---|
| NGSS MS-PS4-1 | Use math to describe waves; amplitude relates to energy | Week 2 waveform lab; week 10 loudness |
| NGSS MS-PS4-3 | Digital signals encode information more reliably than analog | Weeks 2 and 6: recording, sample rates, archiving |
| NGSS MS-ETS1-1 to 1-4 | Define a design problem, test solutions, iterate | Week 11 listening wall prototype |
| NGSS practices 1, 4, 5, 8 | Ask questions; analyze data; use math and computation; communicate findings | Weeks 1, 7 to 9, 12 |
| CSTA 2-DA-07, 2-DA-08 | Represent data in multiple encodings; collect and clean data with tools | Weeks 6 and 8: audio to text to tagged records |
| CSTA 2-IC-21, 2-IC-23 | Discuss bias in technology; public versus private information | Week 7 AI test; week 3 consent covenant |
| Common Core 6.RP.A.3c | Find a percent of a quantity | Week 7 word error rate |
| Common Core 6.SP.B.4, 6.SP.B.5 | Display data in plots; summarize with mean and median | Weeks 8 and 9 |
Pick the dosage that fits your schedule.
Full Lab
12 weeks · 2 days a week · 90 minutes · 36 contact hours
- All four phases
- NFC listening wall
- Full family showcase
Core Lab
10 weeks · 2 days a week · 75 minutes · 25 contact hours
- The same arc
- A lighter build week
- Family showcase
Sprint Lab
8 weeks · 1 day a week · 2 hours · 16 contact hours
- One elder sitting per pair
- QR listening wall
- A strong first trial
Suggested cohort: 16 to 20 students in grades 6 to 8, working in pairs, with at least one adult for every eight students. Near-peer assistants come from our own apprentice cohorts, so your students learn from teens who did this work last summer. Versions for upper elementary are available on request.
Everything but the room.
- 4 to 6 field audio kits
- NFC tags and a reader phone
- Consent covenants for students, guardians and elders
- The full 12-week curriculum and portfolio rubric
- Elders to interview, alongside each student’s own family
- Staff who complete the background checks and training your district requires
A room, Wi-Fi and a point of contact.
- A classroom two afternoons a week
- 8 to 10 laptops or Chromebooks for 16 to 20 students in pairs
- One staff contact for scheduling
- The room for one showcase evening in the final week
By default, transcription runs locally and no audio goes to a cloud service. We follow your district’s rules on recording students and on AI tools.
Schools, districts and communities.
Schools and districts
Fund it from your afterschool or enrichment budget, a district or foundation grant, or book it through Togetherhood with one invoice.
Community hosts
Libraries, recreation centers, congregations and community groups can host a cohort for the young people they already serve.
Sponsors and funders
Underwrite a cohort for a school that cannot cover it. Your name sits beside the elders and the students who recorded them.
What the school keeps. When the twelve weeks end, your school owns something no other school has: its own neighborhood’s record, made by its own students, with a listening wall in the hallway to prove it.
Robert Azeem Jackson III
A data scientist and TEDx speaker who teaches young people to be scientists of the place they come from. Rob founded Cypher × Remembrance, a data studio in Ward 8 of Washington, DC.
- Gates Millennium Scholar; Georgetown University graduate
- M.S. in Learning Analytics, Teachers College, Columbia University
- TEDx speaker: “The Power of Remembrance”
- Built tap-to-play technology for Google and Unilever
Bring Remembrance Lab to your school
Request a proposal.
Tell us about your students and your schedule. We will send a proposal with dates, dosage and pricing within a week.
Thank you for your response. ✨
Your students already carry the most valuable data in this neighborhood. We teach them to be its scientists.
Request a proposalBook through Togetherhood
Questions? hello@remembranceus.org