LEARNING AGENCY LAB: Form 990, tax year 2023

View the entire original 990 at ProPublica ↗

Organization
LEARNING AGENCY LAB (EIN 84-2365060)
Tax period
2023-01-01 to 2023-12-31
Filed
2024-11-14
IRS object ID
202413199349318811
Schema version
2023v5.1
Source
Download XML · Original 990 at ProPublica

Every field in the e-filed return. Faint text is the IRS element name, for citing.

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Return2024-11-14T18:29:47-08:00ReturnTs
Tax Period End2023-12-31TaxPeriodEndDt

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Business Name Line1Iryna ACBusinessNameLine1Txt

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Address Line11000 Broadway Ste 200-CAddressLine1Txt
CityOaklandCityNm
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Tax Period Begin2023-01-01TaxPeriodBeginDt

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Phone2027135854PhoneNum

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Address Line12307 S Rural RoadAddressLine1Txt
CityTempeCityNm
State AbbreviationAZStateAbbreviationCd
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Business Officer BusinessOfficerGrp

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PersonUlrich BoserPersonNm
Person TitleCEOPersonTitleTxt
Signature2024-11-14SignatureDt
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Gross Receipts$1,150,141GrossReceiptsAmt
Group Return For AffiliatesfalseGroupReturnForAffiliatesInd
Organization501c3Yes (X)Organization501c3Ind
Website Addresswww.the-learning-agency-lab.comWebsiteAddressTxt
Type Of Organization CorpYes (X)TypeOfOrganizationCorpInd
Formation2019FormationYr
Legal Domicile StateAZLegalDomicileStateCd
Activity Or MissionThe Learning Agency Lab is focused on developing science of learning-based tools and programs for the social good. We help educators, researchers and others to understand the science of learning and how students can learn how to learn better.ActivityOrMissionDesc
Voting Members Governing Body4VotingMembersGoverningBodyCnt
Voting Members Independent3VotingMembersIndependentCnt
Total Employee7TotalEmployeeCnt
Total Volunteers3TotalVolunteersCnt
Total Gross UBI$0TotalGrossUBIAmt
PY Contributions Grants$1,592,328PYContributionsGrantsAmt
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CY Investment Income$19,926CYInvestmentIncomeAmt
PY Other Revenue$606PYOtherRevenueAmt
CY Other Revenue$0CYOtherRevenueAmt
PY Total Revenue$1,601,616PYTotalRevenueAmt
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CY Benefits Paid To Members$0CYBenefitsPaidToMembersAmt
PY Salaries Comp Emp Bnft Paid$456,800PYSalariesCompEmpBnftPaidAmt
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CY Total Fundraising Expense$0CYTotalFundraisingExpenseAmt
PY Other Expenses$819,816PYOtherExpensesAmt
CY Other Expenses$942,987CYOtherExpensesAmt
PY Total Expenses$1,276,616PYTotalExpensesAmt
CY Total Expenses$1,286,492CYTotalExpensesAmt
PY Revenues Less Expenses$325,000PYRevenuesLessExpensesAmt
CY Revenues Less Expenses-$136,351CYRevenuesLessExpensesAmt
Total Assets BOY$2,225,913TotalAssetsBOYAmt
Total Assets EOY$2,814,970TotalAssetsEOYAmt
Total Liabilities BOY$482,611TotalLiabilitiesBOYAmt
Total Liabilities EOY$1,208,019TotalLiabilitiesEOYAmt
Net Assets Or Fund Balances BOY$1,743,302NetAssetsOrFundBalancesBOYAmt
Net Assets Or Fund Balances EOY$1,606,951NetAssetsOrFundBalancesEOYAmt
MissionThe learning agency lab seeks to improve the effectiveness of k-16 education, especially for historically marginalized populations, by creating free or at-cost resources that are aligned to the science of learning.MissionDesc
Significant New Program SrvcfalseSignificantNewProgramSrvcInd
Significant ChangefalseSignificantChangeInd
Expense$1,128,801ExpenseAmt
Revenue$19,266RevenueAmt
DescIn the 2023 fiscal year, The Learning Agency Lab continued to build momentum in advancing educational technology through data science competitions and community engagement. By September, the Lab successfully launched three major competitions in the Open Data Assessment Fund (ODAF) series, which focused on improving educational outcomes through cutting-edge algorithm development.The first competition, The Quest for Quality Questions: Improving Reading Comprehension through Automated Question Generation, concluded in March 2023. This competition attracted 10 teams, with over 40 participants from around the world. The challenge addressed a critical issue in assessing student reading comprehension by developing models that automatically generate comprehension questions. These models, trained on a dataset of childrens storybooks, demonstrated the ability to produce human-like fluency in question generation, which presents a significant advancement in AI-supported education. By helping educators save time, these tools have the potential to broaden the range of texts students can engage with.In June 2023, the second competition, Predict Student Performance from Game Play, concluded with over 2,000 participants submitting 54,000 entries. Using data from the educational game Jo Wilder and the Capitol Case, this competition focused on advancing knowledge tracing to predict how students learn and perform in educational games. The top-scoring model provided accurate predictions of student success, surpassing human capabilities and paving the way for improved educational game development, targeted interventions, and real-time feedback for educators.The third competition, CommonLit - Evaluate Student Summaries, completed in October 2023. With 2,450 participants and nearly 45,000 entries, it focused on creating models that automatically assess student-written summaries for grades 3-12. The models, developed for both traditional accuracy and computational efficiency, make them suitable for low-resource environments. These models enable teachers to efficiently evaluate students summarization skills, encouraging more frequent use of this important instructional strategy by reducing grading burdens.The Lab also strengthened its engagement with stakeholders by establishing a Teacher Advisory Board, which provided critical feedback for refining competition ideas. The Board consisted of five teachers from a range of grades between K-12 and teach a variety of subjects. Several of the teachers work with low-income students or English Language Learners as well. Their interests, feedback, and ideas drove discussion and goals for many of the data science competitions this year and helped set the stage for next year. For instance, the overwhelming support for an AI detection tool led to a 2024 AI Writing Detection competition. The Lab has also forged strong relationships with vendors to maximize the real-world impact of its work. For example, the algorithms from the Jo Wilder competition were shared with one of the top educational gaming platforms in the U.S., demonstrating the Labs growing influence on the broader educational technology landscape. The Lab hopes to see the algorithm integrated into their future games to provide further insights during gameplay.In alignment with its mission to promote innovation and make data more accessible, the Lab launched the Learning Exchange platform in 2023. This platform hosts datasets and winning algorithms from completed competitions, making them available for public use and encouraging further research and development. Researchers have already begun utilizing the Exchange, with one standout example being a winning team from The Quest for Quality Questions competition whose paper was recognized at the 2023 BAE conference.Beyond its competitions, the Lab continues to foster the learning engineering community through its Learning Engineering Ambassador and Internship programs. The Ambassador Program ran a spring cohort, with one ambassador continuing in the fall. The program engaged students with a diverse range of educational interests, from K-12 learning to adult education. The ambassadors each hosted multiple events, demonstrating the broad scope and relevance of learning engineering. Additionally, the third cohort of the Learning Engineering Internship Program launched in spring 2023, providing undergraduate, graduate, and PhD students the opportunity to work with university labs and education-focused organizations. The interns contributed to a wide range of projects, from research to data visualization and coding, further advancing the Labs mission of empowering the next generation of learning engineers.Overall, the Labs efforts in 2023 significantly advanced AI and data science in education. These competitions and initiatives have produced valuable tools for educators and students alike, while also fostering a growing global community of learning engineers. With plans for future competitions and partnerships, the Lab is positioned for even greater impact in the coming year.Desc
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Total Calendar Year170 TotalCalendarYear170Grp

FieldValueIRS element (for citing)
Current Tax Year Minus4 Years$100,000CurrentTaxYearMinus4YearsAmt
Current Tax Year Minus3 Years$784,207CurrentTaxYearMinus3YearsAmt
Current Tax Year Minus2 Years$2,251,613CurrentTaxYearMinus2YearsAmt
Current Tax Year Minus1 Year$1,592,328CurrentTaxYearMinus1YearAmt
Current Tax Year$1,110,949CurrentTaxYearAmt
Total$5,839,097TotalAmt
FieldValueIRS element (for citing)
Substantial Contributors Tot$0SubstantialContributorsTotAmt
Public Support Total170$5,839,097PublicSupportTotal170Amt

Gross Investment Income170 GrossInvestmentIncome170Grp

FieldValueIRS element (for citing)
Current Tax Year$19,926CurrentTaxYearAmt
Total$19,926TotalAmt

Unrelated Business Net Incm170 UnrelatedBusinessNetIncm170Grp

FieldValueIRS element (for citing)
Total$0TotalAmt

Other Income170 OtherIncome170Grp

FieldValueIRS element (for citing)
Current Tax Year Minus3 Years$288CurrentTaxYearMinus3YearsAmt
Current Tax Year Minus2 Years$213CurrentTaxYearMinus2YearsAmt
Current Tax Year Minus1 Year$606CurrentTaxYearMinus1YearAmt
Total$1,107TotalAmt
FieldValueIRS element (for citing)
Total Support$5,860,130TotalSupportAmt
Gross Receipts Rltd Activities$154,425GrossReceiptsRltdActivitiesAmt
First5 Years170Yes (X)First5Years170Ind
Public Support CY1700.00000PublicSupportCY170Pct

Schedule B IRS990ScheduleB

Contributor Information ContributorInformationGrp

FieldValueIRS element (for citing)
ContributorRESTRICTEDContributorNum

Contributor Business Name ContributorBusinessName

FieldValueIRS element (for citing)
Business Name Line1RESTRICTEDBusinessNameLine1

Contributor US Address ContributorUSAddress

FieldValueIRS element (for citing)
Address Line1RESTRICTEDAddressLine1
Address Line2RESTRICTEDAddressLine2
CityRESTRICTEDCity
StateRESTRICTEDState
ZIP CodeRESTRICTEDZIPCode
FieldValueIRS element (for citing)
Total ContributionsRESTRICTEDTotalContributionsAmt

Schedule O IRS990ScheduleO

Supplemental Information Detail SupplementalInformationDetail (4 entries)

Form And Line ReferenceFormAndLineReferenceDescExplanationExplanationTxt
Form 990, Part VI, Section B, Line 11bIn order to review the Form 990, the Executive Director, Finance and Operations Director, Finance and Operations Manager, and the external bookkeeping consultant all do an initial review of the draft Form 990. After the initial review of Form 990 from the internal staff, then it is distributed to all board members for review prior to filing.
Form 990, Part VI, Section B, Line 12cEach Director, officer, key employee, and member of a committee or subcommittee of the Board with Board-delegated powers shall annually complete and sign the form of the Corporations Annual Conflict of Interest Acknowledgment Statement as may be amended by the Board from time to time.
Form 990, Part VI, Section B, Line 15aThe organization utilized an analysis of similar/like organizations in Guidestar, the largest source of salary information on U.S. nonprofit organizations containing information on every public charity, private foundation, and other 501c3 organizations to which contributions may be tax deductible. The analysis included data on salary minimum/mid/max ranges of similar/like organizations to The Learning Agency Lab. The Learning Agency Lab used this data to create salary bands and considered tenure, number of applicable years of experience and position within the organization to determine equitable pay.
Form 990, Part VI, Section C, Line 19Upon request.

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