2023-11-13T17:00:18-08:00 2022-12-31 204994635 Iryna AC 1000 Broadway Ste 200-C Oakland CA 94607 990 2022-01-01 842365060 The Learning Agency Lab LEAR 2027135854 2307 S Rural Road Tempe AZ 85282 Ulrich Boser CEO 2023-11-13 true Ulrich Boser 999009999 true Kostyantyn Oreshkov EA P00923916 5104679506 2 AAL1 IAL1 FAL1 2022 2023-04-26 12:10:37Z Ulrich Boser 2307 S Rural Road Tempe AZ 85282 1601616 false X www.the-learning-agency-lab.com X 2019 AZ The 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. 4 3 6 3 0 2251613 1592328 36027 8682 0 213 606 2287853 1601616 0 0 324001 456800 0 8351 629771 819816 953772 1276616 1334081 325000 2136148 2225913 717846 482611 1418302 1743302 The 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. false false 1169072 8682 The 2022 fiscal year focused primarily on the development of new research and data competitions to drive open-source educational interventions. The Lab completed three high-profile data science competitions which developed automated feedback tools to improve writing in students in middle and high school grades, resulting in global participation from over 3,000 teams and resulting algorithms demonstrating human-level accuracy in evaluating student writing. Additionally, the team identified and laid the groundwork for a series of competitions focused around the release of assessment focused datasets called the Open Data Assessment Fund. The automated feedback tools competitions, also known as The Feedback Prize, consisted of three contests each building on the goals of the previous. The first competition, launched in December 2021, asked teams to develop algorithms that can identify elements in student writing for grades 6 to 12. More specifically, teams developed models that could segment an essay into elements of an argument (e.g., claim, evidence) and label each one. The second, launched in May 2022, built on its predecessor by tasking participants with evaluating argumentative elements in essays written by students in grades 6-12 as effective, adequate, or ineffective. Finally, the third, launched in September 2022, tasked participants with developing models that score essays based on language proficiency. Together, the algorithms developed in this competition series help students receive more individualized feedback on their writing, and with automated guidance, students can complete more assignments and ultimately become more confident, proficient writers. Over 3,000 teams participated across the Feedback Prize competitions, with winning algorithms demonstrating human-comparable accuracy and several novel algorithms and techniques emerging. Participants dedicated numerous hours to sharing their algorithmic approaches and engaging in reviews of others solutions on the platforms discussion forums. In the Feedback Prize data science competitions, it is conservatively estimated that competition participants invested time worth more than $240 million across all three competitions, to win a combined prize purse of $270,000. The resulting Feedback Prize datasets were also featured in prestigious academic journals such as Assessing Writing. Building on the success of the Feedback Prize, the Lab sought ways to continue research and innovation on school data. The resulting program was the Open Data Assessment Fund (ODAF), a series of competitions designed to respond to the current lack of high-quality, open source assessment datasets in education. The goal of ODAF is to provide innovators and researchers access to these datasets in order to develop new solutions (e.g., artificial intelligence and machine learning) that can reduce the cost and time to develop and administer assessments. The first competition in this series launch in FY21 and was titled The Quest. The Quest dataset explored the role of automatic question generation in supporting reading assessment for students in kindergarten through eighth grade. By leveraging this dataset, teams were tasked with creating models that will support the use of question items for improving reading comprehension, especially for texts that lack associated resources. Final models will pave the way for algorithms that can support automatic question generation for readers of all levels. While promoting further algorithmic research and development, the Lab has also advocated for the growth of the learning engineering community as a whole with both The Learning Engineering Ambassador Program and The Learning Engineering Internship Program. The Learning Engineering Ambassador Programs second cohort ran from August to December 2022 with nine ambassadors. Due to interest from applicants outside of the student population, both undergraduate and graduate, the program was expanded to allow early career professionals and individuals new to LE to participate as ambassadors. By expanding the parameters on participation, the Lab saw a huge influx of rich ideas for events and collaboration. Each ambassador is tasked with running three events during the program, which works out to one event per month, but many have gone above and beyond with multiple monthly events. These events range in size and scope from pizza parties with undergraduate students focused on LE-related career opportunities, to teacher training sessions that demonstrate how to collect and use data in the classroom to improve learning outcomes. The Learning Engineering Internship Programs second cohort also launched in fall of 2022 with five interns. The interns were students undergraduate, graduate, and PhD candidates or recent graduates, and they were matched with five ed tech-focused educational organizations. Interns have been working on a broad range of activities, including research, data preparation and visualization, and coding. 1169072 true true false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false false true 16 0 true 6 true false false false false false false false 0 false false false false false false false X 4 3 false false false false false false false true true false false true true true true false false true true false AZ X Ulrich Boser 2027135854 2307 S Rural Road Tempe AZ 85282 Ulrich Boser CEO 20.00 0.00 X X 105310 0 17213 Perpetual Baffour Data Scientist 40.00 0.00 100000 0 8332 Kenneth Stern President 1.00 0.00 X X 0 0 0 David Domenici Treasurer 1.00 0.00 X X 0 0 0 Gabriela Lopez Director 1.00 0.00 X 0 0 0 205310 25545 1 false false false Kaggle Inc 1600 Amphitheatre Parkway Mountain View CA 94043 Contract Services 270000 Google LLC 1600 Amphitheatre Parkway Mountain View CA 94043 hosting services 122290 2 1592328 1592328 Online Courses 611710 134 134 Program Service Contracts 611710 8548 8548 8682 0 0 0 0 0 0 0 0 Rebates And Redemptions 606 606 606 1601616 8682 606 X 0 0 0 0 122524 110271 6126 6127 0 288566 276049 11072 1445 12176 11637 501 38 464 464 33070 31144 1308 618 0 3131 2921 210 25200 25200 0 0 0 763401 713022 50379 0 168 116 52 11538 8950 2575 13 0 0 3467 3099 368 0 3329 3329 0 0 0 5165 4734 399 32 Dues and other charges 4349 3336 935 78 Miscellaneous Expense 68 68 0 1276616 1169072 99193 8351 1954451 2165177 0 0 179976 60081 0 0 0 0 1721 655 0 0 0 0 0 0 2136148 2225913 62829 87101 655017 395510 717846 482611 X 1116381 441075 301921 1302227 1418302 1743302 2136148 2225913 325000 X false false false X 100000 784207 2251613 1592328 4728148 0 0 100000 784207 2251613 1592328 4728148 5415 4722733 0 0 288 213 606 1107 4729255 135162 X 0.00000 RESTRICTED RESTRICTED RESTRICTED RESTRICTED RESTRICTED RESTRICTED RESTRICTED RESTRICTED Form 990, Part VI, Section B, Line 11b In 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 12c Each Director, officer, key employee, and member of a committee or subcommittee of the Board with Board-delegated powers shall conduct him or herself in a manner consistent with the Corporations Conflict of Interest Policy as may be amended by the Board from time to time.Each 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 15a The 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 19 Upon request.