NY Staffing
Postdoctoral Associate - National Tutoring Observatory, Information Science
NY Staffing, Ithaca, New York, United States, 14850
Postdoctoral Associate - National Tutoring Observatory
The National Tutoring Observatory (NTO) will be centered at Cornell's Ann S. Bowers College of Computing and Information Science under the direction of Principal Investigator, Rene Kizilcec. The NTO will work closely with providers of tutoring services, school leaders, districts, and social scientists to create the world's largest open-access dataset of tutoring (the Million Tutoring Moves [MTM] dataset) and open-source libraries to facilitate processing and analysis grounded in principles of Responsible AI. In addition, the NTO will be tasked with creating open data and computational resources for education researchers and developers. The postdoctoral associate will participate in a cross-disciplinary research team comprised of postdoctoral associates, PhD students, and several Master's or undergraduate students across multiple universities and organizations. The research team will work alongside the engineering team to investigate the validity of components of the data processing workflow and effective tutoring moves using the MTM dataset; and to share innovations and best practices for investigating tutoring data and effective tutoring moves. Success will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and engaging education researchers and EdTech product developers through a mini-grants program. The postdoc will be expected to make meaningful contributions to these activities. The NTO seeks applications for a postdoctoral associate who can start as early as January 2025. We will review applications on an ongoing basis and consider candidates for a later start date up to July 2025. Selected postdocs will work with their primary mentor on research at the intersection of educational data science, AI in Education, and the learning sciences, with additional advisory support from faculty and researchers in the learning sciences, computer science, machine learning, and education research. Research themes identified for the NTO postdoc include, but are not limited to, the following: Developing or evaluating methodologies for cleaning, annotating, and privacy-protecting student tutoring data to promote actionable insights at scale from extant tutoring data Experimental/quasi-experimental design and analysis of effective tutoring moves that promote student motivation, engagement, and learning Development and evaluation of approaches to AI tutoring agents that build on high-quality data (e.g., training tutors entirely on tutoring data, or exploring a wide range of approaches to fine tuning base LLMs) Other relevant themes, as proposed by the applicant Mentors include Rene Kizilcec (primary mentor; Cornell University), Ken Koedinger (Carnegie Mellon University), and Justin Reich (Massachusetts Institute of Technology). Additionally, the postdoc will receive mentoring from members of the research team and a Scientific Advisory Board of experts in the field. Postdocs are expected to fully integrate with the NTO's core research team, and to actively contribute to the technical development and research and dissemination efforts of the National Tutoring Observatory. The postdoc is expected to serve as a domain expert in one of the research themes outlined above. This will involve participating in research and cross-team meetings as needed, engage in design sessions with developers and tutoring providers, and sessions with technical advisory board members, publishing in high-impact scientific journals, and attending conferences. Attendance at the annual NTO in-person team convening is also expected. Postdoc research projects are not intended to be seed grants for projects unrelated to the work of the National Tutoring Observatory. We aim to support postdoc research for projects that are directly linked to the work of the NTO or closely related work that can be easily translated to the work of the NTO. Further, this program is meant to produce use-inspired basic and applied research with a strong AI component, driven by real-world education applications. Faculty mentors are expected to sufficiently engage with their postdoc mentee, providing training and learning opportunities, as well as career guidance. Mentors must develop an Individual Development Plan with their selected fellow, and update the plan as needed. This is a hybrid position with a possibility of working remotely. Some travel may be required for this role. Application process: Postdoctoral applicants will work under the guidance of Cornell faculty member Rene Kizilcec and senior members of the NTO. Applicants will draft a proposal that includes a clear description of the topic, the link to tutoring data infrastructure and student outcomes, and advancements to the field of learning/information science, including the development of AI agents. Applications will be evaluated on (i) intellectual merit and scientific excellence, (ii) AI for tutoring potential, (iii) cross disciplinary connections, (iv) broader societal impact, and (v) diversity, equity, and inclusion experiences and vision. Applications will be evaluated by a review panel. A short list of applicants will be interviewed via Zoom. Applications are due on a rolling basis and will remain open until the position is filled. The intended start date is projected as Spring 2025. Requirements and qualifications: A major goal for the postdocs is to generate scientific research that probes and enhances the quality of the MTM dataset and components of the underlying data pipeline, and publishes these insights and applications using the MTM dataset. Successful candidates must exhibit excellence in their scientific domain, and show evidence of strong quantitative skills. While it is not required that the candidate possess a deep theoretical understanding of AI, practical proficiency in using AI will be considered in the selection process. Applicants must hold a PhD prior to their start date. Support and benefits: Selected postdocs will be appointed as endowed employees at Cornell University. Application materials: Candidates must hold a Ph.D. or equivalent degree by August 2025. Candidates should submit a cover letter, a curriculum vitae (CV), a research statement, and the names and email addresses for three references who will submit letters on the candidate's behalf. The cover letter and research statement should highlight the candidate's most significant work related to the mission of the National Tutoring Observatory (NTO). We also ask applicants to share their experiences and/or approaches (past, current, or future) to fostering learning, research, service, and/or outreach in a diverse community. Applicants may choose to submit a stand-alone statement or embed the information in other parts of their application materials. The above application materials should be submitted online at . We will be reviewing applications (including reference letters) on a rolling basis, and applications will be accepted until the search has concluded.
The National Tutoring Observatory (NTO) will be centered at Cornell's Ann S. Bowers College of Computing and Information Science under the direction of Principal Investigator, Rene Kizilcec. The NTO will work closely with providers of tutoring services, school leaders, districts, and social scientists to create the world's largest open-access dataset of tutoring (the Million Tutoring Moves [MTM] dataset) and open-source libraries to facilitate processing and analysis grounded in principles of Responsible AI. In addition, the NTO will be tasked with creating open data and computational resources for education researchers and developers. The postdoctoral associate will participate in a cross-disciplinary research team comprised of postdoctoral associates, PhD students, and several Master's or undergraduate students across multiple universities and organizations. The research team will work alongside the engineering team to investigate the validity of components of the data processing workflow and effective tutoring moves using the MTM dataset; and to share innovations and best practices for investigating tutoring data and effective tutoring moves. Success will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and engaging education researchers and EdTech product developers through a mini-grants program. The postdoc will be expected to make meaningful contributions to these activities. The NTO seeks applications for a postdoctoral associate who can start as early as January 2025. We will review applications on an ongoing basis and consider candidates for a later start date up to July 2025. Selected postdocs will work with their primary mentor on research at the intersection of educational data science, AI in Education, and the learning sciences, with additional advisory support from faculty and researchers in the learning sciences, computer science, machine learning, and education research. Research themes identified for the NTO postdoc include, but are not limited to, the following: Developing or evaluating methodologies for cleaning, annotating, and privacy-protecting student tutoring data to promote actionable insights at scale from extant tutoring data Experimental/quasi-experimental design and analysis of effective tutoring moves that promote student motivation, engagement, and learning Development and evaluation of approaches to AI tutoring agents that build on high-quality data (e.g., training tutors entirely on tutoring data, or exploring a wide range of approaches to fine tuning base LLMs) Other relevant themes, as proposed by the applicant Mentors include Rene Kizilcec (primary mentor; Cornell University), Ken Koedinger (Carnegie Mellon University), and Justin Reich (Massachusetts Institute of Technology). Additionally, the postdoc will receive mentoring from members of the research team and a Scientific Advisory Board of experts in the field. Postdocs are expected to fully integrate with the NTO's core research team, and to actively contribute to the technical development and research and dissemination efforts of the National Tutoring Observatory. The postdoc is expected to serve as a domain expert in one of the research themes outlined above. This will involve participating in research and cross-team meetings as needed, engage in design sessions with developers and tutoring providers, and sessions with technical advisory board members, publishing in high-impact scientific journals, and attending conferences. Attendance at the annual NTO in-person team convening is also expected. Postdoc research projects are not intended to be seed grants for projects unrelated to the work of the National Tutoring Observatory. We aim to support postdoc research for projects that are directly linked to the work of the NTO or closely related work that can be easily translated to the work of the NTO. Further, this program is meant to produce use-inspired basic and applied research with a strong AI component, driven by real-world education applications. Faculty mentors are expected to sufficiently engage with their postdoc mentee, providing training and learning opportunities, as well as career guidance. Mentors must develop an Individual Development Plan with their selected fellow, and update the plan as needed. This is a hybrid position with a possibility of working remotely. Some travel may be required for this role. Application process: Postdoctoral applicants will work under the guidance of Cornell faculty member Rene Kizilcec and senior members of the NTO. Applicants will draft a proposal that includes a clear description of the topic, the link to tutoring data infrastructure and student outcomes, and advancements to the field of learning/information science, including the development of AI agents. Applications will be evaluated on (i) intellectual merit and scientific excellence, (ii) AI for tutoring potential, (iii) cross disciplinary connections, (iv) broader societal impact, and (v) diversity, equity, and inclusion experiences and vision. Applications will be evaluated by a review panel. A short list of applicants will be interviewed via Zoom. Applications are due on a rolling basis and will remain open until the position is filled. The intended start date is projected as Spring 2025. Requirements and qualifications: A major goal for the postdocs is to generate scientific research that probes and enhances the quality of the MTM dataset and components of the underlying data pipeline, and publishes these insights and applications using the MTM dataset. Successful candidates must exhibit excellence in their scientific domain, and show evidence of strong quantitative skills. While it is not required that the candidate possess a deep theoretical understanding of AI, practical proficiency in using AI will be considered in the selection process. Applicants must hold a PhD prior to their start date. Support and benefits: Selected postdocs will be appointed as endowed employees at Cornell University. Application materials: Candidates must hold a Ph.D. or equivalent degree by August 2025. Candidates should submit a cover letter, a curriculum vitae (CV), a research statement, and the names and email addresses for three references who will submit letters on the candidate's behalf. The cover letter and research statement should highlight the candidate's most significant work related to the mission of the National Tutoring Observatory (NTO). We also ask applicants to share their experiences and/or approaches (past, current, or future) to fostering learning, research, service, and/or outreach in a diverse community. Applicants may choose to submit a stand-alone statement or embed the information in other parts of their application materials. The above application materials should be submitted online at . We will be reviewing applications (including reference letters) on a rolling basis, and applications will be accepted until the search has concluded.