2023 American Educational Research Association (AERA) Meeting

The Friday Institute will be attending and presenting at the 2023 American Educational Research Association Annual Meeting in Chicago, IL, from April 13–16, 2023. Friday Institute researchers will present their work on learning analytics, informational texts, error analysis and data-based instructional decision making during these in-person sessions.




Time Session Location Presenters
Thu 8:00 a.m. - 9:30 a.m. As the use of digital resources and tools continues to expand in education, the volume and variety of data now available to researchers presents new opportunities for understanding and improving student learning. Learning Analytics (LA) has emerged over the past two decades as an interdisciplinary field to help leverage these new data sources and is defined as the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning. This working group roundtable aims to bring together faculty leading graduate programs in Learning Analytics to discuss student competencies, curriculum and instructional approaches in order to improve existing programs and guide the increasing number of programs in development. Swissôtel Chicago, Floor: Event Centre, 1st Floor, Zurich F
  • Shaun Kellogg
  • Shiyan Jiang
Thu 4:40 p.m. - 6:10 p.m. This paper presents qualitative analysis of survey data collected from 57 U.S. secondary English Language Arts (ELA) teachers regarding their use of informational texts. This study was to extend our understanding of teachers’ considerations of equity and power in their use of informational texts. Thematic analysis noted points of silence and hedging regarding how to define informational text and the role of power and equity. Conversational coherence allowed us to zoom in by tracking continuity or changes across conversational turns to better understand respondents’ meaning. Findings signal the need for instructional supports that clarify the role of informational texts in ELA classrooms and strategies to develop students’ critical, disciplinary literacy skills with informational texts. Fairmont Chicago Millennium Park, Floor: B2 Level, Imperial Ballroom
  • Chandra Alston
  • Sarah Bausell
  • Devan MacKenzie
Sat 11:40 a.m. - 1:10 p.m. Since 2017, we have participated in a replication study of the impact of ASSISTments, a free online learning tool for improving students’ mathematics achievement. As part of that study, we conducted an intrinsic, longitudinal multiple-case study of middle school teachers’ implementation of ASSISTments and its impact on their instruction before and during the COVID-19 pandemic. This portion of the study examined teachers’ use of ASSISTments in error analysis and in determining how to respond instructionally to students’ mistakes. Our findings indicate that ASSISTments changed how teachers understood their students’ performance and helped them engage in error analysis to determine next instructional steps. Hyatt Regency Chicago, Floor: West Tower - Ballroom Level, San Francisco Jamie Gillespie
Sun 8:00 a.m. - 12:00 p.m. Learning Analytics, as a computational research methodology, increases the capacity to understand and improve STEM learning and learning environments through the use of new sources of data and powerful analytical approaches. Learning Analytics is a relatively new, rapidly growing field with significant potential to improve digital learning environments. To address the need for trained researchers on a much broader scale, instructors will focus on Learning Analytics foundations and Text Mining with a STEM education focus. Topics broadly emphasize methodologies, literature, applications, and ethical issues as they relate to STEM education. Participants develop basic proficiency with R and RStudio, apply computational analysis techniques (e.g. data visualization, text mining) relevant and appropriate to their STEM education research interests. The target audience includes those who aim to leverage new data sources and apply computational methods in R Studio following the Learning Analytics workflow. The level of instruction will be appropriate for those with little or no experience using R, a popular free open source software program for data science, research, and technical communication. The first part of the course lays the foundations of Learning Analytics and R programming basics. The second part uses that base to dive into Text Mining techniques (e.g. word counts, sentiment analysis) in a STEM education context. Course activities include conceptual overviews, code-alongs, blended-learning labs, small group discussions, and individual consultations. Knowledge of basic descriptive and exploratory analysis is assumed. Participants are required to bring a laptop computer with R and Rstudio downloaded. Swissôtel Chicago, Floor: Event Centre, 1st Floor, Vevey 1
  • Shaun Kellogg
  • Shiyan Jiang
  • Jennifer Houchins
  • Cansu Tatar
  • Jeanne M. McClure