NAEP Process Data Researcher Dr. Ruhan Circi will present on Thursday, November 9, and Saturday, November 11, 2023, at the Association for Public Policy Analysis & Management (APPAM) 2023 Fall Conference in Atlanta, Georgia.
APPAM is an organization focused on improving public policy through the promotion of research, analysis, and education. According to the APPAM website, “The 2023 APPAM Fall Research Conference will be a multi-disciplinary research conference attracting the highest quality research on a wide variety of important current and emerging policy and management issues.” The theme for this year’s conference is “Policy that Matters: Making Public Services Work for All.” Presentations and discussions will focus on the tangible impact of social science theories and empirical research on the policymaking that shapes people’s lives.
In her first presentation, Dr. Circi of the Center for Process Data will discuss a paper she co-authored with Burhan Ogut and Zhuomin Chen, “Who Gets/Use Extra Time: An Examination of Students’ Test-Taking Behaviors,” as part of the panel “Uncovering Sources of Variation in Teacher Instructional Coaching.” The paper examines students’ test-taking behaviors through detailed timing data from NAEP digital assessments, with a focus on students with disabilities and English language learners, who may receive accommodations such as extended time. Students’ pacing on the NAEP assessment is explored through the research questions “What percent of SWD use extended-time accommodation?” “What are the distinct pacing behaviors across major SWD groups and students with no disability?” and “How does pacing behavior differ across student groups?” For further details on attending and to read the abstract, click here.
Dr. Circi will also present a paper she co-authored with Burhan Ogut, Nika Ouyang, and Cassiel Ding, “Looking into Students’ Revisit Patterns: Evidence from NAEP,” at the Saturday Poster Luncheon. This study uses process data from a block of released 2017 eNAEP grade 8 mathematics items to examine exam item revisitation patterns across student groups. In the abstract, the researchers outline their method of analysis: “We observe students’ revisit frequency per item and their revisit time spent on each item utilizing machine learning methods and data mining techniques. We select these revisit-related variables and apply K-means clustering to separate these unlabeled data into a number of clusters. Then, for each cluster, we check variations through percentages. We also use sequence mining techniques to understand the sequence nature of revisits.” For further details on attending and to read the abstract, click here.
Other presentations of potential interest to the NAEP Research and Development community include:
The conference will be held on November 9–11, 2023. You can view an agenda for the event, register to attend, and learn more here.