Jessica Heppen
Associated IES Content
Grant
Identifying Malleable Factors in Blended Learning Environments Using Automated Detectors of Engagement
The purpose of this project is to use data-mining and machine learning methods to explore the relationship between affective and behavioral engagement with measures of student learning within an online adaptive mathematics learning system. Adaptive learning programs for students generate rich data, offering an important opportunity to identify when students are not learning productively and which system features and implementation factors may be most strongly related to productive learning s...
Federal funding program:
Award number:
R305A170167
Science, Technology, Engineering, and Mathematics FY2021-FY2024
Science, Technology, Engineering, and Mathematics Reviewers FY2021-FY2024
FY2022
FY2022 Science, Technology, Engineering, and Mathematics Education (STEM) Peer Review Panel
FY2021
FY2021 Science, Technology, Engineering, and Mathematics Education (STEM) Peer Review Panel
FY2020
FY2020 Science, Technology, Engineering, and Mathematics Education (STEM) Peer Review Panel
FY2019
FY2019 Science, Technology, Engineering, and Mathematics Education (STEM) Peer Review Panel
FY2018
FY2018 Single-Session Peer Review Panel
report
Impact Study
Getting students on track for graduation: Impacts of the Early Warning Intervention and Monitoring System after one year
Although high school graduation rates are rising--the national rate was 82 percent during the 2013/14 school year (U.S. Department of Education, 2015)--dropping out remains a persistent problem in the Midwest and nationally. Many schools now use early warning systems to identify students who are at risk of not graduating, with the goal of intervening early to help students get back on track for on-time graduation. Although research has guided decisions about the types of data and indicators u...
Apr 01, 2017
report
Descriptive Study
Identifying early warning indicators in three Ohio school districts
In partnership with the Midwest Dropout Prevention Research Alliance the study team used student-level data and a five-step process to identify the most accurate indicators of students' failure to graduate from high school on time. Student-level data came from attendance records, transcripts, and discipline records of grade 8 and 9 students in three Ohio school districts. The study found that the most accurate early warning indicators of students being off track for graduating on time vary by...
Jul 01, 2016
Grant
Assessing the Efficacy of Check & Connect for Improving Outcomes for At-Risk High School Students
Because the dropout problem has persisted in the United States over the previous decades, much work has focused on the development and implementation of dropout prevention programs in and outside of school. A review of the research on these programs suggests that some, but not many, dropout prevention programs have positive effects on school persistence and completion. Check & Connect is a dropout prevention program with demonstrated effectiveness for students with disabilities. The research...
Federal funding program:
Award number:
R305A110252
Grant
Assessing the Efficacy of Online Credit Recovery in Algebra I for At-Risk Ninth Graders
Failing Algebra I in the first year of high school significantly decreases a student's likelihood of graduating. Getting students back on track is a high priority, yet little rigorous evidence exists about credit recovery options. This study will test the efficacy of offering an online Algebra I course in the summer after 9th grade for first time 9th graders who failed the second semester of Algebra I.
Federal funding program:
Award number:
R305A110149
View More