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Nazanin Nezami; Parian Haghighat; Denisa Gándara; Hadis Anahideh – Grantee Submission, 2024
The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model…
Descriptors: Evaluation Methods, College Students, Success, Predictor Variables
Carrie Klein; Jessica Colorado – State Higher Education Executive Officers, 2024
Since 2010, the State Higher Education Executive Officers Association's (SHEEO) Strong Foundations survey has reported on the evolution and value of postsecondary student unit record systems (PSURSs) by illuminating the condition of state postsecondary data in the U.S. In the "Strong Foundations 2023" survey, which was administered from…
Descriptors: College Students, Student Records, Data Collection, Databases
Ford, Karly S.; Rosinger, Kelly; Choi, Junghee – Policy Futures in Education, 2022
Policy researchers have difficulty understanding stratification in enrollment in US higher education when race and ethnicity data are plagued by missing values. Students who decline to ethnoracially self-identify become part of a "race unknown" reporting category. In undergraduate enrollment, "race unknown" students are not…
Descriptors: Admission (School), Competitive Selection, Race, Ethnicity
Díaz, Victoria E.; McKeown, Stephanie; Peña, Camilo – British Columbia Council on Admissions and Transfer, 2023
This project reviews data collection practices regarding race, ethnicity and ancestry (REA) in post-secondary institutions (PSIs) in Canada, as well as in other relevant sectors (e.g., health, K-12 education, government agencies). The goal of the project was to identify promising practices and to develop recommendations to guide REA data…
Descriptors: Data Collection, Data Use, Student Characteristics, Race
Galles, Elyse; Gannon, Jamie; Noniyeva, Yuliana; Schweikert, James; Downs, Nancy – Journal of American College Health, 2023
Objective: College students who receive an acute care visit (ACV) from an emergency or inpatient unit require mental health follow-up (MHF) to improve long-term outcomes. This study describes tracking ACVs and MHF, while identifying characteristics of multiple vs. single ACVs. Participants: 191 students who received an ACV (N = 231) at one public…
Descriptors: College Students, Mental Health, Hospitals, Access to Health Care
Bhavik Anil Patel – Journal of Chemical Education, 2022
Accuracy and precision are measures of experimental error and are fundamental to most chemical analysis laboratory classes. Assessment of accuracy and precision is often based on the comprehension of the results generated by students rather than on the quality of the data generated. This activity focused on developing a chemical analysis…
Descriptors: Chemistry, Science Laboratories, Accuracy, Data
Casey Gogno; Scott Burden; Wyntre Stout – Association for Institutional Research, 2024
Creating a welcoming community is key for an academic environment to thrive. This approach includes accurately representing community members' identities to understand their experiences, and establishing procedures for recording and utilizing individuals' names to support their ability to express their identities freely and without fear of…
Descriptors: Data Collection, Information Storage, Student Characteristics, Identification
Pretlow, Josh; Dunlop Velez, Erin; Roberson, Amanda Janice – Institute for Higher Education Policy, 2021
We cannot continue to ask students -- and their families -- to make one of the largest and most important investments of their lives without clearer information about what their time and money will yield. In partnership with RTI International (RTI), operating in an independent capacity, IHEP is gathering expert insights needed to support making…
Descriptors: College Students, Data, Information Networks, Federal Programs
Isaac, James; Pretlow, Josh; Cheng, Diane; Roberson, Amanda Janice – Institute for Higher Education Policy, 2022
We cannot continue to ask students -- and their families -- to make one of the largest and most important investments of their lives without clearer information about what their time and money will yield. Fortunately, support is broad across the country and across the political spectrum for a federal student-level data network (SLDN), which would…
Descriptors: College Students, Information Networks, Federal Programs, Higher Education
Perez-Vergara, Kelly – Strategic Enrollment Management Quarterly, 2020
Institutional staff such as enrollment managers, business officers, and institutional researchers are often asked to predict enrollments. Developing any predictive model can be intimidating, particularly when there is no textbook to follow. This paper provides a practical framework for generating enrollment projection options and for evaluating…
Descriptors: Enrollment Projections, Enrollment Management, Enrollment Trends, Models
Ping Zhao; Chunling Sun; Baojun Lv; Lan Guo; Jiansheng Gao; Xin Zhao; Fengming Jiao – International Journal of Information and Communication Technology Education, 2024
This paper discusses the application value of the writing teaching mode combined with the mixed teaching mode in college English writing teaching against the background of big data. Focusing on production-oriented approach (POA) theory, this paper proposes a mixed learning writing model for English teaching and applies the POA mixed learning…
Descriptors: Writing Instruction, Blended Learning, Data Analysis, Data Collection
Cannistrà, Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics
So, Joseph Chi-ho; Wong, Adam Ka-lok; Tsang, Kia Ho-yin; Chan, Ada Pui-ling; Wong, Simon Chi-wang; Chan, Henry C. B. – Journal of Technology and Science Education, 2023
The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic…
Descriptors: Pattern Recognition, Artificial Intelligence, Higher Education, College Students
Masango, Mxolisi; Muloiwa, Takalani; Wagner, Fezile; Pinheiro, Gabriela – Journal of Student Affairs in Africa, 2020
Knowing relevant information about students entering the higher education (HE) system is becoming increasingly important, thus enabling higher education institutions (HEIs) to design effective studentcentred support programmes. Therefore, HEIs should ascertain all relevant information about their students before the commencement of the academic…
Descriptors: Test Construction, Test Use, Biographical Inventories, Questionnaires
Ruiperez-Valiente, Jose A.; Munoz-Merino, Pedro J.; Alexandron, Giora; Pritchard, David E. – IEEE Transactions on Learning Technologies, 2019
One of the reported methods of cheating in online environments in the literature is CAMEO (Copying Answers using Multiple Existences Online), where harvesting accounts are used to obtain correct answers that are later submitted in the master account which gives the student credit to obtain a certificate. In previous research, we developed an…
Descriptors: Computer Assisted Testing, Tests, Online Courses, Identification