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Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology
Chen, Yu; Upah, Sylvester – Journal of College Student Retention: Research, Theory & Practice, 2020
Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success.…
Descriptors: STEM Education, Academic Advising, Data Analysis, Majors (Students)
University of Chicago Consortium on Chicago School Research, 2014
Districts now have access to a wealth of new information that can help target students with appropriate supports and bring focus and coherence to college readiness efforts. However, the abundance of data has brought its own challenges. Schools and school systems are often overwhelmed with the amount of data available. The capacity of districts to…
Descriptors: College Readiness, Educational Indicators, College Preparation, School Districts
Snyder, Thomas D.; Dillow, Sally A. – National Center for Education Statistics, 2013
The 2012 edition of the "Digest of Education Statistics" is the 48th in a series of publications initiated in 1962. The "Digest" has been issued annually except for combined editions for the years 1977-78, 1983-84, and 1985-86. Its primary purpose is to provide a compilation of statistical information covering the broad field…
Descriptors: School Statistics, Definitions, Tables (Data), Longitudinal Studies
Snyder, Thomas D.; Dillow, Sally A. – National Center for Education Statistics, 2012
The 2011 edition of the "Digest of Education Statistics" is the 47th in a series of publications initiated in 1962. The "Digest" has been issued annually except for combined editions for the years 1977-78, 1983-84, and 1985-86. Its primary purpose is to provide a compilation of statistical information covering the broad field…
Descriptors: Educational Research, Data Collection, Data Analysis, Error Patterns
Varela, Otmar E.; Cater, John James, III; Michel, Norbert – Human Resource Development Quarterly, 2011
This study tests a process model of learning in which trainer and trainee traits are simultaneously considered as endogenous variables of learning outcomes. The article builds on a social view of training and similarity-attraction paradigms. In this context, the authors hypothesize that trainer-trainee similarity in personality (agreeableness)…
Descriptors: Evidence, Undergraduate Students, Personality Traits, Interpersonal Attraction

Ferguson, Richard L. – NASSP Bulletin, 1976
Declining test scores have been a major cause for concern in the past year. Describes the decline and explores some possible causes. (Editor/RK)
Descriptors: Achievement Tests, Data Analysis, Data Collection, Educational Assessment
Carlson, James E.; Spray, Judith A. – 1986
This paper discussed methods currently under study for use with multiple-response data. Besides using Bonferroni inequality methods to control type one error rate over a set of inferences involving multiple response data, a recently proposed methodology of plotting the p-values resulting from multiple significance tests was explored. Proficiency…
Descriptors: Cutting Scores, Data Analysis, Difficulty Level, Error of Measurement
Maloney, Catherine; Sheehan, Daniel; Rainey, Katie – Texas Center for Educational Research, 2010
In 2007, the Texas Legislature (80th Texas Legislature, Regular Session, 2007) authorized the creation of the Texas Rural Technology (R-Tech) Pilot program, which provides $8 million in funding to support rural districts in implementing technology-based supplemental education programs. In order to be eligible for funding, districts must have…
Descriptors: Online Surveys, Teacher Surveys, Student Surveys, Facilitators (Individuals)