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Julia L. Ferguson; Amanda M. Rogue; Tracey D. Terhune; Christine M. Milne; Joseph H. Cihon; Maddison J. Majeski-Gerken; Justin B. Leaf; John McEachin; Ronald Leaf – Exceptionality, 2024
This study aimed to extend previous literature comparing continuous methods of data collection to estimation data, but this time implementing the data collection procedures within a group discrete trial teaching format with three individuals diagnosed with autism spectrum disorder. Group discrete trial teaching was conducted in a classroom setting…
Descriptors: Autism Spectrum Disorders, Kindergarten, Elementary School Students, Elementary School Teachers
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Ethan R. Van Norman; Emily R. Forcht – Journal of Education for Students Placed at Risk, 2024
This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…
Descriptors: Data Collection, Predictive Measurement, Predictive Validity, Predictor Variables
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Jaylin Lowe; Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…
Descriptors: Randomized Controlled Trials, Middle School Mathematics, Middle School Students, Middle Schools
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Krumm, Andrew E.; Beattie, Rachel; Takahashi, Sola; D'Angelo, Cynthia; Feng, Mingyu; Cheng, Britte – Journal of Learning Analytics, 2016
This paper outlines the development of practical measures of productive persistence using digital learning system data. Practical measurement refers to data collection and analysis approaches originating from improvement science; productive persistence refers to the combination of academic and social mindsets as well as learning behaviours that…
Descriptors: Measurement, Persistence, Electronic Learning, Data Analysis
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Burton, Laura J.; Mazerolle, Stephanie M. – Athletic Training Education Journal, 2011
Context: Instrument validation is an important facet of survey research methods and athletic trainers must be aware of the important underlying principles. Objective: To discuss the process of survey development and validation, specifically the process of construct validation. Background: Athletic training researchers frequently employ the use of…
Descriptors: Athletics, Research Methodology, Construct Validity, Validity
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Sood, Vishal – Journal on Educational Psychology, 2013
For identifying children with four major kinds of verbal learning disabilities viz. reading disability, speech and language comprehension disability, writing disability and mathematics disability, the present task was undertaken to construct and standardize verbal learning disabilities checklist. This checklist was developed by keeping in view the…
Descriptors: Verbal Learning, Learning Disabilities, Children, Disability Identification
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Anderson-Butcher, Dawn; Amorose, Anthony J.; Lower, Leeann M.; Riley, Allison; Gibson, Allison; Ruch, Donna – Research on Social Work Practice, 2016
Objective: This study examines the psychometric properties of the revised Perceived Social Competence Scale (PSCS), a brief, user-friendly tool used to assess social competence among youth. Method: Confirmatory factor analyses (CFAs) examined the factor structure and invariance of an enhanced scale (PSCS-II), among a sample of 420 youth.…
Descriptors: Interpersonal Competence, Children, Youth, Summer Programs
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Gobert, Janice D.; Sao Pedro, Michael; Raziuddin, Juelaila; Baker, Ryan S. – Journal of the Learning Sciences, 2013
We present a method for assessing science inquiry performance, specifically for the inquiry skill of designing and conducting experiments, using educational data mining on students' log data from online microworlds in the Inq-ITS system (Inquiry Intelligent Tutoring System; www.inq-its.org). In our approach, we use a 2-step process: First we use…
Descriptors: Intelligent Tutoring Systems, Science Education, Inquiry, Science Process Skills
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Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – International Educational Data Mining Society, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
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Tracy, Allison; Charmaraman, Linda; Ceder, Ineke; Richer, Amanda; Surr, Wendy – Afterschool Matters, 2016
Out-of-school time (OST) youth programs are inherently difficult to assess. They are often very dynamic: Many youth interact with one another and with staff members in various physical environments. Despite the challenge, measuring quality is critical to help program directors and policy makers identify where to improve and how to support those…
Descriptors: After School Programs, Program Evaluation, Educational Quality, Youth Programs
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Jayaprakash, Sandeep M.; Moody, Erik W.; Lauría, Eitel J. M.; Regan, James R.; Baron, Joshua D. – Journal of Learning Analytics, 2014
The Open Academic Analytics Initiative (OAAI) is a collaborative, multi-year grant program aimed at researching issues related to the scaling up of learning analytics technologies and solutions across all of higher education. The paper describes the goals and objectives of the OAAI, depicts the process and challenges of collecting, organizing and…
Descriptors: At Risk Students, College Students, Open Source Technology, Data Analysis
Lee, Jooyoung – ProQuest LLC, 2010
The purpose of this study was to determine whether a standardized test of music aptitude developed for American children yields results, which may have valid interpretation when used with 5-year-old Korean children. The specific questions regarding the Primary Measures of Music Audiation (PMMA) norms were: (1) Does PMMA when used with 5-year-old…
Descriptors: Music Education, Music, Standardized Tests, Pretests Posttests
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Blikstein, Paulo; Worsley, Marcelo; Piech, Chris; Sahami, Mehran; Cooper, Steven; Koller, Daphne – Journal of the Learning Sciences, 2014
New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…
Descriptors: Programming, Computer Science Education, Learning Processes, Introductory Courses
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Gbore, L. O. – Journal on School Educational Technology, 2012
This study examined the predictive validity of pre-university examinations test scores (university matriculation examination (UME), Post-UME and pre-degree) for undergraduate academic achievement. The study is planned along the lines of correlational and ex-post-facto research design. A sample of four hundred university science based…
Descriptors: Predictive Validity, College Entrance Examinations, Scores, Science Achievement
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