NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing all 14 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Interactive Learning Environments, 2024
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2)…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Grantee Submission, 2023
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction; and…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
Kelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – Annenberg Institute for School Reform at Brown University, 2021
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…
Descriptors: At Risk Students, Higher Education, Predictive Measurement, Models
Ji-Eun Lee; Amisha Jindal; Sanika Nitin Patki; Ashish Gurung; Reilly Norum; Erin Ottmar – Grantee Submission, 2022
This paper demonstrates how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. We examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance prediction; and (2) what types of in-game features were associated with student…
Descriptors: Teaching Methods, Algorithms, Mathematics Tests, Computer Games
Peer reviewed Peer reviewed
Direct linkDirect link
Schlauch, Robert S.; Carney, Edward – Journal of Speech, Language, and Hearing Research, 2018
Purpose: Computer simulation was used to estimate the statistical properties of searches for maximum word recognition ability (PB max). These involve presenting multiple lists and discarding all scores but that of the 1 list that produced the highest score. The simulations, which model limitations inherent in the precision of word recognition…
Descriptors: Word Recognition, Computer Simulation, Scores, Phonemes
Peer reviewed Peer reviewed
Direct linkDirect link
Strietholt, Rolf; Rosén, Monica; Bos, Wilfried – Large-scale Assessments in Education, 2013
Background: Since the early days of international large-scale assessments, an overarching aim has been to use the world as an educational laboratory so countries can learn from one another and develop educational systems further. Cross-sectional comparisons across countries as well as trend studies derive from the assumption that there are…
Descriptors: Measurement, International Assessment, Foreign Countries, Sampling
Peer reviewed Peer reviewed
Direct linkDirect link
Oketch, Moses; Ngware, Moses; Mutisya, Maurice; Kassahun, Admassu; Abuya, Benta; Musyoka, Peter – Peabody Journal of Education, 2014
In East Africa, there is great effort directed toward ensuring that there is learning and value for money invested in universal education policies initiated over the past decade. Kenya and Uganda are two countries that typify this effort. The effort includes the work of research organisations such as Uwezo, which assess learning levels; RTI, which…
Descriptors: Foreign Countries, Reading Programs, Literacy Education, Numeracy
Peer reviewed Peer reviewed
Direct linkDirect link
Leue, Anja; Lange, Sebastian – Assessment, 2011
The assessment of positive affect (PA) and negative affect (NA) by means of the Positive Affect and Negative Affect Schedule has received a remarkable popularity in the social sciences. Using a meta-analytic tool--namely, reliability generalization (RG)--population reliability scores of both scales have been investigated on the basis of a random…
Descriptors: Social Sciences, True Scores, Generalization, Affective Behavior
Peer reviewed Peer reviewed
Direct linkDirect link
Marsh, Herbert W.; Ludtke, Oliver; Nagengast, Benjamin; Trautwein, Ulrich; Morin, Alexandre J. S.; Abduljabbar, Adel S.; Koller, Olaf – Educational Psychologist, 2012
Classroom context and climate are inherently classroom-level (L2) constructs, but applied researchers sometimes--inappropriately--represent them by student-level (L1) responses in single-level models rather than more appropriate multilevel models. Here we focus on important conceptual issues (distinctions between climate and contextual variables;…
Descriptors: Foreign Countries, Classroom Environment, Educational Research, Research Design
Trentham, John David – ProQuest LLC, 2012
The intent of this study was to explore the variance of epistemological development in pre-ministry undergraduates across different institutional contexts, using the Perry Scheme as a theoretical lens. Semi-structured interviews were employed in order to elicit information from participants that revealed their personal perspectives regarding their…
Descriptors: Theological Education, Undergraduate Students, Semi Structured Interviews, Student Attitudes
Lewis, Charles; Willingham, Warren W. – 1995
As strongly suggested by recent work, patterns of gender difference can change because of changes in the selectivity of the sample itself. This is a statistical influence connected with the distributions of female and male scores, rather than a substantive influence related to demographic characteristics of the sample such as age or ethnicity. It…
Descriptors: Age Differences, Educational Assessment, Models, Sampling
Harmon, Michelle G.; And Others – 1994
The stability of a two-factor model recently proposed for the Gibb Experimental Test of Testwiseness was assessed, using confirmatory factor analysis. Designed to measure seven specific testwiseness skills with 10 items per skill, Gibb's test has been shown to discriminate between persons trained and untrained in selected testwiseness skills. Such…
Descriptors: Factor Structure, Higher Education, Models, Sampling
Brandenburg, Dale C.; Forsyth, Robert A. – 1973
Multiple matrix sampling (MMS) procedures were utilized to determine the necessary parameters of a Pearson Type I curve. Empirical norms distributions were approximated by both the Type I model and the negative hypergeometric model. Four existing ITED norms distributions, two subtests and two grades, were approximated by the MMS procedures. Two…
Descriptors: Comparative Analysis, Matrices, Models, National Norms
Peer reviewed Peer reviewed
Direct linkDirect link
Meyer, Kevin D.; Foster, Jeff L. – International Journal of Testing, 2008
With the increasing globalization of human resources practices, a commensurate increase in demand has occurred for multi-language ("global") personality norms for use in selection and development efforts. The combination of data from multiple translations of a personality assessment into a single norm engenders error from multiple sources. This…
Descriptors: Global Approach, Cultural Differences, Norms, Human Resources