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Sijia Huang; Seungwon Chung; Carl F. Falk – Journal of Educational Measurement, 2024
In this study, we introduced a cross-classified multidimensional nominal response model (CC-MNRM) to account for various response styles (RS) in the presence of cross-classified data. The proposed model allows slopes to vary across items and can explore impacts of observed covariates on latent constructs. We applied a recently developed variant of…
Descriptors: Response Style (Tests), Classification, Data, Models
Ajao, Adeola; Fitzallen, Noleine; Chick, Helen; Oates, Greg – Mathematics Education Research Group of Australasia, 2023
In this paper, the SOLO taxonomy is used to identify different levels of student understanding of the statistical concepts associated with sampling distribution. This study was part of a research project investigating students' conceptual understanding of concepts of hypothesis testing taught with the support of simulation learning activities. The…
Descriptors: Taxonomy, Statistics Education, Learning Activities, Simulation
Bradley, Alex; Quigley, Martyn – Studies in Higher Education, 2023
The mass participation in higher education has led to greater spending by governments and students which has increased the focus on graduate outcomes. In England, the Office for Students (OfS) is planning to take regulatory action, using the Proceed metric, against universities and their courses which do not have 60% of students with positive…
Descriptors: Foreign Countries, Higher Education, Education Work Relationship, Outcomes of Education
Best, Ryan M.; Goldstone, Robert L. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
Categorical perception (CP) effects manifest as faster or more accurate discrimination between objects that come from different categories compared with objects that come from the same category, controlling for the physical differences between the objects. The most popular explanations of CP effects have relied on perceptual warping causing…
Descriptors: Bias, Comparative Analysis, Models, College Students
Kollars, Nina A.; Rosen, Amanda M. – Journal of Political Science Education, 2013
Assessment through simulation is something that political science pedagogy has yet to explore in a robust manner. This article advances analysis of social science simulation and assessment by laying out a typology of active-learning activities that isolates and examines their potential for assessment. In short, we argue that there are essentially…
Descriptors: Simulation, Student Evaluation, Political Science, Active Learning
Perry, Conrad; Ziegler, Johannes C.; Zorzi, Marco – Cognitive Science, 2013
It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the…
Descriptors: English, Graphemes, Reading Processes, Cognitive Mapping
Redondo Duarte, Sara; Learreta Ramos, Begoña; Ruiz Rosillo, María Auxiliadora; Alperstedt, Cristiane; Hazé, Emmanuël – International Journal of Pedagogies and Learning, 2015
The main objective of this article is to present the results of a study aimed at determining, classifying and evaluating practices of interest for general competency development and assessment in undergraduate programmes. The study encompassed the following phases: (1) focus group in order to establish a starting point regarding competency…
Descriptors: Foreign Countries, Undergraduate Study, Undergraduate Students, Student Evaluation
Badurdeen, Fazleena; Marksberry, Philip; Hall, Arlie; Gregory, Bob – Simulation & Gaming, 2010
Problem-based learning focuses on small groups using authentic problems as a means to help participants obtain knowledge and problem-solving skills. This approach makes problem-based learning ideal for teaching lean manufacturing, which is driven by a culture of problem solving that values learning as one key output of manufacturing production.…
Descriptors: Manufacturing Industry, Problem Based Learning, Problem Solving, Literature Reviews
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection