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Lauren A. Mason; Abigail Miller; Gregory Hughes; Holly A. Taylor – Cognitive Research: Principles and Implications, 2025
False alarming, or detecting an error when there is not one, is a pervasive problem across numerous industries. The present study investigated the role of elaboration, or additional information about non-error differences in complex visual displays, for mitigating false error responding. In Experiment 1, learners studied errors and non-error…
Descriptors: Error Correction, Error Patterns, Evaluation Methods, Visual Aids
Melina Verger; Chunyang Fan; Sébastien Lallé; François Bouchet; Vanda Luengo – Journal of Educational Data Mining, 2024
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term…
Descriptors: Algorithms, Prediction, Bias, Classification
Mohd Fazil; Angelica Rísquez; Claire Halpin – Journal of Learning Analytics, 2024
Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…
Descriptors: Learning Analytics, Learning Management Systems, Learning Processes, Decision Making
Wonkyung Choi; Jun Jo; Geraldine Torrisi-Steele – International Journal of Adult Education and Technology, 2024
Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of…
Descriptors: College Students, Data Analysis, Data Collection, Learning Analytics
Blagg, Kristin; Blom, Erica; Kelchen, Robert; Chien, Carina – Urban Institute, 2021
Policymakers have expressed increased interest in program-level higher education accountability measures as a supplement to, or in place of, institution-level metrics. But it is unclear what these measures should look like. In this report, we assess the ways program-level data could be developed to facilitate federal accountability. Evidence shows…
Descriptors: Higher Education, Accountability, Program Evaluation, Evaluation Methods
Lu, Dang-Nhac; Le, Hong-Quang; Vu, Tuan-Ha – Education Sciences, 2020
The COVID-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online…
Descriptors: Electronic Learning, Adoption (Ideas), Higher Education, Evaluation Methods
Siebrase, Benjamin – ProQuest LLC, 2018
Multilayer perceptron neural networks, Gaussian naive Bayes, and logistic regression classifiers were compared when used to make early predictions regarding one-year college student persistence. Two iterations of each model were built, utilizing a grid search process within 10-fold cross-validation in order to tune model parameters for optimal…
Descriptors: Classification, College Students, Academic Persistence, Bayesian Statistics
Musthofa, Tulus – Eurasian Journal of Applied Linguistics, 2022
Common European Framework of Reference (CEFR) is an international standard to measure learners' language abilities on a six-point scale, A1 for beginners up to C2 for those who have mastered a language. this study attempted to examine the implementation of the CEFR policy in learning Arabic in Indonesia at al levels, beginning form curriculum…
Descriptors: Arabic, Semitic Languages, Second Language Learning, Second Language Instruction
Karimi, Hamid; Derr, Tyler; Huang, Jiangtao; Tang, Jiliang – International Educational Data Mining Society, 2020
Online learning has attracted a large number of participants and is increasingly becoming very popular. However, the completion rates for online learning are notoriously low. Further, unlike traditional education systems, teachers, if any, are unable to comprehensively evaluate the learning gain of each student through the online learning…
Descriptors: Online Courses, Academic Achievement, Prediction, Teaching Methods
Limones Meráz, Tomás Francisco; Amador, Julieta Flores; Reaiche, Carmen – Industry and Higher Education, 2021
To keep up with rapid evolutions in technical and scientific developments, countries must create competitive dynamics that enable key actors to generate high-tech projects, boosting both a country's productivity and economic development. Higher education institutions (HEIs), with their intellectual capital and as core generators of knowledge, are…
Descriptors: School Business Relationship, Foreign Countries, Competition, Technological Advancement
Scholes, Vanessa – Educational Technology Research and Development, 2016
There are good reasons for higher education institutions to use learning analytics to risk-screen students. Institutions can use learning analytics to better predict which students are at greater risk of dropping out or failing, and use the statistics to treat "risky" students differently. This paper analyses this practice using…
Descriptors: Data Collection, Data Analysis, Educational Research, At Risk Students
Hughes, John; Petscher, Yaacov – Regional Educational Laboratory Southeast, 2016
The high rate of students taking developmental education courses suggests that many students graduate from high school unready to meet college expectations. A college readiness screener can help colleges and school districts better identify students who are not ready for college credit courses. The primary audience for this guide is leaders and…
Descriptors: College Readiness, Screening Tests, Test Construction, Predictor Variables
Kaider, Friederika; Hains-Wesson, Rachael; Young, Karen – Asia-Pacific Journal of Cooperative Education, 2017
Increased graduate employment is an aspiration of Australian universities who are adopting new and innovative approaches to ensure that all students have opportunities to develop the employability skills much sought after by employers. Placements have been a traditional means for developing such skills but because these are not available to all…
Descriptors: Learning Activities, Integrated Activities, Workplace Learning, Classification
Hamouda, Sally; Shaffer, Clifford A. – Computer Science Education, 2016
In this paper, we study the relationship between the use of "crib sheets" or "cheat sheets" and performance on in-class exams. Our extensive survey of the existing literature shows that it is not decisive on the questions of when or whether crib sheets actually help students to either perform better on an exam or better learn…
Descriptors: Cheating, Documentation, Data, Evaluation Methods
Zwaal, Wichard; Otting, Hans – Journal of Problem Based Learning in Higher Education, 2016
The study focuses on the seven-step procedure (SSP) in problem-based learning (PBL). The way students apply the seven-step procedure will help us understand how students work in a problem-based learning curriculum. So far, little is known about how students rate the performance and importance of the different steps, the amount of time they spend…
Descriptors: Management Development, Hospitality Occupations, Problem Based Learning, Teaching Methods