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Doleck, Tenzin; Bazelais, Paul; Lemay, David John – Journal of Computing in Higher Education, 2018
It is widely recognized and accepted that behavioral intention is the key direct determinant of technology use and the majority of research continues to promote this practice. Yet the influence of behavioral intention (i.e., an individual's conscious plan to use a technology) has been called into question more recently, as behavioral intention…
Descriptors: Computer Attitudes, Intention, Technology Uses in Education, Electronic Learning
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Bosch, Nigel; Paquette, Luc – Journal of Learning Analytics, 2018
Metrics including Cohen's kappa, precision, recall, and F[subscript 1] are common measures of performance for models of discrete student states, such as a student's affect or behaviour. This study examined discrete model metrics for previously published student model examples to identify situations where metrics provided differing perspectives on…
Descriptors: Models, Comparative Analysis, Prediction, Probability
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Beck, Melissa R.; Goldstein, Rebecca R.; van Lamsweerde, Amanda E.; Ericson, Justin M. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2018
Attention allocation determines the information that is encoded into memory. Can participants learn to optimally allocate attention based on what types of information are most likely to change? The current study examined whether participants could incidentally learn that changes to either high spatial frequency (HSF) or low spatial frequency (LSF)…
Descriptors: Attention, Incidental Learning, Memory, Visual Perception
Makela, Susanna; Si, Yajuan; Gelman, Andrew – Grantee Submission, 2018
Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. We consider a two-stage cluster sampling design where the clusters are first selected with probability proportional to…
Descriptors: Bayesian Statistics, Statistical Inference, Sampling, Probability
Sinharay, Sandip – Grantee Submission, 2018
Producers and consumers of test scores are increasingly concerned about fraudulent behavior before and during the test. There exist several statistical or psychometric methods for detecting fraudulent behavior on tests. This paper provides a review of the Bayesian approaches among them. Four hitherto-unpublished real data examples are provided to…
Descriptors: Ethics, Cheating, Student Behavior, Bayesian Statistics
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Montero, Shirly; Arora, Akshit; Kelly, Sean; Milne, Brent; Mozer, Michael – International Educational Data Mining Society, 2018
Personalized learning environments requiring the elicitation of a student's knowledge state have inspired researchers to propose distinct models to understand that knowledge state. Recently, the spotlight has shone on comparisons between traditional, interpretable models such as Bayesian Knowledge Tracing (BKT) and complex, opaque neural network…
Descriptors: Artificial Intelligence, Individualized Instruction, Knowledge Level, Bayesian Statistics
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Magalhães, Marcos N.; Camargo Magalhães, Maria Cecilia – Statistics Education Research Journal, 2021
Conceptual appropriation is central to the teaching-learning and development processes. The intellectual stage is expressed by writing, verbalization and the use of the object under construction, among other means of expression. This paper discusses the conceptual appropriation by means of the creation of collaborative situations in which the…
Descriptors: Statistics Education, Persuasive Discourse, Mathematical Concepts, Concept Formation
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Luik, Piret; Lepp, Marina – International Review of Research in Open and Distributed Learning, 2021
Computer programming MOOCs attract people who have different motivations. Previous studies have hypothesized that the motivation declared before starting the course can be an important predictor of distinctive dropout rates. The aim of this study was to outline the main motivation clusters of participants in a computer programming MOOC, and to…
Descriptors: Student Motivation, Programming, Online Courses, Large Group Instruction
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Cohausz, Lea – Journal of Educational Data Mining, 2022
Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…
Descriptors: Guidelines, Academic Achievement, Dropouts, Prediction
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Yates, Stephanie R. – Journal of Financial Counseling and Planning, 2020
Using data from the 2016 Survey of Consumer Finances, this study investigates factors that affect electronic banking adoption rates. Financial knowledge, income, education, and credit card ownership are associated with a high probability of electronic banking adoption. However, age is negatively associated with the probability of online banking…
Descriptors: Banking, Online Systems, Money Management, Probability
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Van Brown, Bethany L.; Kopak, Albert; Hoffmann, Norman – Journal of Drug Education, 2020
Exposure to violence can lead to a dramatic increase in the likelihood of the development of a substance use disorder (SUD). Given the overlap between the two, substance use for survivors of violence, then, can be a coping mechanism to manage the traumatic effects of abuse and persistent use can evolve into a diagnosable SUD. This study was…
Descriptors: Violence, Probability, Substance Abuse, Correlation
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Masa, Rainier; Chowa, Gina; Sherraden, Michael – Youth & Society, 2020
The objective of this study was to examine the effect of a financial inclusion project on youth's sexual risk behaviors and victimization. The project occurred in Ghana, where 100 schools were assigned to either a school-based savings program (SBSP), a marketing campaign, or a control group. Pretest and posttest data were collected in 2011 and…
Descriptors: Money Management, Sexuality, Health Behavior, At Risk Persons
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Britton, Tobias C.; Wilkinson, Ellen H.; Hall, Scott S. – American Journal on Intellectual and Developmental Disabilities, 2020
Limited information is available concerning the specificity of the forms and functions of aggressive behavior exhibited by boys with fragile X syndrome (FXS). To investigate these relationships, we conducted indirect functional assessments of aggressive behavior exhibited by 41 adolescent boys with FXS and 59 age and symptom-matched controls with…
Descriptors: Aggression, Males, Genetic Disorders, Adolescents
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Günhan, Burak Kürsad; Röver, Christian; Friede, Tim – Research Synthesis Methods, 2020
Meta-analyses of clinical trials targeting rare events face particular challenges when the data lack adequate numbers of events for all treatment arms. Especially when the number of studies is low, standard random-effects meta-analysis methods can lead to serious distortions because of such data sparsity. To overcome this, we suggest the use of…
Descriptors: Meta Analysis, Medical Research, Drug Therapy, Bayesian Statistics
Marini, Jessica P.; Westrick, Paul A.; Young, Linda; Shaw, Emily J. – College Board, 2020
Recent national research on the validity of the SAT shows that students with higher SAT scores are more likely to earn higher grades in college, and that SAT scores add about 15% more predictive power above high school grade point average (HSGPA) to estimate students' college performance (Westrick, Marini, Young, Ng, Shmueli, & Shaw, 2019).…
Descriptors: College Entrance Examinations, Validity, Test Validity, Scores
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