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Batley, Prathiba Natesan; Hedges, Larry V. – Grantee Submission, 2021
Although statistical practices to evaluate intervention effects in SCEDs have gained prominence in the recent times, models are yet to incorporate and investigate all their analytic complexities. Most of these statistical models incorporate slopes and autocorrelations both of which contribute to trend in the data. The question that arises is…
Descriptors: Bayesian Statistics, Models, Accuracy, Computation
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Danielle S. McNamara; Tracy Arner; Elizabeth Reilley; Paul Alvarado; Chani Clark; Thomas Fikes; Annie Hale; Betheny Weigele – Grantee Submission, 2022
Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…
Descriptors: Electronic Learning, Educational Technology, Networks, Learning Analytics
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Sao Pedro, Michael A.; Gobert, Janice D.; Baker, Ryan S. – Grantee Submission, 2014
We explore in this paper if automated scaffolding delivered via a pedagogical agent within a simulation can help students acquire data collection inquiry skills. Our initial analyses revealed that such scaffolding was effective for helping students who initially did not know two specific skills, designing controlled experiments and testing stated…
Descriptors: Automation, Scaffolding (Teaching Technique), Intelligent Tutoring Systems, Data Collection
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Sao Pedro, Michael A.; Baker, Ryan S. J. d.; Gobert, Janice D. – Grantee Submission, 2012
Data-mined models often achieve good predictive power, but sometimes at the cost of interpretability. We investigate here if selecting features to increase a model's construct validity and interpretability also can improve the model's ability to predict the desired constructs. We do this by taking existing models and reducing the feature set to…
Descriptors: Content Validity, Data Interpretation, Models, Predictive Validity