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Lauren M. Lapinski – ProQuest LLC, 2021
The purpose of this study was to determine the relationship between student participation in Technology Student Association and the development of soft skills necessary for gainful employment. This study specifically sought information on relationships between soft skills development and (a) time spent on Technology Student Association activities;…
Descriptors: Student Organizations, Student Participation, Technology, Technology Education
Plesnarski, Cathleen E. – ProQuest LLC, 2018
The purpose of this quantitative study was to examine which measures of technical skill attainment and program hours predict positive post-program placement of secondary Pennsylvania career and technical education (CTE) students who completed a Phase I or Phase II program of study. Predictor variables included performance on the end-of-program…
Descriptors: Skill Development, Predictor Variables, Student Placement, Secondary School Students
Riccio, Patricia A. – Universal Journal of Educational Research, 2015
The purpose of this pilot study was to examine predictors of improvement in critical thinking skills among online graduate nursing students in a graduate nursing research course. Thirty-five students who had taken an online Nursing research course within the prior 12 months and who were currently enrolled in the online graduate Nursing program at…
Descriptors: Predictor Variables, Critical Thinking, Thinking Skills, Nursing Students
Baker, Ryan S.; Hershkovitz, Arnon; Rossi, Lisa M.; Goldstein, Adam B.; Gowda, Sujith M. – Journal of the Learning Sciences, 2013
We present a new method for analyzing a student's learning over time for a specific skill: analysis of the graph of the student's moment-by-moment learning over time. Moment-by-moment learning is calculated using a data-mined model that assesses the probability that a student learned a skill or concept at a specific time during learning (Baker,…
Descriptors: Learning Processes, Intelligent Tutoring Systems, Probability, Skill Development
Baker, Ryan S. J. D.; Goldstein, Adam B.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we present a machine-learned model that assesses the probability that a student learned a KC at a…
Descriptors: Intelligent Tutoring Systems, Mastery Learning, Probability, Knowledge Level