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Download full textEdelsbrunner, Peter; Schneider, Michael – Frontline Learning Research, 2013
Musso et al. (2013) predict students' academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can…
Descriptors: Prediction, Statistical Analysis, Structural Equation Models, Academic Achievement
Direct linkAbercrombie, Sara; Parkes, Jay; McCarty, Teresita – Interdisciplinary Journal of Problem-based Learning, 2015
This study investigates the ways in which medical students' achievement goal orientations (AGO) affect their perceptions of learning and actual learning from an online problem-based learning environment, Calibrated Peer Review™. First, the tenability of a four-factor model (Elliot & McGregor, 2001) of AGO was tested with data collected from…
Descriptors: Medical Education, Problem Based Learning, Teaching Methods, Peer Evaluation
Hakkarainen, Airi M.; Holopainen, Leena K.; Savolainen, Hannu K. – Journal of Learning Disabilities, 2015
In this longitudinal study, we investigated the role of word reading and mathematical difficulties measured in 9th grade as factors for receiving educational support for learning in upper secondary education in Grades 10 to 12 (from ages 16 to 19) and furthermore as predictors of dropout from upper secondary education within 5 years after…
Descriptors: Followup Studies, Dropout Prevention, Dropout Research, Secondary Education
