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Albuquerque, Maria Luiza F. Q.; Lopes, Charlie Silva; da Silveira, Denis Silva – Journal of Education for Business, 2023
Abstraction in business processes (BP) modeling arises from the recognition of similarities to the detriment of its differences. However, teaching modeling to beginning students in the context of process management is a hard task to perform, given the high level of abstraction required for these students to develop. This paper uses BP fragments to…
Descriptors: Business Administration Education, Models, Pattern Recognition, Teaching Methods
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Becker, Kirk; Meng, Huijuan – Journal of Applied Testing Technology, 2022
The rise of online proctoring potentially provides more opportunities for item harvesting and consequent brain dumping and shared "study guides" based on stolen content. This has increased the need for rapid approaches for evaluating and acting on suspicious test responses in every delivery modality. Both hiring proxy test takers and…
Descriptors: Identification, Cheating, Computer Assisted Testing, Observation
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Aydin, Gökhan; Duran, Volkan; Mertol, Hüseyin – International Journal of Curriculum and Instruction, 2021
This study aims to develop a computer program for the identification key to insect orders (Arthropoda: Hexapoda) and to investigate its effectiveness as teaching material. Secondly, this study is aiming at whether this program improves students' computational thinking skills or not longitudinal quasi-experimental design. Firstly, the study is…
Descriptors: Computer Software, Identification, Entomology, Computation
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Cannistrà, Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics