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BACKGROUND Grading individual students in teams has always been problematic. To accurately gauge individual learning outcomes, students' grades need to be based on what they have learned as an individual within the team context. However, within engineering team-based project-oriented subjects, individuals have traditionally been assigned a grade heavily influenced by the team's project deliverables rather than their individual efforts.
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Research in progress: Assessing individual student learning within team-based engineering curricula2012 •
ABSTRACT: Assessment of student learning in team-based subjects can be challenging, as the inherent complexity in this learning environment can create ambiguity for academic staff and students alike. This five-institution research project gathered data from academic staff and students about their experiences with assessment in team-based settings, data which served as a support for the development of a conceptual model for effective assessment of individual student learning in this highly collaborative setting.
2011 •
Abstract: The effective assessment of individual students' learning in team-based coursework is a complex process which may not be fully understood by academic practitioners, much less mastered. To help bring greater understanding to this pedagogical challenge, this research team developed an ALTC-funded project to investigate current assessment practices in the team-based context and to derive an effective assessment framework.
2020 ASEE Virtual Annual Conference Content Access Proceedings
Predicting Team Project Score: It’s More about Team Harmony and Less about Individual Performance2019 •
International Journal of Collaborative Engineering
A guidance framework for facilitating effective engineering student teams and its impact on individual learning2014 •
2024 •
Introduzione alle catene di Markov con matrice di transizione costante (omogenee). Numerosi esempi risolti con R.
Artificial Intelligence (AI) is the theory and development of computer systems capable of performing complex tasks that historically requires human intelligence such as recognizing speech, making decisions and identifying patterns. These tasks cannot be accomplished without the ability of the systems to learn. Machine learning is the ability of machines to learn from their past experiences. Just like humans, when machines learn under supervision, it is termed supervised learning. In this work, an in-depth knowledge on machine learning was expounded. Relevant literatures were reviewed with the aim of presenting the different types of supervised machine learning paradigms, their categories and classifiers.
2023 •
Police Practice and Research
Research in police education: current trendsJournal of Nanoscience and Nanotechnology
Synthesis of Magnetite/Amphiphilic Polymer Composite Nanoparticles as Potential Theragnostic Agents2012 •
Journal of Clinical Neurophysiology
Lateralization of Activity Associated with Language Function Using Magnetoencephalography2000 •
ACTIO: Docência em Ciências
A pandemia da COVID-19 como uma questão sociotécnica para a educação científicaPediatric Health, Medicine and Therapeutics
Prediction of Birth Weight by Using Neonatal Anthropometric Parameters at Birth in Finote Selam Hospital, Ethiopia2021 •
Revista Ibero-Americana de Estudos em Educação
A formação do pedagogo e a práxis pedagógica inclusiva em espaços escolares e não escolaresTrav. Inst. Scient., Rabat Sér. Gén
Genetic resources and molecular markers in Talitrus saltator (Amphipoda, Talitridae) from the beach of Smir2005 •
2011 •
Academia: Cite this paper: Gholami, Kiarash. 2023. "Genealogy and Coinage of Baydād." Academia.edu. Accessed March 10, 2023. pp 1-38.
From Bactria to Persis: The Genealogy and Coinage of Baydād2023 •