Quantitative Methods in Biomedical Applications: Creative Inquiry and Digital-Learning Environments to Engage and Mentor STEM Students in Mathematics (NSF Funded Research)

Khan, Taufiquar; DesJardins, John D.; Reba, Marylin; Breazel, Ellen; Viktorova, Irina

doi:10.1007/978-1-4614-9332-7_3

Cited by 1 publication

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“…This industry-driven mandate requires innovative models of learning. Besides traditional approaches, learning should include: authentic tasks (Harris, Bransford, & Brophy, 2002); problem-oriented learning (Magjarevic et al, 2010); information and communication technology (Mantas, Ammenwerth, Demiris et al, 2010); and small-group collaborations (Khan, Desjardins, Reba, Breazel, & Viktorova, 2013) to enhance problem-solving and decision-making competencies (Huang, 2007). In South Africa (SA), there is scant research on the competencies needed in the biomedical sciences and the specific contribution from the Mathematics discipline.…”

Section: Introductionmentioning

confidence: 99%

Competencies in Mathematical Modelling Tasks: An Error Analysis

Kotze

2018

EURASIA J MATH SCI T

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This paper aimed to investigate the type of modelling task that may elicit competencies that are more aligned with demands from the biomedical technology industry. The inquiry identified strengths and weaknesses in students' modelling competencies by analysing errors in two types of modelling tasks: atomistic and holistic. By using subtasks, errors could be identified according to the Newman error categories and compared with six modelling competencies according to the framework of Blum and Leiß. First-year biomedical technology students at a South African university made more errors in interpreting, validating and presenting, these being modelling competencies required to convert mathematical results to real-world results. The findings indicated that competencies embedded in atomistic tasks are more relevant to workplace demands in the local setting than those elicited in holistic modelling tasks. The implications for classroom practices are discussed.

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Section: Introductionmentioning

confidence: 99%

Competencies in Mathematical Modelling Tasks: An Error Analysis

Kotze

2018

EURASIA J MATH SCI T

View full text Add to dashboard Cite

show abstract

Quantitative Methods in Biomedical Applications: Creative Inquiry and Digital-Learning Environments to Engage and Mentor STEM Students in Mathematics (NSF Funded Research)

Cited by 1 publication

References 3 publications

Competencies in Mathematical Modelling Tasks: An Error Analysis

Competencies in Mathematical Modelling Tasks: An Error Analysis

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