TY - GEN
T1 - Python for Engineers Concept Inventory (PECI)
T2 - 47th SEFI Annual Conference 2019 - Varietas Delectat: Complexity is the New Normality
AU - Cooke, N. J.
AU - Hawwash, K. I.M.
AU - Smith, B. A.
PY - 2020
Y1 - 2020
N2 - A key outcome of Engineering Education is computing skills for industry 4.0. The curriculum must address an increasing reliance on model-based design, simulation, and algorithmic data processing. Python is a popular, in-demand language; exhaustive online resources and tutorials exist, challenging educators to make assessment robust to online plagiarism, and to make instruction contextually relevant to engineering. How do engineering educators develop, measure, and evaluate engineering students programming skills, accelerate their learning, and improve instruction effectiveness? A Concept Inventory (CI) is a powerful standardised assessment instrument to answer this question and its development and validation is an active research topic. We present a Python for Engineers CI (PECI) which assesses concepts, and errors observed in learners through 250 Multiple Choice Questions (MCQs). It is evaluated by embedding it into an introductory undergraduate module, whose pedagogy includes contextualised learning and auto-graded interactive computing laboratory notebooks. Using a mixed-methods approach on learning data from a student cohort (n=68), we validate PECI through student learning gains, reliability analysis and qualitative feedback. Items are shown to differentiate high and low performing students, and each item contributes to assessment reliability. The work is of value to engineering educators wishing to develop standardised assessment instruments for computing skills.
AB - A key outcome of Engineering Education is computing skills for industry 4.0. The curriculum must address an increasing reliance on model-based design, simulation, and algorithmic data processing. Python is a popular, in-demand language; exhaustive online resources and tutorials exist, challenging educators to make assessment robust to online plagiarism, and to make instruction contextually relevant to engineering. How do engineering educators develop, measure, and evaluate engineering students programming skills, accelerate their learning, and improve instruction effectiveness? A Concept Inventory (CI) is a powerful standardised assessment instrument to answer this question and its development and validation is an active research topic. We present a Python for Engineers CI (PECI) which assesses concepts, and errors observed in learners through 250 Multiple Choice Questions (MCQs). It is evaluated by embedding it into an introductory undergraduate module, whose pedagogy includes contextualised learning and auto-graded interactive computing laboratory notebooks. Using a mixed-methods approach on learning data from a student cohort (n=68), we validate PECI through student learning gains, reliability analysis and qualitative feedback. Items are shown to differentiate high and low performing students, and each item contributes to assessment reliability. The work is of value to engineering educators wishing to develop standardised assessment instruments for computing skills.
KW - Assessment reliability
KW - Computing skills
KW - Concept inventory
KW - Python
UR - http://www.scopus.com/inward/record.url?scp=85077819182&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85077819182
T3 - SEFI 47th Annual Conference: Varietas Delectat... Complexity is the New Normality, Proceedings
SP - 270
EP - 279
BT - SEFI 47th Annual Conference
A2 - Nagy, Balazs Vince
A2 - Murphy, Mike
A2 - Jarvinen, Hannu-Matti
A2 - Kalman, Aniko
PB - European Society for Engineering Education (SEFI)
Y2 - 16 September 2019 through 19 September 2019
ER -