Engineering Program Evaluations Based on Automated Measurement of Performance Indicators Data Classified into Cognitive, Affective, and Psychomotor Learning Domains of the Revised Bloom's Taxonomy Article Swipe
Wajid Hussain
,
Fong Mak
,
Mohammad F. Addas
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.18260/p.27299
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.18260/p.27299
Abstract: This research references past work that indicates that the major driving force of outcomes assessment initiatives in engineering institutions has been regional and specialized accreditation standards. Continuous quality improvement and accreditation-based activity at various engineering institutions remain as relatively isolated processes, with realistic continuous quality improvement efforts maintaining minimal reference to learning outcomes assessment data measured for accreditation. The lack of utilization of digital technology and appropriate methodologies supporting the automation of outcomes assessment further exacerbate this situation. Furthermore, the learning outcomes data measured by most institutions are rarely classified into all three learning domains of the revised Bloom’s taxonomy and their corresponding categories of the levels of learning. Generally institutions classify courses of a program curriculum into three levels: introductory, reinforced and mastery. The outcomes assessment data is measured for the mastery level courses in order to streamline the documentation and effort needed for an effective program evaluation. A major disadvantage of this approach is that it does not facilitate accurate and comprehensive root-cause analysis with early remediation of observed performance deficiencies because necessary outcomes information related to deficient teaching and learning mechanisms are measured at only mastery level courses. A holistic approach for continuous quality improvement in academic learning would require a systematic, quantified measurement of performance indicators in all three domains of learning and their corresponding categories of learning levels for all course levels in a given program’s curriculum. This research presents an innovative methodology for engineering program evaluation utilizing significant customization implemented in a web-based software EvalTools® 6 for the Faculty of Engineering at _______ University. The customization includes unique curricular assessments implementing scientific constructive alignment for measurement of specific performance indicators related to ABET student outcomes. Performance indicators are classified according to the three domains of the revised Bloom’s taxonomy and their corresponding categories of learning levels. Final values of ABET student outcomes are obtained based on calculations applying an intelligent weighted averaging algorithm to associated performance indicators. The weights are related to the numerical counts of performance indicators measured for the different levels of learning for each of the three domains in multiple course levels classified as introductory, reinforced and mastery. The computed values of ABET student outcomes are then used as a performance index in program term reviews. Analytical information related to the performance indicators measured for multiple course levels, their distribution in each of the learning domains, and corresponding categories of learning levels provide valuable information that helps identify specific areas for improvement in the education process. Prioritized action items are generated and electronically transmitted to various academic committees. The committees have specific functions to continuously improve the overall quality of academic learning by aligning the design, and implementation of learning outcomes, curriculum, teaching, learning activities and assessments to acquire holistic standards as outlined in an ideal outcomes-based educational system.
Related Topics
Concepts
Accreditation
Documentation
Computer science
Curriculum
Bloom's taxonomy
Psychomotor learning
Outcome-based education
Engineering education
Continuous assessment
Artificial intelligence
Engineering management
Cognition
Knowledge management
Medical education
Mathematics education
Engineering
Psychology
Programming language
Pedagogy
Neuroscience
Medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18260/p.27299
- https://peer.asee.org/27299.pdf
- OA Status
- gold
- Cited By
- 13
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2476726359
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2476726359Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18260/p.27299Digital Object Identifier
- Title
-
Engineering Program Evaluations Based on Automated Measurement of Performance Indicators Data Classified into Cognitive, Affective, and Psychomotor Learning Domains of the Revised Bloom's TaxonomyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2016Year of publication
- Publication date
-
2016-07-07Full publication date if available
- Authors
-
Wajid Hussain, Fong Mak, Mohammad F. AddasList of authors in order
- Landing page
-
https://doi.org/10.18260/p.27299Publisher landing page
- PDF URL
-
https://peer.asee.org/27299.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://peer.asee.org/27299.pdfDirect OA link when available
- Concepts
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Accreditation, Documentation, Computer science, Curriculum, Bloom's taxonomy, Psychomotor learning, Outcome-based education, Engineering education, Continuous assessment, Artificial intelligence, Engineering management, Cognition, Knowledge management, Medical education, Mathematics education, Engineering, Psychology, Programming language, Pedagogy, Neuroscience, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2024: 6, 2023: 1, 2021: 1, 2020: 2, 2019: 1Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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