Improving Instruction and Assessment via Bloom’s Taxonomy and Descriptive Rubrics

Gosselin, Kathryn R.; Okamoto, Nicole

doi:10.18260/1-2--30630

Cited by 3 publications

(13 citation statements)

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“…We primarily considered research on accreditation topics in popular engineering education and educational psychology journals and conference proceedings spanning the last 15 years. The results of the literature review were parsed using an OBE theory based qualitative analysis of CQI systems to yield the summary below: PIs are used to assess SOs and are mostly generic thereby lack the required specificity for valid and reliable assessment and evaluation [20,23,[25][26]28,[30][31]36,[39][40][41]45,48,56,57,58] Most rubrics are generic and vague using simplistic language and lack technical details to accurately assess several hundred complex engineering activities [20,23,26,41,56,57,58] Generic rubrics are applied by independent raters to score past course portfolios [36,[40][41]45…”

Section: Sos Data For Manual Cqi Systems-a Qualitative Analysismentioning

confidence: 99%

“…As per Adelman (2015), language of PIs should be specific to accurately align with course content and student learning activity [20]. The PIs should be assessed in courses from all phases of a curriculum to achieve learning progression for achieving proficiency in engineering skills [20,23,26,41,52,[56][57][58]76].…”

Section: ) 'Design Down' Mapping Model From Goals To Performance Indmentioning

confidence: 99%

“…Manual CQI models have been increasingly cited in literature as being deficient with several issues such as deficient standards of language of learning outcomes statements, generic and vague performance criteria, lack of use of accurate topic specific analytic rubrics, lack of reliable and valid assessment and evaluation criteria, random or ad hoc sampling of outcomes information, lack of proper alignment of learning activities with outcomes, inability to achieve comprehensive coverage of Bloom's three domains of learning, lengthy and impractical quality cycles, inability to implement real-time learning improvements in enrolled cohorts etc. [23][24][25][26][27][28][29][39][40][42][43][44][45]48,[53][54]56,58,[61][62]76].…”

Section: Introductionmentioning

confidence: 99%

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Impact Evaluations of Engineering Programs Using ABET Student Outcomes

Hussain

Spady²,

Khan

et al. 2021

IEEE Access

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Engineering programs worldwide collect and report student learning outcomes data to conduct program evaluations for quality assurance and accreditation purposes. Accreditation agencies such as ABET typically mandate that at least two years of program evaluation data be provided and for institutions to show how this data has been used for continuous quality improvement. Engineering programs rarely evaluate interventions using multi-term student outcomes information over several years, since this quantitative data generally lacks accuracy and statistical power. The quality of outcomes data is affected by obsolete assessment methods and lack of digital access and technical analysis. In this study, we present essential elements of an authentic outcome based assessment model that used web-based software and embedded assessment technology to collect and report accurate cohort outcomes for credible multi-term evaluations. A non-experimental approach employing regression analyses were used to identify trends in student outcomes and evaluate the impact for three engineering programs. Detailed rubrics provide criteria to accurately classify multi-year student outcomes. The findings of this study present practical steps for engineering programs to effectively collect and report accurate cohort outcomes data and perform credible evaluations of program interventions based on multi-year outcomes data.

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Section: Sos Data For Manual Cqi Systems-a Qualitative Analysismentioning

confidence: 99%

Section: ) 'Design Down' Mapping Model From Goals To Performance Indmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact Evaluations of Engineering Programs Using ABET Student Outcomes

Hussain

Spady²,

Khan

et al. 2021

IEEE Access

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“… Learning models are generally not understood and used comprehensively as the founding framework for CQI efforts [3] , [16] , [42] , [49] , [62] , [67] Language of Course Outcomes (COs) and associated PIs is deficient and lacks alignment with actual learning activities [3] , [16] , [28] , [29] , [42] , [49] , [62] – [64] , [67] . PIs are mostly generic and lack the required specificity to achieve required validity and reliability in assessment and evaluation [16] , [21] , [23] , [24] , [26] , [28] , [29] , [35] , [40] – [42] , [46] , [49] , [67] – [69] . Most rubrics are generic, simplistic and vague, and lack the necessary detail to accurately assess several hundred complex student learning activities of any engineering specialization [16] , [21] , [24] , [42] , [67] – [69] .…”

Section: Research Frameworkmentioning

confidence: 99%

ABET Accreditation During and After COVID19 - Navigating the Digital Age

Hussain

Spady²,

Naqash

et al. 2020

IEEE Access

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Engineering accreditation agencies and governmental educational bodies worldwide require programs to evaluate specific learning outcomes information for attainment of student learning and establish accountability. Ranking and accreditation have resulted in programs adopting shortcut approaches to collate cohort information with minimally acceptable rigor for Continuous Quality Improvement (CQI). With tens of thousands of engineering programs seeking accreditation, qualifying program evaluations that are based on reliable and accurate cohort outcomes is becoming increasingly complex and is high stakes. Manual data collection processes and vague performance criteria assimilate inaccurate or insufficient learning outcomes information that cannot be used for effective CQI. Additionally, due to COVID19 global pandemic, many accreditation bodies have cancelled onsite visits and either deferred or announced virtual audit visits for upcoming accreditation cycles. In this study, we examine a novel meta-framework to qualify state of the art digital Integrated Quality Management Systems for three engineering programs seeking accreditation. The digital quality systems utilize authentic OBE frameworks and assessment methodology to automate collection, evaluation and reporting of precision CQI data. A novel Remote Evaluator Module that enables successful virtual ABET accreditation audits is presented. A theory based mixed methods approach is applied for evaluations. Detailed results and discussions show how various phases of the meta-framework help to qualify the context, construct, causal links, processes, technology, data collection and outcomes of comprehensive CQI efforts. Key stakeholders such as accreditation agencies and universities can adopt this multi-dimensional approach for employing a holistic metaframework to achieve accurate and credible remote accreditation of engineering programs.

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“…The motivation to use scoring rubrics in engineering education also is due to the lack of satisfaction emanating from the use of the traditional grading process which have been criticized for their bias, and unrealistic standards [2,9,15]. Rubrics are attractive since they can be adjusted to assess specific skills and describe precisely the expected outcomes [16]. In addition, they can convey the professor's expectations to students, make the assessment method more transparent [17], and can facilitate in providing feedbacks to the students on the quality and quantity of student learning.…”

Section: Introductionmentioning

confidence: 99%

Reconciling the Student’s Deliverables with the Instructor’s Expectations in Engineering Exams

Zegeye

Eldredge

2020 ASEE Virtual Annual Conference Content Access Proceedings

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and his Ph.D degree from Louisiana State University. He is an Associate Professor of mechanical engineering at Liberty University. His research interests are development and application of composite and nanocomposites, smart structures and tactile sensors, functionally graded materials and synthesis of carbon nanomaterial and structures. Dr. Tom Eldredge, Liberty UniversityTom Eldredge received his B.S., M.S., and Ph.D. degrees all in mechanical engineering from the University of Tennessee. He is an Associate Professor of mechanical engineering at Liberty University. He is a Professional Engineer, licensed in the state of Connecticut. Tom Eldredge has over 25 years of experience in computational fluid dynamics (CFD) modeling, related to the power industry for design of combustion systems, cooling tower modeling, and hydro power applications. He has an interest in energy research, particularly as it relates to the thermal sciences and fluid mechanics.

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Improving Instruction and Assessment via Bloom’s Taxonomy and Descriptive Rubrics

Cited by 3 publications

References 13 publications

Impact Evaluations of Engineering Programs Using ABET Student Outcomes

Impact Evaluations of Engineering Programs Using ABET Student Outcomes

ABET Accreditation During and After COVID19 - Navigating the Digital Age

Reconciling the Student’s Deliverables with the Instructor’s Expectations in Engineering Exams

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