He has received specialized Quality Leadership Training at LSI Corporation and received an award LSI Corporation Worldwide Operations Review 1999 for his significant contributions to the Quality Improvement Systems. At LSI Wajid was the PE in charge of the world famous APPLE IPOD 2000-2001 processor WW qualification/production. Over the years Wajid has managed several projects related to streamlining operations with utilization of state of the art technology and digital systems.This has given him significant experience working with ISO standard quality systems.He is a specialist on ABET accreditation procedures and was appointed by the Dean of Engineering, KFUPM, Hafr Al Batin campus to lead the intensive effort of preparing the EEET program for the ABET Evaluators Team site visit in 2013. EEET received excellent comments for the display materials presented by Dr. Subal Sarkar ABET team chair which was managed to completion by Wajid. He is Digital Integrated Quality Management Systems Expert for Automated Academic Student Outcomes based Assessments MethodologyHe has taught several courses on electronics, microprocessors, electric circuits, digital electronics and instrumentation. He has conducted several workshops at the IU campus and eslewhere on Outcomes Assessment best practices, OBE, EvalTools R 6 for faculty, E learning with EvalTools R 6 for students, ABET accreditation process.He is a member of SAP Community, ISO 9001, Senior Member IEEE, IEEE Qatar, ASEE and REED MEP professionals International & Middle East.Dr. Fong K. Mak P.E., Gannon University Abstract: This research references past work which 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, learning outcomes data measured by most institutions is rarely classified into all three 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 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 early remediation of performance failures because necessary outcomes information related to deficient teaching and learning mechanisms is measured only for mastery level courses. A holistic approach for continuous quality improvement in academic learning would requi...

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In this research, we present the essential principles of an authentic outcome based educational model related to the development of learning outcomes, performance indicators and their rubrics with a focus on measurement of specific skills related to Bloom's 3 learning domains and their learning levels for engineering specializations. An analysis of culminating ABET Engineering Accreditation Commission student outcomes is made with reference to Bloom's 3 learning domains and their learning levels. A hypothetical model is presented for this analysis. The correlation of ABET student outcomes, course learning outcomes and performance indicators is clearly outlined. The necessity of the use of performance indicators is highlighted especially in reference to the measurement of course learning outcomes, development of assessments, teaching and learning activities. The importance of scientific constructive alignment of learning outcomes, performance indicators, assessments, teaching and learning strategies is discussed. A novel hybrid rubric for accurate assessment and scoring of student performances is also presented. Actual examples of implementation of this theory to program, course and student level performance evaluations using state of the art web based digital technology are shown. In summary, the benefits of specific performance indicators over generic ones are explained in detail with respect to support of authentic OBE principles, scientific constructive alignment, accurate measurement of student performances in specific engineering learning activities, performance failure analysis and continuous quality improvement.

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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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Background Primary microcephaly (MCPH) is a congenital neurodevelopmental disorder manifesting as small brain and intellectual disability. It underlies isolated reduction of the cerebral cortex that is reminiscent of early hominids which makes it suitable model disease to study the hominin‐specific volumetric expansion of brain. Mutations in 25 genes have been reported to cause this disorder. Although majority of these genes were discovered in the Pakistani population, still a significant proportion of these families remains uninvestigated. Methods We studied a cohort of 32 MCPH families from different regions of Pakistan. For disease gene identification, genome‐wide linkage analysis, Sanger sequencing, gene panel, and whole‐exome sequencing were performed. Results By employing these techniques individually or in combination, we were able to discern relevant disease‐causing DNA variants. Collectively, 15 novel mutations were observed in five different MCPH genes; ASPM (10), WDR62 (1), CDK5RAP2 (1), STIL (2), and CEP135 (1). In addition, 16 known mutations were also verified. We reviewed the literature and documented the published mutations in six MCPH genes. Intriguingly, our cohort also revealed a recurrent mutation, c.7782_7783delGA;p.(Lys2595Serfs*6), of ASPM reported worldwide. Drawing from this collective data, we propose two founder mutations, ASPM:c.9557C>G;p.(Ser3186*) and CENPJ:c.18delC;p.(Ser7Profs*2), in the Pakistani population. Conclusions We discovered novel DNA variants, impairing the function of genes indispensable to build a proper functioning brain. Our study expands the mutational spectra of known MCPH genes and also provides supporting evidence to the pathogenicity of previously reported mutations. These novel DNA variants will be helpful for the clinicians and geneticists for establishing reliable diagnostic strategies for MCPH families.

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