This document provides information about preparing for an ABET accreditation evaluation visit. It discusses what ABET is and its purpose in accrediting engineering programs. It outlines the accreditation timeline and responsibilities of the program evaluator and team chair before, during, and after the visit. These include reviewing the self-study report and conducting interviews and facility tours to evaluate how the program meets ABET's criteria. The typical visit agenda involves initial team meetings and meetings with campus administrators over a 2-day period.
Function of science is to Observe ,Discover and to Formulate the laws of nature. Approach for excellence in science also need creation of Infrastructure facilities and Environment
Function of Technology is to Deal with means of application of science and scientific content which is Universal and Application conditioned by environment of application. Excellence in Technology is location specific.
Main Function of Engineering is to convert resources available in Goods and services needed by society
Realistic and optimal solution besides due Weightage to existing parameters involved in lies in Scientific , Technological ,Economic , Social, Ethical , Management , political
Discover the power of Vector Search using OpenAI in Azure Cognitive Search through a comprehensive .NET application tutorial. This presentation will delve into the intricacies of integrating Azure OpenAI with your .NET applications, focusing specifically on the creation and utilization of vector embeddings. Learn how to effectively harness the capabilities of Azure OpenAI for generating precise vector embeddings, which are crucial for enhancing search functionalities in your applications. We will explore the concept of Hybrid search, demonstrating how it combines traditional keyword search with the advanced vector search to provide more relevant and context-aware results. This session is designed to equip developers with the knowledge and skills needed to implement state-of-the-art search capabilities in their .NET applications, leveraging the cutting-edge AI and machine learning technologies provided by Azure OpenAI.
Machine learning at scale with Google Cloud PlatformMatthias Feys
Machine Learning typically involves big datasets and lots of model iterations. This presentation shows how to use GCP to speed up that process with ML Engine and Dataflow. The focus of the presentation is on tooling not on models or business cases.
Function of science is to Observe ,Discover and to Formulate the laws of nature. Approach for excellence in science also need creation of Infrastructure facilities and Environment
Function of Technology is to Deal with means of application of science and scientific content which is Universal and Application conditioned by environment of application. Excellence in Technology is location specific.
Main Function of Engineering is to convert resources available in Goods and services needed by society
Realistic and optimal solution besides due Weightage to existing parameters involved in lies in Scientific , Technological ,Economic , Social, Ethical , Management , political
Discover the power of Vector Search using OpenAI in Azure Cognitive Search through a comprehensive .NET application tutorial. This presentation will delve into the intricacies of integrating Azure OpenAI with your .NET applications, focusing specifically on the creation and utilization of vector embeddings. Learn how to effectively harness the capabilities of Azure OpenAI for generating precise vector embeddings, which are crucial for enhancing search functionalities in your applications. We will explore the concept of Hybrid search, demonstrating how it combines traditional keyword search with the advanced vector search to provide more relevant and context-aware results. This session is designed to equip developers with the knowledge and skills needed to implement state-of-the-art search capabilities in their .NET applications, leveraging the cutting-edge AI and machine learning technologies provided by Azure OpenAI.
Machine learning at scale with Google Cloud PlatformMatthias Feys
Machine Learning typically involves big datasets and lots of model iterations. This presentation shows how to use GCP to speed up that process with ML Engine and Dataflow. The focus of the presentation is on tooling not on models or business cases.
Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]Animesh Singh
Kubeflow Pipelines and TensorFlow Extended (TFX) together is end-to-end platform for deploying production ML pipelines. It provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor your machine learning system. In this talk we describe how how to run TFX in hybrid cloud environments.
An introduction to engineering for K-12 counselors and educators. Strategies are introduced for introducing students to engineering. This presentation was designed for the educators who participate in the T-STEM Gender Equity workshops hosted by WTIF-HTHH. This specific workshop was presented on Dec 1, 2010 by Meagan Ross (mail@meaganross.com).
My books- Learning to Go https://gum.co/learn2go & The 30 Goals Challenge for Teachers http://routledge.com/books/details/9780415735346/
Resources http://shellyterrell.com/lessonstarters
How to apply machine learning into your CI/CD pipelineAlon Weiss
A quick introduction to AIOps, the business reasons why the CI/CD pipeline needs to constantly improve, and how this can be accomplished with data that's already available with existing Machine Learning and other algorithms.
Using MLOps to Bring ML to Production/The Promise of MLOpsWeaveworks
In this final Weave Online User Group of 2019, David Aronchick asks: have you ever struggled with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on the ways MLOps has helped to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with more stability, than ever before.
The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ
Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft
Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups.
Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List
Weaveworks will cover concepts such as GitOps (operations by pull request), Progressive Delivery (canary, A/B, blue-green), and how to apply those approaches to your machine learning operations to mitigate risk.
Engineering career presentation for middle schoolikfly2002
Engineering Week -like (aka Career Day) presentation targeted toward middle schoolers. This discusses what engineers do, similarity to inventors, famous inventors, a "make a sandwich" instructions exercise, and engineering career information.
Effectively talking to kids about engineeringDiscoverE
This workshop walks you through the top engineering messages that resonate with kids and shows you how to incorporate them into your outreach efforts. Learn about the research behind the messaging and get practical tips for how to engage kids with real-world examples and compelling images. Whether you are a new volunteer or a seasoned veteran, a review of this workshop will help to increase your effectiveness.
Simplifying Model Management with MLflowDatabricks
<p>Last summer, Databricks launched MLflow, an open source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs and model packaging. MLflow has grown quickly since then, with over 120 contributors from dozens of companies, including major contributions from R Studio and Microsoft. It has also gained new capabilities such as automatic logging from TensorFlow and Keras, Kubernetes integrations, and a high-level Java API. In this talk, we’ll cover some of the new features that have come to MLflow, and then focus on a major upcoming feature: model management with the MLflow Model Registry. Many organizations face challenges tracking which models are available in the organization and which ones are in production. The MLflow Model Registry provides a centralized database to keep track of these models, share and describe new model versions, and deploy the latest version of a model through APIs. We’ll demonstrate how these features can simplify common ML lifecycle tasks.</p>
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionBATbern
What powers the AI/ML services of Switzerland's leading telecommunication company? In this talk, we will provide an overview of the different AI/ML projects at Swisscom, from Conversational AI and Recommender Systems to Anomaly Detection. Moreover, we will show how we automate, scale, and operationalise these ML pipelines in production, highlighting the MLOps techniques and open source tools that are used. Finally, we will present Swisscom's roadmap towards the cloud with AWS and discuss how we envision a common MLOps solution for the organisation.
Torry Harris API and Application Integration Governance FrameworkShubaS4
An API and application integration governance framework should facilitate good governance. It must allow the initiative to evolve, and iteratively present best practices based on results achieved. The Torry Harris API and Application Integration Governance Framework enables cohesive integration across the enterprise such that all elements are connected, rationalized, and organized to provide consistent guidance and incentives that executives and business unit leaders require.
For more information, visit - https://www.torryharris.com/services/api-and-application-integration-governance
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
Accelerating Path to Production for Generative AI-powered ApplicationsHostedbyConfluent
"In this session, we will discuss some recent developments in Generative AI and how those can be leveraged to build intelligent applications. Learn how to bring the power of large language models (LLMs) to your private, real-time operational data across multiple data types. We will talk about improving the accuracy of LLMs in your applications by leveraging Retrieval Augmented Generation, which provides proprietary knowledge to the LLM.
From real-time responses to sophisticated interactions, learn how you can easily build a range of AI-driven experiences that leverage your operational data with minimal complexity.
MongoDB Atlas provides native vector search capabilities and a flexible document model all within an enterprise-ready developer data platform empowering teams to iterate quickly on applications enriched with generative AI. Coupling Atlas with Confluent makes it easier to leverage streaming data when informing LLMs with proprietary data."
Regulating Generative AI - LLMOps pipelines with TransparencyDebmalya Biswas
The growing adoption of Gen AI, esp. LLMs, has re-ignited the discussion around AI Regulations — to ensure that AI/ML systems are responsibly trained and deployed. Unfortunately, this effort is complicated by multiple governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms.
Rather than trying to understand and regulate all types of AI, we recommend a different (and practical) approach in this talk based on AI Transparency —
to transparently outline the capabilities of the AI system based on its training methodology and set realistic expectations with respect to what it can (and cannot) do.
We outline LLMOps architecture patterns and show how the proposed approach can be integrated at different stages of the LLMOps pipeline capturing the model's capabilities. In addition, the AI system provider also specifies scenarios where (they believe that) the system can make mistakes, and recommends a ‘safe’ approach with guardrails for those scenarios.
Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]Animesh Singh
Kubeflow Pipelines and TensorFlow Extended (TFX) together is end-to-end platform for deploying production ML pipelines. It provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor your machine learning system. In this talk we describe how how to run TFX in hybrid cloud environments.
An introduction to engineering for K-12 counselors and educators. Strategies are introduced for introducing students to engineering. This presentation was designed for the educators who participate in the T-STEM Gender Equity workshops hosted by WTIF-HTHH. This specific workshop was presented on Dec 1, 2010 by Meagan Ross (mail@meaganross.com).
My books- Learning to Go https://gum.co/learn2go & The 30 Goals Challenge for Teachers http://routledge.com/books/details/9780415735346/
Resources http://shellyterrell.com/lessonstarters
How to apply machine learning into your CI/CD pipelineAlon Weiss
A quick introduction to AIOps, the business reasons why the CI/CD pipeline needs to constantly improve, and how this can be accomplished with data that's already available with existing Machine Learning and other algorithms.
Using MLOps to Bring ML to Production/The Promise of MLOpsWeaveworks
In this final Weave Online User Group of 2019, David Aronchick asks: have you ever struggled with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on the ways MLOps has helped to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with more stability, than ever before.
The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ
Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft
Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups.
Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List
Weaveworks will cover concepts such as GitOps (operations by pull request), Progressive Delivery (canary, A/B, blue-green), and how to apply those approaches to your machine learning operations to mitigate risk.
Engineering career presentation for middle schoolikfly2002
Engineering Week -like (aka Career Day) presentation targeted toward middle schoolers. This discusses what engineers do, similarity to inventors, famous inventors, a "make a sandwich" instructions exercise, and engineering career information.
Effectively talking to kids about engineeringDiscoverE
This workshop walks you through the top engineering messages that resonate with kids and shows you how to incorporate them into your outreach efforts. Learn about the research behind the messaging and get practical tips for how to engage kids with real-world examples and compelling images. Whether you are a new volunteer or a seasoned veteran, a review of this workshop will help to increase your effectiveness.
Simplifying Model Management with MLflowDatabricks
<p>Last summer, Databricks launched MLflow, an open source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs and model packaging. MLflow has grown quickly since then, with over 120 contributors from dozens of companies, including major contributions from R Studio and Microsoft. It has also gained new capabilities such as automatic logging from TensorFlow and Keras, Kubernetes integrations, and a high-level Java API. In this talk, we’ll cover some of the new features that have come to MLflow, and then focus on a major upcoming feature: model management with the MLflow Model Registry. Many organizations face challenges tracking which models are available in the organization and which ones are in production. The MLflow Model Registry provides a centralized database to keep track of these models, share and describe new model versions, and deploy the latest version of a model through APIs. We’ll demonstrate how these features can simplify common ML lifecycle tasks.</p>
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionBATbern
What powers the AI/ML services of Switzerland's leading telecommunication company? In this talk, we will provide an overview of the different AI/ML projects at Swisscom, from Conversational AI and Recommender Systems to Anomaly Detection. Moreover, we will show how we automate, scale, and operationalise these ML pipelines in production, highlighting the MLOps techniques and open source tools that are used. Finally, we will present Swisscom's roadmap towards the cloud with AWS and discuss how we envision a common MLOps solution for the organisation.
Torry Harris API and Application Integration Governance FrameworkShubaS4
An API and application integration governance framework should facilitate good governance. It must allow the initiative to evolve, and iteratively present best practices based on results achieved. The Torry Harris API and Application Integration Governance Framework enables cohesive integration across the enterprise such that all elements are connected, rationalized, and organized to provide consistent guidance and incentives that executives and business unit leaders require.
For more information, visit - https://www.torryharris.com/services/api-and-application-integration-governance
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
Accelerating Path to Production for Generative AI-powered ApplicationsHostedbyConfluent
"In this session, we will discuss some recent developments in Generative AI and how those can be leveraged to build intelligent applications. Learn how to bring the power of large language models (LLMs) to your private, real-time operational data across multiple data types. We will talk about improving the accuracy of LLMs in your applications by leveraging Retrieval Augmented Generation, which provides proprietary knowledge to the LLM.
From real-time responses to sophisticated interactions, learn how you can easily build a range of AI-driven experiences that leverage your operational data with minimal complexity.
MongoDB Atlas provides native vector search capabilities and a flexible document model all within an enterprise-ready developer data platform empowering teams to iterate quickly on applications enriched with generative AI. Coupling Atlas with Confluent makes it easier to leverage streaming data when informing LLMs with proprietary data."
Regulating Generative AI - LLMOps pipelines with TransparencyDebmalya Biswas
The growing adoption of Gen AI, esp. LLMs, has re-ignited the discussion around AI Regulations — to ensure that AI/ML systems are responsibly trained and deployed. Unfortunately, this effort is complicated by multiple governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms.
Rather than trying to understand and regulate all types of AI, we recommend a different (and practical) approach in this talk based on AI Transparency —
to transparently outline the capabilities of the AI system based on its training methodology and set realistic expectations with respect to what it can (and cannot) do.
We outline LLMOps architecture patterns and show how the proposed approach can be integrated at different stages of the LLMOps pipeline capturing the model's capabilities. In addition, the AI system provider also specifies scenarios where (they believe that) the system can make mistakes, and recommends a ‘safe’ approach with guardrails for those scenarios.
This slide is about academic and administrative audit for the quality control in the educational institutes. it also deals with various management techniques including Kaizen, 5S, etc. This slideshow is useful for the NAAC purpose.
accreditation-by-nba-jntu I know you.pdfthefact9354
Accredited by Zomato h to be a e e e e e launda h to u get I love you 😘😘 h to kya karu yar kya ho gya tha but nhi hai to warna hum bhi to nhi ho rha tha aur fir se baat hai kya yu nhi hoo filhal h n u ready ho to be the same as my friend to be in a hurry and I love 💕💕💕💕💕💕💕💕💕 h to tee shirt to kya karu yar kya ho raha hai na ki nhi h kya hai kya yu to kya hua tha ki wo log kya sochenge
Using Nursing Exam Data Effectively in Preparing Nursing AccreditationExamSoft
Presented by Ainslie Nibert, Associate Dean/Associate Professor, College of Nursing, Texas Woman's University
Faculty facing either an initial nursing accreditation, or those preparing for a re-affirmation of accreditation visit, need to amass evidence demonstrating how the program is evaluated for achievement of program outcome using reliable and valid measurements. One of the most valuable resources of this evidence is a collection of student performance data from teacher-made and standardized exams used throughout the curriculum. How can faculty demonstrate that the exams they deliver to students are both reliable and valid? The purpose of this webinar is to discuss how faculty can incorporate assessment data and related analysis into their curriculum evaluation processes; establish that the teacher-made and standardized exams administered throughout the program are reliable and valid; and include assessment findings in the accreditation self-study that demonstrate compliance with nationally-recognized education standards in nursing.
2. Disclaimer
The information presented here represents
the experience of the consultant and does
not represent any endorsement by either
the ABET Foundation or ABET, Inc..
3/29/2016 2
3. Topics
• What/Who is ABET?
• Context for Evaluation
• Timeline & Terminology
• PEV Responsibilities Before, During & After
Visit
• Typical Visit Schedule
• EAC Criteria & Suggestions
• Questions
3/29/2016 3
4. ABET’s Core Purpose
With ABET accreditation,
students, employers, and
the society we serve can be
confident that a program
meets the quality standards
that produce graduates
prepared to enter a global
workforce
3/29/2016 4
5. Who Recognizes ABET?
In the U.S.
• 35 Member and Associate Member Societies of ABET
• Council for Higher Education Accreditation (CHEA)
• State Boards for Engineering & Surveying Licensure & Registration (over 55
jurisdictions)
• U.S. Patent Office
• U.S. Reserve Officers Training Corps
• Council of Engineering Specialty Boards (CESB)
• Board of Certified Safety Professionals (BCSP)
• Accreditors in other disciplines
• U.S. Trade Office
• U.S. State Department
• Employers (position announcements)
3/29/2016 5
7. What Does ABET Accredit?
• Academic program leading to specific degree in
a specific discipline
• Misconceptions clarified:
– Not institutions
– Not schools, colleges, or departments
– Not facilities, courses, or faculty
– Not graduates
– Not degrees
3/29/2016 7
8. Objectives of ABET Accreditation
(1) Assure that graduates of an accredited
program are adequately prepared to enter
and continue the practice of engineering’
(2) Stimulate the improvement of engineering
education;
(3) Encourage new and innovative approaches
to engineering education and its assessment;
and
(4) Identify accredited programs to the public.
3/29/2016 8
9. ABET Impact
• Approximately 3,500 programs at over 700
colleges and universities in 29 countries
have received ABET accreditation.
• Approximately 85,000 students graduate
from ABET-accredited programs each year,
and millions of graduates have received
degrees from ABET-accredited programs
since 1932.
3/29/2016 9
12. Organizational Structure
Volunteer-Driven: 2,200+ Volunteers
100% of accreditation decisions are made by volunteers
Board of
Directors
• Elected by Board of
Delegates
• Provides strategic
direction and plans
• Appeals process
4 Commissions
• ASAC, CAC,
EAC, ETAC
• Make decisions
on accreditation
status
• Implement
accreditation
policies
• Propose changes
to criteria
Program
Evaluators
• Visit campuses
• Evaluate individual
programs
• Make initial
accreditation
recommendations
• “Face of ABET”
Board of
Delegates
• Nominated by &
represent the
member societies
• Decides policy and
procedures
• Approves criteria
13. Proliferation of criteria
Need for innovation in programs
Prescriptiveness of criteria
Industry call for change – continuous
improvement & preparation for
professional practice
CATALYST FOR CHANGE
(early 1990’s)
3/29/2016 13
15. New Philosophy
• Institutions and Programs define mission and
objectives to meet the needs of their constituents –
enables program differentiation
• Emphasis on outcomes – preparation for
professional practice
• Programs demonstrate how criteria and educational
objectives are being met
• Focus on continuous improvement and sense of
urgency
3/29/2016 15
16. ISO 9001:2008
• ABET is committed to total quality
management in is own operations and has
obtained ISO 9001:2008 certification. A
third party auditor has verified compliance
with the criteria.
– A focus on the customer
– Organization-wide continuous improvement
– Documented critical processes
– Management commitment to a QMS.
3/29/2016 16
17. Quality Management System: ISO
9000:2008
17
Measurement,
Analysis &
Improvement
Resource
Allocation
Management
Responsibility
Product / Service
RealizationRqmts
Product /
Service
Measurement,
Analysis &
Improvement
Resource
Planning
Management
Responsibility
Satisfaction
Customer Customer
Continual improvement of quality management system
3/29/2016
18. ABET Value
Students and Parents
• Helps students select quality programs
• Shows institution is committed to improving the
educational experience
• Helps students prepare
to enter “the profession”
• Enhances employment
opportunities
• Establishes eligibility for
financial aid and scholarships
3/29/2016 18
19. ABET Value
Institutions
• “Third-party” confirmation
of quality of programs
• Prestige, recognition by
“the profession”
• Attract the strongest students
• Acceptability of transfer credits
• Some external funding depends on
accreditation status
3/29/2016 19
20. ABET Value
Faculty
• Encourages “best practices” in
education
• Structured mechanisms
for self-improvement
• Institution is serious and
committed to improving
quality
– Facilities, financial resources,
training, etc.
3/29/2016 20
21. ABET Value
Industry
• Ensures educational
requirements to enter
“the profession” are met
• Aids industry in recruiting
– Ensures “baseline” of
educational experience
• Enhances mobility
• Opportunity to help guide
the educational process
– Program’s industrial advisory groups
– Professional, technical societies
3/29/2016 21
23. Readiness Review
• Required of all programs at institutions with no prior ABET
experience.
• Based on the Self-Study Report (SSR)and transcript of
program graduate.
• Request for Readiness Review (RREv) due Oct 1
• SSR +1 transcript per program due Nov 1
• Reviewed by ABET HQ Accreditation staff, members of the
Commission ExCom or designees. (Nov – early Feb)
• Provide recommendation to:
– Submit the RFE in the immediate upcoming accreditation review
cycle, addressing the REv suggestions, if any;
– Postpone the RFE submission unless substantive changes in the
Self-Study preparation and documentation are made; or
– Not submit the RFE in the immediate upcoming accreditation
review cycle because it is likely to be rejected.
3/29/2016 23
24. Context for Evaluation
• The Program Evaluator will perform an initial
evaluation BEFORE arriving on campus
– Evaluation centers on the evidence provided that
supports achievement of each of the criterion
– The SSR will be the primary evidence used in this
initial evaluation.
• The SSR provides the first impression of program to the
PEV and the only impression for the Readiness Review.
• The Program Evaluator will make adjustments to
his/her evaluation during the campus visit
– Interviews, display materials and tours will provide
the additional evidence.
3/29/2016 24
25. 25
The Accreditation Timeline
January
Institution requests
accreditation for
engineering programs
February - May
Institution prepares
self-evaluation
(Program Self-Study Report)
May - July
Team chairs assigned,
dates set, team members
chosen
September - December
Visits take place, draft statements written
and finalized following
7-day response period
January - February
Draft statements edited
and preliminary statements
sent to institutions
March - April
Institutions respond
to draft statement and
return to ABET w/i 30 days
May - June
Necessary changes,
if any, are made
July
EAC meets to take
final action
August
Institutions notified
of this action
Year 1 Year 2
3/29/2016
26. Terminology: Strengths and
Shortcomings
• Strength – stands above the norm
• Concern – program currently satisfies criterion, policy, or
procedure, however potential exists for the situation to
change such that the criterion, policy, procedure may not be
satisfied
– Working definition: criterion, policy, or procedure is fully met,
but there is potential for non-compliance in the near future
(duration of accreditation)
• Weakness – program lacks strength of compliance with
criterion, policy, or procedure
– Working definition: policy, or procedure is met to some
meaningful extent, but compliance is insufficient to fully satisfy
requirements
• Deficiency – program does NOT satisfy the criterion, policy,
or procedure
– Working definition: assigned to any criterion, policy, or
procedure that is totally or largely unmet
3/29/2016 26
27. Shortcomings vs. Accreditation
Action for a General Review
Shortcoming
Results of Evaluation
Weakness
No Yes Yes ----
Deficiency
No No No Yes
Type of Review Possible Actions
General NGR IR IV SC
Following a SC
NGR IR IV SC or NA
27
3/29/2016
28. Who is a Program Evaluator
(PEV)?
• A volunteer (one of more than 2,200 dedicated technical
professionals from academia, industry, and government)
• A member of one or more ABET member societies
– IIE for Industrial Engineering
– IEEE for Electrical Engineering
• May have academic or industry background
• Selected by the member society to represent ABET on program
evaluations
• Provide knowledge concerning professional practice, professional
preparation, and continuous improvement.
• Work with a team of colleagues from other professional societies to
evaluate the requested programs at an institution
• ABET experience may vary, but has extensive training conducted by
ABET and is evaluated after each visit using the ABET PEV Competency
Model.
3/29/2016 28
29. How are PEVs Selected?
• Using a Competency Model
– Technically Current
– Effective Communicator
– Professional
– Interpersonally Skilled
– Team-Oriented
– Organized
• Assigned to visit team by member society;
accepted by Team Chair and institution.
3/29/2016 29
Using a rubric
found on ABET
website
(www.abet.org)
30. Who is the Team Chair?
• A volunteer.
• A member of the Engineering Accreditation Commission (EAC) as a
representative of an ABET member society
• Nominated by the member society to represent ABET on the EAC
using a Team Chair Competency Model; approved by EAC and
appointed by the Board of Delegates.
• Assigned to visits by the Engineering Accreditation Commission
Executive Team.
• Provide knowledge concerning professional practice, professional
preparation, and continuous improvement.
• Lead a team of colleagues from appropriate professional societies to
evaluate the requested programs at an institution
• ABET experience may vary, but has extensive training conducted by
ABET and is evaluated after each visit using the ABET Team Chair
Competency Model.
3/29/2016 30
32. Team Chair Responsibilities
• Coordinate logistics for the visit with the
institution.
– Schedule
– Lodging, meals
• Prepare and coordinate the Program
Evaluators for the visit
• Lead team meetings before, during and after
the visit to arrive at consensus decisions.
• Meet with institution leaders during the visit:
– President, Provost, Registrar, CFO
– Others as needed
3/29/2016 32
33. PEV Responsibilities Before the
Visit
• PEV will complete initial evaluation compared to
criteria:
– Review the SSR
– Complete transcript analysis
– Review additional material provided by the program
• PEV will complete Pre-Visit Forms
• PEV will draft a plan for the visit
333/29/2016
34. PEV Responsibilities Before the
Visit: Review Self-Study
• Corrective actions taken after previous visit.
• All graduates meet graduation requirements
• Students meet minimum accreditation
requirements.
• Students took all courses in the proper order
(prerequisites/co-requisites).
• Identify potential program strengths and
shortcomings compared to the accreditation
policies, procedures, and criteria.
343/29/2016
35. PEV Responsibilities Before the
Visit: Transcript Analysis
• ABET recognizes transcripts as the official record
of student coursework
• The Program Evaluator will look for:
– Does the transcript identify the name of the degree in
a way that clearly identifies the program as an EAC of
ABET accredited program?
– Are courses counted toward the degree consistent
with the published requirements of the program?
– Are prerequisites taken before each course that
requires them?
– Are the number of transfer credits and number of
course substitutions excessive? 353/29/2016
36. PEV Responsibilities Before the
Visit: Review Additional Material
• What material?
– Catalogs and other publications provided by the
institution
– University, College, and program websites
• The Program Evaluator will look for:
– Additional information not provided in the SSR
– Consistency with the information provided in the SSR
– How the institution and program present themselves
to the public
363/29/2016
37. PEV Responsibilities During Visit:
Examine Assessment Materials
• Program Evaluator will:
– Verify the assessment plan for Student Outcomes as
described in the SSR
– Evaluate assessment processes:
• Are assessment processes adequate to determine
attainment of the Criteria?
• Are assessment processes robust enough to
identify program shortcomings?
• Are assessment processes sustainable?
• Will the assessment process lead to program
improvement?
373/29/2016
38. PEV Responsibilities During Visit:
Interviews
• Program Head
– Program leadership
– Program strengths and continuous improvement
• Faculty Members
– Particular course questions
– Teaching philosophy and activities to maintain currency
– Student advising and interaction
– Role in the assessment process
– Role in preparation for the visit
– Quality and maintenance of facilities
– Professional development
– Institutional support
383/29/2016
39. PEV Responsibilities During Visit:
Interviews
• Students
– Level of satisfaction/enthusiasm for the program
– Curriculum & course quality
– Advising – academic and career
– Adequacy of facilities
• Staff
– Level of satisfaction/enthusiasm for program
– Professional development
– Adequacy of resources
393/29/2016
40. PEV Responsibilities During Visit:
Review Facilities
• Labs/Design Studios focused on undergraduate
curriculum.
– Sufficient number and size
– Appropriate coverage across the breadth of
specializations within the program
– Appropriate equipment, in good repair
– Appropriate student access outside scheduled lab
times.
– Appropriate technical and instructional support
– Safe physical arrangement and appropriate safety
practices in place
403/29/2016
41. PEV Responsibilities During Visit:
Review Facilities
• Classrooms
– Appropriate physical arrangement, support for
educational technologies, etc.
– Not overcrowded
• Faculty offices
– Sufficient size, privacy
– Access to computing resources
413/29/2016
42. PEV Responsibilities During Visit:
Review Facilities
• Support facilities
– Computing resources
• Sufficient number & access
• Software
• Support personnel
– Appropriate spaces for students to gather (not an explicit
criterion but relates to several criteria)
– Appropriate shop with parts, repair facilities, etc. (as
appropriate to the discipline)
• Plan for on-going facilities maintenance, repair and
upgrade
– PROCESS in place to ensure facilities remain up-to-date,
support PEO’s and are safe
423/29/2016
43. What is a Process?
The ABET Criteria include the word process in several locations and
implies it in several others.
A process is a series of sequenced activities that convert inputs
(materials, information/data, people, machines/equipment) into outputs
to satisfy customer requirement/need.
43
ProcessMaterial Outputs
Customers
Info/Data
People
Machines/
Equipment
Therefore, whenever you see Process in the criteria, you need to
specify the activities, activity sequence & timing and roles &
responsibilities that make up that process.
3/29/2016
44. PEV Responsibilities During Visit:
Visit Support Areas
• Library
• Adequate resources for faculty & students
• Adequate hours
• Supporting departments (Mathematics, Physics,
Chemistry, English, etc.)
• Advisors
• Career support center
• Cooperative education/Internship office
• Etc.
The Team will share responsibilities for visiting
support areas
443/29/2016
45. Typical Visit Agenda
• Sunday
– Initial team meeting
– Visit campus to evaluate materials and tour facilities
– Team meeting
• Monday
– Team meeting with President/Dean
– Meet with program head, faculty, students, and staff
– Visit supporting areas
– Team meeting
– Draft Exit Statement
453/29/2016
46. Typical Visit Agenda
• Tuesday
– Complete interviews, facility tours, and material review
– Debrief program head and Dean on strengths and
shortcomings
– Team meeting to finalize evaluation
• Complete Visit Report and Exit Statement
• Team review and preliminary recommendation
– Exit Meeting with President, Provost, Dean and designated
guests.
• Each PEV will read statement of findings for their assigned
program.
• Team will leave behind a Program Audit Form (PAF)
summarizing findings for each program evaluated.
463/29/2016
47. Responsibilities After the Visit
• Team Chair will combine exit statements for each
program into one Draft Statement.
• Team will complete online Team Chair and Peer PEV
Performance Appraisal Forms
• Institution representatives requested to complete online
Team Chair and PEV Performance Appraisal Forms.
• PEVs will review Draft Statement written by Team Chair.
• PEVs will review Due Process materials provided by
institution and consult with Team Chair on Final
Statement.
• PEVs will consult with Team Chair on recommended
accreditation action.
473/29/2016
48. How Can You Help the PEV?
• Provide clear, concise, consistent responses to Self-Study
questions; Use current version of the template.
– Quality Not Quantity helps the PEV identify appropriate
evidence; use tables/graphs where appropriate
– ANSWER THE QUESTIONS!
• Provide supporting documentation for each transcript
– Include Registrar accepted degree audit form/checklist for each
transcript with description of waivers, substitutions, transfers
– Provide copies of earlier curricula and pre-requisite flowcharts if
applicable to transcripts
– Do NOT include transcripts in body of SSR.
• Organize / label display materials so it is easy to locate
materials
• Support PEV and Team agenda / schedule
483/29/2016
51. Criteria are Quality Management
System
51
Measurement,
Analysis &
Improvement
Resource
Allocation
Management
Responsibility
Product / Service
RealizationRqmts
Product /
Service
Measurement,
Analysis &
Improvement
Resource
Planning
Management
Responsibility
Satisfaction
Customer Customer
Continual improvement of quality management system
3/29/2016
52. Changes
• Changes can occur before your visit in the
following documents:
– Accreditation Policy & Procedures Manual
– Self-Study Questionnaire
– Criteria, including Program Criterion and Definitions
– Interpretations
• Changes are posted on the ABET website and sent
via ENEWS.
• Dean should attend Institutional Rep training and
Luncheon at the July Commission Meeting
preceding the visit.
523/29/2016
53. Self-Study Questionnaire
• Self-Study Questionnaire
– Follow the template format as much as possible
and include ALL tables in the template.
– Remove instructions from the document
– May include additional tables and/or graphs as
needed to best document how the program
meets the criteria.
• Tables/graphs often summarize information in less
space (“Picture is worth a thousand words.”)
– Questions focus on describing processes:
• Sequence of steps
• Timing
• Responsibility
533/29/2016
54. Criterion 1: Students
• Admission, acceptance of credits from other
institutions
• Advising regarding curricular & career matters
– Have and enforce registration procedures regarding
pre- and co-requisites, course substitutions
• Have and enforce procedures to ensure all
graduates meet graduation requirements.
– Degree audit
– Supporting documentation on file
3/29/2016 54
55. Criterion 2
The program must have:
• Published PEO’s consistent with mission,
needs of constituents and the criteria.
• A documented and effective process,
involving constituents, for the periodic
review and revision of the PEO’s
553/29/2016
56. PEO Issues
• Contain Student Outcomes language
• Focus on the program and not graduates.
• Frequently too many
• Language imprecise, e.g.,
– ‘are capable of’
– ‘are equipped with’
– ‘have the attitude and —’
– ‘have good or a solid understanding of’
• Large number of constituents, many not involved in
establishing the PEO’s, nor in subsequent reviews and
revisions.
• ‘what graduates are expected to attain’ is much broader
than ‘career and professional accomplishments’
563/29/2016
57. PEO Highlights
1. The process needs to document and demonstrate that
the PEO’s are based on constituent needs which were
determined by involving them in some manner.
List the needs and show linkage to PEOs
2. They are also to be reviewed and revised as needed.
3. If you survey the alumni in order to capture
information about your graduates, you can potentially
use the results for a continuous improvement action.
573/29/2016
58. Sample PEO Process Involving
Constituents*
3/29/2016 58
Input Method Schedule Constituent
Alumni survey Every three years Alumni 2-5 years out
Employer focus group Every two years during
Career Fair
Employers (and recruiters);
some are alumni
Senior exit interview Annually Students; retrospective
discussion of PEOs and
their intended career paths
Advisory Council discussions As needed—available
annually
Industrial representatives,
employers, alumni
Curriculum Committee
meetings
Available as frequently as
needed
Faculty and students
*From Upper State University mock self-study, ABET PEV training, 2011.
Not specific- may raise questions
59. Criterion 2 & 4 Language
• Even though programs are no longer
required to assess and evaluate their
PEO’s, they must still conform to the PEO
definition and not appear to be
Outcomes!
593/29/2016
60. PEO Issues
• Do the published PEO’s meet the definition?
• Are they really broad statements that describe what the
graduates are expected to attain within a few years?
• Can the program convince the team that the PEO’s
are consistent with constituent needs?
• There is NO language that insists on constituent
approval, however there must be involvement!
• Is there a documented and effective process,
involving program constituencies, for the periodic
review and revision of PEOs?
603/29/2016
61. Scenario A: Are these PEOs?
Are they really broad statements that describe what graduates
are expected to attain within a few years of graduation?
Graduates of the program will have:
• A solid understanding of the basic principles of
mathematics, science, and engineering and the technical
competency to use the techniques, skills and modern tools
for practice in engineering as well as for graduate
education.
• The ability to work in a team and develop problem-solving
skills that include oral and written communication skills to
effectively communicate technical and professional
information.
613/29/2016
No, they are not really PEOs, but rather reworded
student outcomes
62. Scenario B: Are these PEOs?
Are they really broad statements that describe what graduates
are expected to attain within a few years of graduation?
Graduates of the culinary engineering program are expected
within a few years of graduation to have:
1. Established themselves as practicing professionals or be
engaged in advanced study in culinary engineering or a
related area.
2. Demonstrated their ability to work successfully as a member
of a professional team and function effectively as responsible
professionals.
623/29/2016
Yes, they describe what graduates are expected to
attain a few years after graduation
63. Criterion 2 FAQ’s
• What if the PEO’s really sound like outcomes (instead of
objectives)?
– If PEO’s are not PEO’s, there will be a Criterion 2 shortcoming.
• What if PEO’s are ambiguous or reflect outcomes retooled to
apply after graduation?
– Becomes a team judgment – do they meet the intent of the
Criterion?
• What if there is no process for determining the needs of the
program’s constituents?
– If the PEOs do not incorporate constituents’ needs, there will
be a Criterion 2 shortcoming.
633/29/2016
64. Criterion 3-Outcomes Definitions
Current Definition: Student Outcomes describe what
students are expected to know and be able to
do by the time of graduation. These relate to
the skills, knowledge, and behaviors that students
acquire as they progress through the program.
643/29/2016
65. Criterion 3: Student Outcomes
• The program must have documented
student outcomes that prepare graduates
to attain the program educational
objectives.
653/29/2016
66. Criterion 3: Student Outcomes
• Student outcomes are defined as (a) – (k) for
engineering plus any additional ones articulated by the
program
• The program must demonstrate that the engineering
criteria (a) – (k) are attained to some extent.
– The assessment and evaluation process that periodically
documents and demonstrates the degree to which outcomes are
attained is in Criterion 4.
• Student outcomes must foster attainment of the PEOs
– Must describe the relationship between SOs and PEOs
in the SSR.
2016-17 Student Outcomes
663/29/2016
67. Criterion 3:Student Outcomes
• The definition of student outcomes are (a) – (k) plus locally
articulated ones
– Some programs don’t have their student outcomes expressed as (a)
– (k). They may have identified their own set of outcomes. As long
as the program demonstrates coverage of all elements of (a) – (k)
in its own outcomes, this part of the criterion is met.
– If additional outcomes beyond (a)-(k) are identified, they
MUST be assessed (Criterion 4)
– Assessment and evaluation of Student Outcomes is in
Criterion 4.
Changes to Criterion3, in conjunction with changes to
Criterion 5 are out for public comment.
• Reduces number of required Student Outcomes to 6, covering 5
categories.
• Earliest implementation, if approved, would be 2017-18 cycle,
with a possible phase-in period.
• Check ABET website (www.abet.org) periodically for updates.
• Proposed Student Outcomes
673/29/2016
68. Criterion 4: Continuous
Improvement
• The program must regularly use appropriate,
documented processes for evaluating the extent to
which the student outcomes are being attained. The
results of these evaluations must be utilized as
input for the continuous improvement of the
program. Other available information may also be
used to assist in the continuous improvement of the
program.
683/29/2016
69. Criterion 4 Components
• Criterion 4 essentially contains two
components:
1. Process(es) for assessment and evaluation
of the extent of attainment of each of the
Student Outcomes, and
2. Actions taken to improve the program,
regardless of how information/data obtained
This is a closed loop Corrective Action process.
3/29/2016 69
70. Assessment
ABET defines effective assessment as:
“Effective assessment uses relevant direct, indirect,
quantitative and qualitative measures as
appropriate to the outcome being measured.
Appropriate sampling methods may be used as
part of an assessment process.”
703/29/2016
71. Criterion 4: Continuous
Improvement
• The process of assessment and evaluation needs to
demonstrate the degree to which student outcomes are
attained, however …
– There is NO language that says all student outcomes must
be attained to the same degree or be measured on a
numerical scale
– There is NO language that says assessment must be done
in every course, every student or every semester.
• Many of the student outcomes contain multiple
aspects that may not be possible to assess with one
instrument. Be sure to define each aspect and
assess accordingly. (see example for SO (e) on
upcoming slide).
713/29/2016
72. Student Outcomes Assessment:
SSQ Text
1. A listing and description of the assessment processes used
to gather the data upon which the evaluation of each
student outcome is based. Examples of data collection
processes: specific exam questions, student portfolios,
internally developed assessment exams, senior project
presentations, nationally-normed exams, oral exams, focus
groups, industrial advisory committee meetings, or other
processes that are relevant and appropriate to the program
2. The frequency with which these assessment processes
are carried out
3. The expected level of attainment for each of the
student outcomes
723/29/2016
73. Student Outcomes Assessment:
SSQ Text
4. Summaries of the results of the evaluation process
and an analysis illustrating the extent to which each of
the student outcomes is attained
5. How the results are documented and maintained
733/29/2016
74. Student Outcomes Assessment
• What is adequate data?
– Does it all have to be objective/direct? (NO)
– Can it be subjective? (Some of it may be; nothing
says it cannot)
– Is the observation or conclusion of course instructor
adequate? (What was his or her basis for the
observation?)
– Does evidence for each student outcome have to be
in the form of work the student has produced? (No,
however, the PEV & ultimately the team, needs to be
convinced that outcome attainment has been
demonstrated.)
743/29/2016
75. Student Outcome Assessment
Issues
• Excessive number of student outcomes
supported in a single course
– All 11 in the major design experience is not
credible or sustainable
• Course grades used as basis for assessment
• Design of Experiments (Outcome b)
– Students never actually ‘design’ an experiment
and then ‘run’ it to see if the design worked.
• Confusing course assessment with outcome
assessment.
753/29/2016
76. Simple Student Outcome
Assessment Process
• Major design experience for engineering
programs:
– a- ability to apply knowledge of math, science
and engineering
– c- design a system, component, process
– d- multi-disciplinary teams
– e- formulate & solve engineering problems
– g- communicate
• FE Exam for f – ethics
• 5 or more outcomes remain that need to be
addressed
763/29/2016
77. Sample SO Assessment Process*
77
*From Upper State University mock self-study, ABET PEV training, 2011.
3/29/2016
78. Sample SO Assessment Frequency*
3/29/2016 78
*From Upper State University mock self-study, ABET PEV training, 2011.
Student Outcome 2005 2006 2007 2008 2009 2010
a. an ability to identify, formulate, and solve
engineering problems
X X
b. an ability to apply knowledge of mathematics,
science, and engineering X X
c. an ability to use the techniques, skills, and
modern engineering tools necessary for
engineering practice.
X X
d. an ability to design and conduct experiments,
as well as to analyze and interpret data
X X
e. an ability to design a system, component, or
process to meet desired needs within realistic
constraints such as economic, environmental,
social, political, ethical, health and safety,
manufacturability, and sustainability
X X
f. an ability to function on multi-disciplinary
teams
X X
g. an understanding of professional and ethical
responsibility
X X
h. an ability to communicate effectively, both
orally and in writing
X X
i. the broad education necessary to understand
the impact of engineering solutions in a
global, economic, environmental, and societal
context
X X
j. a recognition of the need for, and an ability to
engage in life-long learning
X X
k. a knowledge of contemporary issues X X
l. a willingness to assume leadership roles and
responsibilities
X X
79. Sample Assessment for Student
Outcome e*
(an ability to identify, formulate and solve engineering problems)
Performance
Indicators
Method(s) of
Assessment
Where data
are collected
(summative)
Length of
assessment
cycle (yrs)
Year(s) of
data
collection
Target for
Performance
1) Problem
statement
shows
understanding
of the problem
Faculty
assessment of
design problem
statement
EGR 4090
3 years 2007, 2010 90%
Senior Survey On-line survey
2) Solution
procedure and
methods are
defined.
Faculty
assessment of
senior project
plan
EGR 4090
3 years 2007, 2010 85%
Senior Survey On-line survey
3) Problem
solution is
appropriate and
within
reasonable
constraints
Faculty
assessment of
senior design
solution
EGR 4090
3 years 2007, 2010 80%
Senior Survey On-line survey
79
*From Upper State University mock self-study, ABET PEV training, 2011.
3/29/2016
80. Sample Assessment Analysis &
Evaluation for Student Outcome e*
(an ability to identify, formulate and solve engineering
problems)
3/29/2016 80
Assessment Results (direct measures) 2005: For the summative assessment (end
of program), the decision was made to focus on the faculty’s direct assessment for
all indicators.
*From Upper State University mock self-study, ABET PEV training, 2011.
81. Continuous Improvement
Common Issues
• Linkages between assessment and CI
actions not documented.
• Loop not closed between assessment and
actions taken to improve the program.
– For every student outcome not attained, a
corresponding action should be identified
(even if still in-progress)
– Sense of urgency lacking
3/29/2016 81
83. Criterion 4 Continuous Improvement
Closed Loop Process Control
3/29/2016 83
Suppliers Process Customers
Process
Management
and Improvement
Supplier
Measures
Customer
Feedback
Input
Measures
Output
Measures
Process
MeasuresProcess
Changes
High Schools
Other Programs at Institution
Other Institutions
Student
Performance on
entrance exams
Employers
Alumni
Graduate Programs
Institution
Program
Curriculum
Outcomes
Objectives
Course & Outcome
Assessment/CI
Facilities, Faculty,
Resources
Student
Monitoring &
Advising
84. Criterion 5:Curriculum
2 Elements
1. Professional Component:
a) 1 year combination of college level mathematics
and basic science (some with experimental
experience) appropriate to the discipline.
Proposed Criterion 5 Definitions
a) 1.5 years of engineering topics, consisting of
engineering sciences and engineering design
appropriate to the field of study.
b) General education component that complements the
technical content of the curriculum and is consistent
with program and institution objectives.
843/29/2016
85. Criterion 5:Curriculum
2 Elements
2. Curriculum culminates in a major design experience
based on the:
a) knowledge and skills acquired in earlier course work, and
b) incorporates appropriate engineering standards and realistic
constraints.
Changes to Criterion 5, in conjunction with changes to
Criterion 3 have been proposed and are out for public
comment.
853/29/2016
86. Criterion 5:Curriculum
Common Issues
• Split of an Engineering Course between Math/Basic
Science and Engineering Topics categories
• Major Design Experience
– Must be based on knowledge and skills acquired in earlier
coursework
– And, incorporate engineering standards and multiple
realistic constraints (project reports should identify them
and show use)
• Common courses across programs (i.e. statics,
dynamics, circuits, engineering economy) categorized
differently by different programs.
• Project management and computer programming
courses categorized as engineering science or design
– Should be categorized as ‘Other’
863/29/2016
87. • Suggestion: Include table for projects associated with summited transcripts:
(ME sample)
• OR, include summary table in SSR for all projects completed previous
course cycle.
• OR, ask students to include similar table in their project report.
Student #1 #2 #3-6
Project Title & Area
Thermal Systems
Mechanical Systems
Constraints
Economic
Environmental
Sustainability
Manufacturability
Ethical
Health and Safety
Social
Political
Other
Standards
?
?
Standards & Constraints
873/29/2016
88. Criterion 6: Faculty
• Sufficient number to:
– achieve program educational objectives and student outcomes,
– deliver curriculum for students to graduate in a timely manner
– achieve adequate levels of student-faculty interaction,
– provide student advising and counseling,
– Include university service,
– provide time for professional development, and
– interact with industrial and professional partners.
• Make sure description, Tables 6-1 and 6-2 AND faculty Vitae (in
Appendix B) are consistent.
• Competent to cover all curricular areas of program.
– Include a table of faculty by curricular area.
• Authority for creation, delivery, evaluation, modification
and continuous improvement of the program.
– Should align with the description of the CI process
3/29/2016 88
89. Criterion 7: Facilities
• Adequate to support educational objectives and
student outcomes of the program.
• Fosters faculty-student interaction
• Encourages professional development &
professional activities, and
• Provides opportunities to use modern
engineering tools.
• APPM II.G. 6.b.(1): Safe
3/29/2016 89
90. Criterion 8: Support
• Sufficient to attract, retain, and provide for
continued professional development of
faculty.
• Sufficient to acquire, maintain, and operate
facilities & equipment appropriate for the
program.
• Constructive leadership
• Consider adding a table demonstrating
budget stability since previous
evaluation/initiation of program. (table no
longer required in SSQ)
3/29/2016 90
91. Program Criteria
• Program Criteria for almost all programs
have 2 elements:
1. Curriculum
2. Faculty
Note: this is no longer identified as Criterion 9.
913/29/2016
92. Program Criteria Curriculum
Aspects
• If add as Student Outcomes, MUST assess.
– Sage advice: do NOT convert program criteria into SOs.
• Simply demonstrate how addressed in the
curriculum by providing specific examples.
• May be impacted by proposed changes to Criterion 3
and Criterion 5.
923/29/2016
93. Program Criteria:
Key Curriculum Elements
• IE: integrated systems; analytical,
computational, and experimental
practices.
• EE: analyze & design complex electrical
and electronic devices, software, and
systems containing hardware and software
components
• ME: work professionally in either thermal
or mechanical systems
3/29/2016 93
94. Program Criteria:
Key Faculty Elements
• IE: understand professional practice and
maintain currency in their respective
professional areas
• EE: none
• ME: maintain currency in their specialty
area
3/29/2016 94
95. APPM Requirements
• II.A.1 – represent the accreditation status of each
program accurately and without ambiguity.
• II.A.6 – Each accredited program must be specifically
identified as “accredited by the _____ Accreditation
Commission of ABET, http//www.abet.org.”
• II.A.6.a – Each ABET accredited program must
publically state the program’s educational objectives
and student outcomes.
• II.A.6.b - Each ABET accredited program must
publically post annual student enrollment and
graduation data per program.
• II.G.6.b – Examine facilities – to assure the
instructional and learning environments are adequate
and are safe for the intended purposes.
3/29/2016 95
98. Student Outcomes
• Engineering programs must demonstrate that their graduates have:
a) An ability to apply knowledge of mathematics, science and
engineering appropriate to the discipline
b) An ability to design and conduct experiments, analyze and interpret
data
c) An ability to design a system, component, or process to meet desired
needs
d) An ability to function on multi-disciplinary teams
e) An ability to identify, formulate, and solve engineering problems
f) An understanding of professional and ethical responsibility
g) An ability to communicate effectively
h) The broad education necessary to understand the impact of
engineering solutions in a societal context
i) A recognition of the need for, and an ability to engage in life-long
learning
j) A knowledge of contemporary issues
k) An ability to use the techniques, skills, and modern engineering tools
necessary for engineering practice
3/29/2016 99
99. Proposed Student Outcomes
1) An ability to identify, formulate, and solve engineering problems by
applying principles of engineering, science, and mathematics.
2) An ability to apply both analysis and synthesis in the engineering design
process, resulting in designs that meet desired needs.
3) An ability to develop and conduct appropriate experimentation, analyze
and
4) interpret data, and use engineering judgment to draw conclusions.
5) An ability to communicate effectively with a range of audiences.
6) An ability to recognize ethical and professional responsibilities in
engineering situations and make informed judgments, which must
consider the impact of engineering solutions in global, economic,
environmental, and societal contexts.
7) An ability to recognize the ongoing need for additional knowledge and
locate, evaluate, integrate, and apply this knowledge appropriately.
8) An ability to function effectively on teams that establish goals, plan tasks,
meet deadlines, and analyze risk and uncertainty
3/29/2016 100
100. Proposed Criterion 5 Definitions
• College-level Mathematics consists of mathematics above pre-calculus
level.
• Basic Sciences consist of chemistry and physics, and other
biological, chemical, and physical sciences, including astronomy,
biology, climatology, ecology, geology, meteorology, and oceanography.
• Engineering Science is based on mathematics and basic sciences but
carry knowledge further toward creative application needed to solve
engineering problems.
• Engineering Design is the process of devising a system, component, or
process to meet desired needs, specifications, codes, and standards
within constraints such as health and safety, cost, ethics, policy,
sustainability, constructability, and manufacturability. It is an iterative,
creative, decision-making process in which the basic sciences,
mathematics, and the engineering sciences are applied to convert
resources optimally into solutions.
• Teams consist of more than one person working toward a common
goal and may include individuals of diverse backgrounds, skills, and
perspectives.
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