The document discusses outcome-based education (OBE) and continuous quality improvement (CQI). It defines OBE as focusing on student learning outcomes by stating expected learning outcomes, providing learning activities to achieve them, and assessing student achievement. The document also discusses issues in higher education that OBE addresses, the characteristics of OBE curricula including program objectives and outcomes, implementation of OBE programs, and assessment tools and how they feed into CQI.
Outcome Based Education and Continuous Quality Improvement in HEIsMd. Nazrul Islam
After completion of the presentation the participants will be able to know :
- Issues in Higher Education, Teaching & Learning
- Why Outcome-based Education?
- What is the Washington Accord?
- Outcome-based Education
- Implementation of OBE
- Characteristics of OBE Curriculum
- Operation Models of OBE
- Program Objectives
- Program Outcomes
- Learning Outcomes
- Assessment Issues and Tools
- Continual Quality Improvement
Generating smart goals is very essential for the development of engineering programs, improving the attributes of the graduates and faculty development.
Prospective Student Web Content Team - University of Edinburgh intro sessionNeil Allison
Introductory presentation and workshop organised by the University of Edinburgh's new Prospective Student Web Content Team. Sessions run for University staff involved in web marketing, recruitment and admissions during December 2019.
Outcome Based Education and Continuous Quality Improvement in HEIsMd. Nazrul Islam
After completion of the presentation the participants will be able to know :
- Issues in Higher Education, Teaching & Learning
- Why Outcome-based Education?
- What is the Washington Accord?
- Outcome-based Education
- Implementation of OBE
- Characteristics of OBE Curriculum
- Operation Models of OBE
- Program Objectives
- Program Outcomes
- Learning Outcomes
- Assessment Issues and Tools
- Continual Quality Improvement
Generating smart goals is very essential for the development of engineering programs, improving the attributes of the graduates and faculty development.
Prospective Student Web Content Team - University of Edinburgh intro sessionNeil Allison
Introductory presentation and workshop organised by the University of Edinburgh's new Prospective Student Web Content Team. Sessions run for University staff involved in web marketing, recruitment and admissions during December 2019.
WBL IN ACTION Event Slides Feb. 17, 2015innovatetk
Educators, work-based learning partners, superintendents, and those involved in Linked Learning get together to adopt and celebrate common definitions and effective practices along the College & Career Continuum for Tulare and King Counties. www.innovatetk.com/wbl-in-action
Class project for EdTech 501
A sample Technology Use Plan for a fictional school as a ppt presentation to a school and community team as an educational technician
Creating a coherent performance indicator framework for the higher education ...Sonia Whiteley
The Australian Government recently made an ongoing commitment to a suite of innovative, integrated surveys that collect data about students’ experiences of their higher education from the commencement of their qualification to employment. The Quality Indicators for Learning and Teaching (QILT) survey program includes the Students Experience Survey, the Graduate Outcomes Survey, and the Employer Satisfaction Survey. All higher education institutions offering undergraduate and postgraduate courses in Australia, which includes 40 universities and around 105 private providers, are in-scope for the collection.
The QILT measures will work together to provide a coherent insight into student engagement, the student experience and post-study outcomes. The challenges of meeting this broad range of requirements to deliver an indicator framework that provides timely evidence for institutions to improve the experiences of current and future students and to position themselves in the higher education landscape will be discussed.
CT/ACE Collections Management Traineeship ProgrammeNicholas Poole
An introduction to the joint Arts Council England/Collections Trust Collections Management Traineeship Programme for employers and prospective candidates.
Have a look at a presentation from the Workshop in Nice which was organised within the TRIGGER project (project number: 2617309-EPP-1-2020-1-SK-EPPKA2-CBHE-JP). The aim of the project is to improve conditions at universities in Central Asia and to educate students in an innovative way so they acquire the skills needed for today's job market. In this presentation Côte d'Azur University will take you through planning, managing, and promotion of graduates employability in cooperation with employers and will introduce different services to support the students in this regard.
WBL IN ACTION Event Slides Feb. 17, 2015innovatetk
Educators, work-based learning partners, superintendents, and those involved in Linked Learning get together to adopt and celebrate common definitions and effective practices along the College & Career Continuum for Tulare and King Counties. www.innovatetk.com/wbl-in-action
Class project for EdTech 501
A sample Technology Use Plan for a fictional school as a ppt presentation to a school and community team as an educational technician
Creating a coherent performance indicator framework for the higher education ...Sonia Whiteley
The Australian Government recently made an ongoing commitment to a suite of innovative, integrated surveys that collect data about students’ experiences of their higher education from the commencement of their qualification to employment. The Quality Indicators for Learning and Teaching (QILT) survey program includes the Students Experience Survey, the Graduate Outcomes Survey, and the Employer Satisfaction Survey. All higher education institutions offering undergraduate and postgraduate courses in Australia, which includes 40 universities and around 105 private providers, are in-scope for the collection.
The QILT measures will work together to provide a coherent insight into student engagement, the student experience and post-study outcomes. The challenges of meeting this broad range of requirements to deliver an indicator framework that provides timely evidence for institutions to improve the experiences of current and future students and to position themselves in the higher education landscape will be discussed.
CT/ACE Collections Management Traineeship ProgrammeNicholas Poole
An introduction to the joint Arts Council England/Collections Trust Collections Management Traineeship Programme for employers and prospective candidates.
Have a look at a presentation from the Workshop in Nice which was organised within the TRIGGER project (project number: 2617309-EPP-1-2020-1-SK-EPPKA2-CBHE-JP). The aim of the project is to improve conditions at universities in Central Asia and to educate students in an innovative way so they acquire the skills needed for today's job market. In this presentation Côte d'Azur University will take you through planning, managing, and promotion of graduates employability in cooperation with employers and will introduce different services to support the students in this regard.
Similar to outcomebasededucation-191011150604.pptx (20)
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
Image segmentation is a computer vision task that involves dividing an image into multiple segments or regions, where each segment corresponds to a distinct object, region, or feature within the image. The goal of image segmentation is to simplify and analyze an image by partitioning it into meaningful and semantically relevant parts. This is a crucial step in various applications, including object recognition, medical imaging, autonomous driving, and more.
Key points about image segmentation:
Semantic Segmentation: This type of segmentation assigns each pixel in an image to a specific class, essentially labeling each pixel with the object or region it belongs to. It's commonly used for object detection and scene understanding.
Instance Segmentation: Here, individual instances of objects are separated and labeled separately. This is especially useful when multiple objects of the same class are present in the image.
Boundary Detection: Some segmentation methods focus on identifying the boundaries that separate different objects or regions in an image.
Methods: Image segmentation can be achieved through various techniques, including traditional methods like thresholding, clustering, and region growing, as well as more advanced techniques involving deep learning, such as using convolutional neural networks (CNNs) and fully convolutional networks (FCNs).
Challenges: Image segmentation can be challenging due to variations in lighting, color, texture, and object shape. Overlapping objects and unclear boundaries further complicate the task.
Applications: Image segmentation is used in diverse fields. For example, in medical imaging, it helps identify organs or abnormalities. In autonomous vehicles, it aids in identifying pedestrians, other vehicles, and obstacles.
Evaluation: Measuring the accuracy of segmentation methods can be complex. Metrics like Intersection over Union (IoU) and Dice coefficient are often used to compare segmented results to ground truth.
Data Annotation: Creating ground truth annotations for segmentation can be labor-intensive, as each pixel must be labeled. This has led to the development of datasets and tools to facilitate annotation.
Semantic Segmentation Networks: Deep learning architectures like U-Net, Mask R-CNN, and Deeplab have significantly improved the accuracy of image segmentation by effectively learning complex patterns and features.
Image segmentation plays a fundamental role in understanding and processing images, enabling computers to "see" and interpret visual information in ways that mimic human perception.
Image segmentation is a computer vision task that involves dividing an image into meaningful and distinct segments or regions. The goal is to partition an image into segments that represent different objects or areas of interest within the image. Image segmentation plays a crucial role in various applications, such as object detection, medical imaging, autonomous vehicles, and more.
Support Vector Machine (SVM) is a popular supervised machine learning algorithm used for classification and regression tasks. It works by finding a hyperplane in a high-dimensional space that best separates data points of different classes. SVM aims to maximize the margin between the classes, where the margin is defined as the distance between the hyperplane and the nearest data points from each class. The data points that are closest to the hyperplane are called support vectors.
Here are some key concepts associated with SVM:
Hyperplane: In a two-dimensional space, a hyperplane is a line that separates the data points of different classes. In higher dimensions, it becomes a hyperplane. SVM tries to find the hyperplane with the maximum margin between classes.
Margin: The margin is the distance between the hyperplane and the nearest data points of each class. SVM seeks to maximize this margin.
Support Vectors: These are the data points that are closest to the hyperplane and have the most influence on determining its position. These points "support" the placement of the hyperplane.
Kernel Trick: SVM can be extended to non-linearly separable data using the kernel trick. A kernel function takes the original feature space and maps it to a higher-dimensional space where the data might be linearly separable. Common kernel functions include the linear kernel, polynomial kernel, and radial basis function (RBF) kernel.
C parameter: In SVM, the C parameter is a regularization parameter that balances the trade-off between maximizing the margin and minimizing the classification error. A small C value allows for a larger margin but may lead to more misclassifications, while a large C value prioritizes correct classification over margin maximization.
SVM can be used for both classification and regression tasks:
Classification: In classification, SVM tries to find a hyperplane that separates data points into different classes. New data points can then be classified based on which side of the hyperplane they fall.
Regression: In regression, SVM is used to find a hyperplane that best fits the data points. The goal is to minimize the error between the actual and predicted values.
SVMs have been widely used in various fields such as image classification, text categorization, bioinformatics, and more. However, they can be sensitive to the choice of hyperparameters and might not perform well on extremely noisy or overlapping data.
It's important to note that while SVMs are a powerful and versatile algorithm, newer algorithms like deep learning models have gained popularity due to their ability to automatically learn complex features and patterns from data.
Support Vector Machine (SVM) is a powerful machine learning algorithm for classification and regression. It finds a hyperplane that best separates data into classes, aiming to maximize the margin between them. Support vectors, the closest data points to the hyperplane, influence its position.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
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Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2. • Issues in Higher Education, Teaching & Learning
• Why Outcome-based Education?
• What is Washington Accord?
• Outcome-based Education
• Implementation of OBE
• Characteristics of OBE Curricular
• Operation Models of OBE
• Programme Objectives
• Programme Outcomes
• Learning Outcomes
• Assessment Issues and Tools
• Continual Quality Improvement
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Overview
3. Issues In Higher Education
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• Accountability towards students – fulfilling
requirements of the curriculum
• Satisfying needs of industry – unemployed
graduates
• Maintaining academic standards –
unaccredited programmes
• Accountable to grant providing organizations
– stakeholders
• Accreditation – outcome based education
4. Issues In Teaching And Learning
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• Student intake – qualification, quantity
• Staff – Qualification, competency
• Teaching process – transparent, control
• Assessment - outcomes
• Courses – up to date, relevant
• Facilities – sufficient, up to date,
5. 5
Why Outcome-based Education?
• To fulfill the requirements of EAC*, BEM*,
Washington Accord
– BEM registers graduates and professional engineers
– Programmes attain standard comparable to global practice,
hence accreditation required
– EAC is the body delegated by BEM
– requires elements of outcomes in engineering curriculum to
ensure CQI culture in the spirit of OBE.
• It is a natural way of what higher level education
should be based on
*EAC – Engineering Accreditation Council
10/11/20*1B9EM – Board of Engineers Malaysia
www.nursingpath.in
6. Accreditation Objective
• …graduates of accredited programme satisfy
minimum requirement for registration with
BEM / IEM
• …ensures CQI is being practiced
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7. What’s Washington Accord?
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• an international accreditation agreement for
professional engineering academic degrees,
• established in 1989, the signatories as of 2007 are
Australia, Canada, the Republic of Ireland, Hong
Kong, Japan, New Zealand, Singapore, South Africa,
South Korea, Taiwan, the United Kingdom and the
United States.
• recognizes that there is substantial equivalency of
programs accredited by those signatories.
• graduates of accredited programs in any of the
signatory countries are recognized by the other
signatory countries as having met the academic
requirements for entry to the practice of
engineering.
8. What’s Washington Accord? (cont…)
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• The following countries have provisional signatory
status and may become member signatories in the
future:
– Germany
– India
– Malaysia
– Russia
– Sri Lanka
9. OBE addresses the following key
questions:
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• What do you want the students to have or able
to do?
• How can you best help students achieve it?
• How will you know what they have achieved it?
• How do you close the loop
10. OBE addresses the following key
questions: (cont...)
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• Who are our stakeholders?
• What services do we provide?
• Do constituencies understand our objectives?
• What services, facilities and policies must be
present?
• How do we measure our results?
• How do we use these results for CQI?
• Are we achieving our objectives and
improving?
• Are our constituencies satisfied?
11. Outcome-based Education
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Focuses on student learning by:
• Using learning outcome statements to make explicit
what the student is expected to be able to know,
understand or do;
• Providing learning activities which will help the
student to reach these outcomes;
• Assessing the extent to which the student meets
these outcomes through the use of explicit
assessment criteria.
13. 13
Implementation of OBE Program
• Effective Program Educational Objectives.
• Effective Program Outcomes.
• Practical Assessment Tools.
• Effective Assessment Planning.
• Robust Evaluation Planning.
• CQI procedures in place
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14. 14
Characteristics of OBE curricula
• It has program objectives, program
outcomes, course outcomes and performance
indicators.
• It is objective and outcome driven, where
every stated objective and outcomes can be
assessed and evaluated.
• It is centered around the needs of the
students and the stakeholders.
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15. Characteristics of OBE curricula (cont…)
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• Every learning outcome is intentional and
therefore the outcomes must be assessed
using suitable performance indicators.
• Program objectives address the graduates
attainment within 3-5 years after their
graduation.
• Program outcomes, which consist of abilities
to be attained by students before they
graduate, are formulated based on the
program objectives.
16. Characteristics of OBE curricula (cont…)
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• Program outcomes address Knowledge (K),
Skills (S) and Attitudes (A) to be attained
by students.
• Teaching / Learning method may have to
be integrated to include different delivery
methods to complement the traditional
Lecturing method.
17. Operation Models for OBE
Yr. 4
Yr. 3
Yr. 2
Yr. 1
K 70%
S&A
30%
S&A
30%
K 70% K 70%
S&A
30%
S&A
30%
K 70%
Distribution of K, S,Aelements throughout the 4 years
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A B C D
A B C D
18. Mision and Vision
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Multimedia University
Vision
To be a premier university that propagates the
generation and dissemination of knowledge in cutting
edge technologies
Mission
• To deliver quality academic programmes based on state-of-
the-art R&D.
• To attract and nurture quality minds who will contribute
towards the global knowledge economy.
• To inculcate a strong research culture within a dynamic,
efficient and effective team of academic and support staff.
• To be financially self-sustaining via education and the
commercialisation of R&D products and services.
19. Mision and Vision (cont…)
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Faculty of Engineering & Technology
Vision
To be a competitive engineering faculty that innovates
learning and research as well as supports the production
of versatile graduates in facing the challenges of
globalisation.
Mission
• To produce competent engineers who will drive
and support the K-economy of the country
To function as a leading faculty for R&D activities
To serve as a catalyst for ideas/resources of ICT
20. Programme Objectives
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• What the programme is in preparing
graduates for their career and professional
accomplishments (published)
• Consistent with institution missions
(evidence)
• Involvement of constituents / stakeholders
(evidence)
21. • Expected to know and able to perform or
attain by the time of graduation (skills,
knowledge and behaviour/attitude)
• Outcomes (a) to (k)
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Programme Outcomes
22. a) Ability to acquire and apply fundamental principles of
science and engineering.
b) Capability to communicate effectively.
c) Acquisition of technical competence in specialised areas of
engineering discipline
d) Ability to identify, formulate and model problems and find
engineering solutions based on a system approach.
e) Ability to conduct research in chosen fields of engineering.
f) Understanding of the importance of sustainability and cost-
effectiveness in design and development of engineering
solutions.
g) Understanding and commitment to professional and ethical
responsibilities.
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Programme Outcomes
23. Program Outcomes (cont…)
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h) Ability to work effectively as an individual, and as a
member/leader in a team.
i) Ability to be a multi-skilled engineer with good technical
knowledge, management, leadership and entrepreneurial
skills.
j) Awareness of the social, cultural, global and environmental
responsibilities as an engineer.
k) Capability and enthusiasm for self-improvement through
continuous professional development and life-long learning.
24. • Outcomes that are expected from a certain
subject and these are assessed and evaluated
through various measurement tools.
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Learning Outcomes
25. Requirements from the students
• Active role – must come prepared for each
class; contribute by teaching others,
actively participating, taking risks, learning
from instructor/classmates
• Ethics – respect, trust and openness
• Committed to learning – continual
improvement
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26. Assessment Tools
• Exit surveys, Exit interviews (P)
• Alumni surveys and interviews (P)
• Employer surveys and interviews (P)
• Job offers, starting salaries (relative to national
benchmark) (P)
• Admission to graduate schools (P)
• Performance in group and internship assignments
(P,C)
• Assignments, report and tests (P,C)
P: Program C: Course
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27. Assessment Tools (cont…)
• Student surveys, individual and focus group interviews
(P,C)
• Peer-evaluations, self evaluations (P,C)
• Student portfolios (P,C)
• Behavioral observation (P,C)
• Written tests linked to learning objectives (C)
• Written project reports (C)
• Oral presentation, live or videotape (C)
• Research proposals, student-formulated problems (C)
• Classrooms assessment techniques (C)
P: Program C: Course
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28. Continual Quality Improvement
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• Assessment and evaluation processes provide
critical information to faculty (lecturers) and
administrators on the effectiveness of the design,
delivery, and direction of an educational program
- CQI
• Improvements based on feedback from
evaluations will close the system loop and the
process will continue year after year.
