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4th Edition-FYP Computing Essential Okt 22 (1)

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COMPREHENSIVE SUPERVISION PLATFORM (CSP) for CSP600 and CSP650 

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COMPREHENSIVE SUPERVISION PLATFORM (CSP) for CSP600 and CSP650 

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CSP is a Comprehensive Supervision Platform designed to manage every aspect of Computing Project (CP), CSP600 and CSP650 teaching and learning efforts. This easy access platform provides all materials related to CS.

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1. Make sure to keep/save all conversations with your supervisee.
2. Show's our efforts to guide and assist our supervisee.
3. Guide your supervisee, don't do it for them. Let them learn.
4. Share related articles/websites/tools that might help your supervisee.

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The most important thing is how to connect the Problem Statement, Objective, Methodology, and Result. 

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The most important thing is how to connect the Problem Statement, Objective, Methodology, and Result. 

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Refer to the example provided.

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1. Chapter name and outcomes.
2. Chapter outline.
3. Chapter Notes
4. Mind map
5. Video from YouTube
6. Video - Revision
7. Video - Experience Sharing (Alumni/Industry)
8. Supporting Material
9. Supervision Experience
10. Contact Us

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1. INTRODUCTION

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CHAPTER 1: INTRODUCTION

1. Introduction
2. Current process
3. Objective, scope and significance
4. Project framework, Gantt chart
5. Conclusion

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How to Write an Eye-Catching Essay Introduction | Scribbr

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How to Write an Eye-Catching Essay Introduction | Scribbr 🎓

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Chapter 1: Introduction to Final Year Project

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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Sharing Session - Anis Ahmad Badri

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Join the group for further discussion.

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CHAPTER 2: LITERATURE REVIEW

Learning Outcomes:
1. Explain what is Literature Review (LR)
2. Describe the related content for the LR
3. Describe the previous research related to the project
4. Describe the implication of LR to the project

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CHAPTER 3: METHODOLOGY

Learning Outcomes:
1. Explain what is methodology
2. Describe the types of available methodology
3. Describe the suitable methodology based on project
4. Describe the phases involve

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CHAPTER 4: ANALYSIS AND DISCUSSION

Learning Outcomes:
1. Explain what is result and analysis
2. Describe the project that has been develop
3. Describe the implementation of the theory in the project
4. Describe the discussion
5. Summarize the project

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CHAPTER 5: CONCLUSION AND RECOMMENDATION

Learning Outcomes:
1. Explain output of the project according to the project objectives.
2. Describe the constraints.
3. Describe the project improvement.
4. Conclude the project

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Analysis and Discussion

a. Results and Analysis

b. Discussion

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How to Write a Literature Review: 3 Minute Step-by-step Guide

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How to Write a Literature Review: 3 Minute Step-by-step Guide | Scribbr 🎓

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Chapter 1: Introduction to Final Year Project

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Chapter 1: Introduction to Final Year Project

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Result and Discussion

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How to Write a Discussion Section | Scribbr 🎓

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What To Include in Your Final Year Project (FYP) Presentation Slides

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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Industrial Sharing: System Development Methodologies

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Industrial Sharing: Project Methodologies and Processes

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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Farisa Aqilla - Sharing Session

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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Nurul Aqilah - Sharing session

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Skip to Main Content

It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

Introduction

What is a Literature Review?
A literature review is a systematic analysis and summary of the published academic writings directly relating to your topic of research. It should provide the reader with background information on your topic, and explain the significance of your research, and how your research relates to the rest of the academic publications on the topic.

This section will guide you on what a literature review is and is not, why you need a literature review, and how you should go about getting started with your literature review.

Be sure to check out the below links with more details on how to evaluate the sources for your literature review as well as the usage of grey literature in your literature review!

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Skip to Main Content It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results. What is a Literature Review?

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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CSP Sharing Session - Nurul Nadiah

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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Zainurin Syafiq - Sharing Session

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CSP Sharing Session - Mohd Zainurin Syafiq

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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7. VIDEO - EXPERIENCE SHARING (ALUMNI/INDUSTRY)

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CSP Sharing Session - Mohd Fazli

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BI Development

Chapter 18 recommends a hybrid iterative and incremental project methodology. This hybrid methodology uses the traditional waterfall approach to design the overall BI framework and architecture while using an agile approach to develop the architecture's underlying components, such as the BI applications. Although I advocate an agile approach, this book is agnostic as to whether a formal agile project methodology needs to be used by the BI project manager. There are other alternatives that can be used to achieve the same results, as discussed in Chapter 18. From a development perspective, the BI team needs to build the BI applications incrementally and iteratively by using prototyping (described later).

Note that this book uses the term BI application to mean the application that business people interact with to perform their reporting and analysis. The BI application may be built with any BI style such as reports, dashboards, scorecards, online analytical processing (OLAP) cubes, data discovery, predictive analytics, or advanced data visualizations. The assumption is that the data that the BI application uses is already available for the development team, but if not, then those data integration tasks discussed in the data integration section of this book will need to be done.

Business people are always getting surprised when BI applications are implemented. They will say that the application doesn't do what they asked for, is providing results different than expected or they need to add something they forgot to ask for. This scenario plays out time and time again. It is exacerbated when teams use a waterfall project methodology in which business people are engaged to gather requirements in the early stages of a project. Then, weeks or months later, when business people are reengaged to test the completed BI application, they notice different things. Although it's still a good idea to do the requirements early in the project to define scope, ensure that the overall framework is built, and to get the data in place for BI application development, switch to prototyping mode when you get to the BI application development phase. This gets people actively involved in a feedback loop and avoids the implementation surprises that plague so many BI projects.

Prototyping Lifecycle

Software prototyping is a technique to build specific portions of an application to investigate certain aspects such as functionality, feasibility, efficiency, performance, or how well it meets requirements. There are two primary objectives for BI prototypes:

*

Obtain feedback from its intended users.

*

Enable developers to build BI applications in an incremental manner.

There are several critical success factors in the BI prototyping efforts:

*

Dedicate time and effort. Business people who will be using the BI applications in their jobs need to be actively involved in working with the BI prototypes and providing feedback to the BI developers. Business management needs to make a commitment that these people will have dedicated time assigned to these prototyping tasks. The BI team should create a schedule with times and tasks for these business people to participate in prototyping.

*

Work incrementally. Although there are software prototypes that are either throwaway or only work on part of an application, you're better off using a BI prototype that incrementally builds the entire BI application. This type of prototyping may be referred to as incremental, evolutionary, or extreme.

*

Time-box it. The prototypes are rapid and time-boxed. This approach keeps both the developers and business people focused and better able to manage their time (the business people will have their "regular" jobs to complete besides the prototypes). Building the BI application in small and discrete increments enables a more thorough examination and validation when the prototyping is completed. Using this approach, you can develop and test incremental prototypes within hours.

The BI prototyping lifecycle as depicted in Figure 14.6 consists of the following tasks and feedback loops:

*

Define the prototype scope. Determine the component that will be developed. Identify the participants (developers, reviewers, and manager). Define the objectives, deliverables sign-off criteria (success criteria), and the timeline. If the team is using a rapid prototyping or agile approach then this step may be completed in a single meeting.

*

Create BI application prototype. Build the assigned component of the BI application. The assumption is that it will be built with the BI tool selected to deploy the BI application. The prerequisites are that a development environment exists and, at a minimum, sample data from the BI schema is available.

*

Conduct unit tests. The developer runs the prototype and examines test results. If changes are required, then revisions are made and the test is rerun. The prototype should only be passed along to the business people to conduct their own tests if it has passed the unit tests. If not, the developer informs the business people of any outstanding issues.

*

Users test prototype. The business people test and assess the prototype. Over the course of the incremental prototypes, business people will test the data, UI, and types of analytics offered.

*

Gather user feedback. The intent is to more actively involve business people in the development process to solicit their feedback and improve the overall BI application.

*

Revise as necessary. If the prototype has serious flaws or shortcomings, then it should be revised and that prototyping lifecycle repeated. If the issues are minor or need to be investigated further, then implement any changes and test them in the final prototyping step that involves the overall BI application.

*

Prototype signoff. For the individual components that are prototyped, a signoff indicates the next prototype can proceed. For the final prototype, the signoff indicates that the overall BI application has been validated and can proceed to the BI application testing lifecycle.

BI Application Development Tasks

BI prototyping is used for one or more of the following reasons:

1.

Validate, expand, or gather business, data, and technical requirements

2.

Determine feasibility of specific functionality

3.

Examine effectiveness of components built

4.

Engage business people to work with and provide feedback

The BI team may use prototyping to flush out the requirements (reason 1 listed previously) for the BI design. Prototyping may be used to determine either the BI content or the types of analytics that will be used in the BI application UI. If prototyping is used for design purposes, the BI team needs to document the design by using the BI tool's documentation capabilities. The prototype itself should be saved as part of the documentation.

BI prototyping is the best practice technique to develop BI applications (reasons 2-4 listed previously). The BI application will be divided into the following three components:

*

Create data content

*

Create data chart or visualizations for analysis

*

Create overall BI application

The BI prototyping lifecycle is depicted in Figure 14.7.

Create Data Content

The objective of this task is to get the data required for the analysis that will be performed using the BI application. At a minimum, this includes connecting to the required data sources, executing queries to gather the data, creating the schema used by the BI tool, examining the query results, and testing if the data is valid.

If the data sources are relational databases, then use open database connectivity (ODBC), java database connectivity (JDBC), or direct database connections. Nonrelational sources will require connection information filled into source-specific application programming interfaces (APIs). BI tools typically provide all the prebuilt connection APIs that a BI developer would need, but there are ways to supplement those if necessary.

The scope of creating and modifying the schema used by the BI tool, often called a repository, will depend on whether the business person accesses the schema to perform analysis or relies on prebuilt filters and drop-down lists to select data. Self-service BI applications such as data discovery, OLAP cubes, and pivot tables are the type of application requiring the business person to access the schema. When this happens, the BI developer may need to modify the BI tool schema to make it easier to use by doing the following:

This is typically performed using visual applications; however, some BI tools may require scripting.

Create Data Chart or Visualizations for Analysis

To select the best data chart or visualization, the BI developer examines the type of analysis that the business person is going to perform and the data that it will use. Many BI applications, such as dashboards, will support multiple types of analysis and data sets, so more than one data chart or visualization will need to be selected.

After the data content has been loaded, the BI developer creates each data chart individually and tests how well it meets business requirements. After these tests, business people should get a chance to provide feedback on each of these data charts. This is not a formal user acceptance test (UAT), but rather one of several checkpoints in the development lifecycle to get feedback as the BI application is being incrementally built.

Create Overall BI Application

Once all the data sources have been set up and each data chart has been built it is time to create the overall BI application. This means putting all the individual components into the overall BI application layout, establishing the navigation capabilities, implementing the UI standards and interconnecting the components as needed to support business requirements.

Once the overall BI application has gone through its prototyping tasks, then it will proceed into the BI application testing lifecycle discussed in the following section.

Read full chapter

DMAIC

DMAIC (define, measure, analyze, improve, and control) is used for projects aimed at improving an existing business process. The DMAIC project methodology has five phases ( Figure 18):

1.

Define the system, the voice of the customer and their requirements, and the project goals, specifically.

2.

Measure key aspects of the current process and collect relevant data.

3.

Analyze the data to investigate and verify cause-and-effect relationships. Determine what the relationships are, and attempt to ensure that all factors have been considered. Seek out the root cause of the defect under investigation.

4.

Improve or optimize the current process based upon data analysis using techniques such as design of experiments, poka yoke or mistake proofing, and standard work to create a new, future-state process. Set up pilot runs to establish process capability.

5.

Control the future-state process to ensure that any deviations from the target are corrected before they result in defects. Implement control systems such as SPC, production boards, visual workplaces, and continuously monitor the process.

Some organizations add a "recognize" step at the beginning, which is to recognize the right problem to work on, thus yielding an RDMAIC methodology. ⁹³

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14.2 Risk minimization

Software project management is strictly based on project management principles to achieve the stringent results required in an effective way. These principles are generally tailored to suite the market in which it is operating in, and accordingly a project methodology is to be used to make it successful in real-life applications.

There are different phases in the life cycle of a project, from the original decision, to the final phasing out/replacement of the solution. It facilitates a phased approach to projects, where each phase can be clearly defined, estimated and even contracted separately, with specific deliverables at each point of completion. The completion of each phase reduces the risk for subsequent phases by increasing the level of information available about the required solution. The cost of identifying and removing defects increases exponentially once development starts, as changing code or configuration within a package is much more expensive than changing the words/pictures in a document (refer Figure 14.1).

Additional cost spent on extra checking at the beginning stage(s) of a project is cheaper than the errors at the developed stages because testing is more complex and costly, and documentation changes are more widespread when more people are involved in correcting the error. Seventy percent of software costs go on maintenance of existing software, so it is very important to ensure that the design is easily maintainable. The design phase impacts dramatically on the development costs and the total development costs.

The later in the process, the more it costs to effect a change. This does not even take into account the effect of non-functional software on product quality, throughput and yield. This approach lessens the risk for both the customer and software provider and produces better systems at lower costs.

Some software solution providers use IPEC (Initiate, Plan, Execute, Control) as the high level approach to all projects, where the initiate component is an internal process of the provider leading to a proposal being prepared. The plan, execute and control components are directly applicable to the deliverables and client involvement.

The detail project plan is finalized in the project-planning phase and any uncertainties regarding the project scope are clarified and the quality assurance plan is finalized. All ambiguous/unclear requirements will be clarified and abbreviated requirements will be expanded on by means of request for information (RFI) forms. These RFIs will be filed in the established project note book (PNB), as they will then form part of the contractual baseline.

It is preferable to obtain a fixed price for each phase of the project from the solution provider, and to handle each phase as a separate project. This ensures that each phase is measured and controlled independently, and that the relationship with the provider can be terminated after completion of any phase, should the relationship deteriorate beyond saving.

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8.4 Package Stereotypes

8.4.1 Packages and Models

Packages are the primary organizing and CM structure for UML Models. UML tools often cooperate with standard CM tools to allow check-in, check-out, change management, compare, baseline, and restore on Package boundaries.

When projects are new and subject to many changes, Packages tend to be small, because if you have to check out a Package to start editing, you do not want to lock out other modelers from doing any work. On the other hand, when maintenance work is causing changes that tend to ripple through multiple Packages, small Packages with repeated check-outs, and check-ins will cause delay to the working process. Some projects will change their Package structure as the project evolves, but it is best to pick Packages properly sized to capture closely related Elements so typical changes would only affect one Package.

Another consideration is that Packages tend to be the review structure. Just as with code, your model should be reviewed, so at the lowest level of Packages, they should be sized to be conveniently peer-reviewed models.

Every Element in UML must be owned by exactly one other higher level Element, except the top-level Packages in the model. This requirement produces a chain of ownership that must end in a Package. This is the origin of the use of Packages as the organizing Element.

One of the common stereotypes of Package allows the modeler to show that we are splitting the model by organizing principles. The stereotype is "model" which indicates that the contents of the Package are intended to be a complete version of your system based on a modeling aspect. If you remember our discussion at the end of Chapter 2, What is UML?, we talked about different types of a modeling: Such as conceptualization, requirements analysis, analysis, and design. If you have a Package whose totality of contents follows one of these aspects, you would flag the Package with the stereotype of "model".

How we use these "models" depends mostly on project methodology. Each model can offer up a complete view of the system at a particular level of abstraction. If not based on the types of modeling, the models might be based on a metamodeling level (e.g., M0, M1, ...), or modeling phase. Many projects have formal official reviews at a planned time, e.g., SRR (System Requirements Review), SDR (System Design Review), PDR (Preliminary Design Review), or CDR (Critical Design Review), or TRR (Test Readiness Review). The models produced at these times are kept in "model" Packages and baselined so that the project can go back and review them. Models, as Packages, are also Namespaces, so there is no problem having identically named Elements in each model.

Depending on the methodology, the separate "models" may be connected by a chain of dependencies. There are special types of dependencies that often are, but are not required to be used in these circumstances. An abstraction relates two NamedElements that represent the same concept at different levels of abstraction or from different viewpoints. Any of the abstractions in Table 8.3 can be associated with a string that explains how the mapping works.

Abstraction Type

Usage

"abstraction"

Relates two Elements or sets of Elements that denote the same concept but at different levels of abstraction or from different viewpoints. Adjacent to the stereotype may be a string that explains how the mapping works

"derive"

A calculable relationship between levels

"realize"

The arrowhead points to Elements that act as requirements to the Elements at the tail that realize or implement the target Elements. We often show this relationship as a dashed line with a hollow triangular arrowhead

"refine"

A relationship between two different levels of abstraction

"trace"

A generic relationship between different versions. May be bi-directional

As a type of Package, models look like Package with the stereotype of "model". The optionally have a triangle adornment Δ in the upper right to indicate their model status. Models may have a viewpoint field that uses a string to document the organizing principle or perspective for that model, see Fig. 8.20.

Many modelers make their top-level Packages (the ones not contained by other things) into "models". However, this is not required and would prevent models from being contained in other Packages or models (as shown in Fig. 8.20). As with most use of models, the project methodology will determine your practice.

8.4.2 Miscellaneous Stereotypes of Packages

8.4.2.1 ModelLibrary

The stereotype "modellibrary" indicates that the majority of its contents are used by other Packages or models. Typically, we use a "modelLibrary" to contain common types, units, utilities, or parts that other Packages in the system can use. The "modelLibrary" will be marked a publicly visible and potentially "imported" into the top-level Package-Ensuring visibility and accessibility by all of the system's Packages.

8.4.2.2 Framework

Similar to a "modelLibrary", a "framework" contains the infrastructure and architectural Elements shared my many of the other system Packages. A "framework" usually includes event and error handlers, message passing, logging, self-check, built-in-test, diagnostics, and security enforcement.

8.4.2.3 Profiles

Although profiles (and their associated profile diagrams) are not included in the Foundational level of the OCUP-2 examinations, you need to know their purpose and use. Profiles are similar to the standard Package except for the Profile stereotype. Profiles typically contain "metaclasses" (see Chapter 4: The Organization of UML) that are tailored to aid in enforcing the project's methodology or reporting and status regime. You can use it to create or removed metaclasses or stereotypes, though typically only one person on a project is allowed. Of course, these Profiles must be available to everyone on the project, and are difficult to create, potentially introducing portability problems.

8.4.2.4 Diagrams

Diagrams of the contents of "models", "modelLibrarys", "frameworks", and "profiles" all have a <kind> field of pkg or package and the name of the Namespace in the diagram header. In many cases, the <kind> field can be omitted as it can be determined by the contents.

If the diagram primarily shows Packages and their relationships (e.g., imports, accesses, dependencies, or abstractions), the diagram would be considered a Package Diagram. If the diagram primarily shows Classes, generalization, and associations, the diagram would be considered a Class Diagram, despite the header. If the diagram primarily shows instances, it would be considered an Instance Diagram.

A Profile Diagram looks like a Package Diagram but supports a slightly different notation that allows the extension of or restriction of existing metaclasses and the definition of stereotypes. We will not cover the details in this book nor are they on the OCUP-2 Foundational exam.

Points to Remember

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The Data Mart

The next era was the rise of data marts. Data marts promised to be quicker and cheaper to build, and provided many more benefits-including the benefit of actually being able to finish building them! The data mart was primarily a backlash to the big, cumbersome CDW projects, with the key difference being that its scope was limited to a single business group rather than the entire enterprise. Of course, that shortcut did speed things up, but at the expense of obtaining agreement on consistent data definitions, thereby guaranteeing data silos.

Although the early-adopter technology companies and Bill Inmon both advocated that data marts should be fed from the EDW, the rest of the industry was pushing data marts based on a different architecture where they were fed directly from source systems. Instead, data mart vendors and pundits stated there was no real architectural difference between a DW and data mart. As Figure 6.2 shows, the data mart, just like the DW, sourced data from SORs, used a waterfall project methodology and had to use quite a bit of manually-created custom code. The pundits' sales pitches said that the data mart was just like a DW - using the same tools, data models and design principles - but simpler and faster to build.

By the time data mart projects were starting to be widely built, two trends were also occurring: the emergence of BI tools and the use of relational databases to build new source systems. These trends resulted in a much shorter time to build a data mart than a CDW, aided, of course, by the fact that data marts have a more limited scope than CDWs.

Rather than being part of an overall enterprise data architecture, data marts were the architecture. You could build them faster and cheaper because you did not take the whole rest of the enterprise into account. This meant you could take shortcuts and avoid the most difficult part of DW: DI and reaching agreement on data definitions and metrics across business groups. As a result, data marts were built for the parochial interests of their sponsors rather than for the enterprise.

Companies sprouted multiple data marts, each built to accommodate only their sponsor with their own data definitions, data transformation, reporting, and technical platform. We moved from trying to achieve one version of the truth to building multiple single versions of the truth-an oxymoron. To top it off, many of these projects overpromised and under-delivered, just like the CDW projects.

Data mart projects exacted another toll. The tradeoff for being able to deliver cheaper and faster was to severely limit scope of the breadth and depth of data, their integration, their quality, and the analytical capability offered to business users. Initially, data marts were perceived as a great success-until business groups realized they were debating in meetings which one of their reports (and associated data marts) had the "right" numbers. Each group had its own data silo.

In the end, data marts left their business users wanting more, which often meant building another round of data silos with more disjointed data marts or data shadow systems and more versions of the truth. At this point there started to be a debate within the industry on whether enterprises should build a DW versus data marts. Many vendors jumped on the data mart bandwagon because those projects were initially successful and they could therefore sell more of their products.

Later, this type of data mart would be become known as an independent data mart versus a dependent data mart that is part of a hub-and-spoke architecture. This meant it was independent from the DW and extracted data directly from the source systems rather than pulling the data from (and being dependent on) the DW.

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Chapter 18 recommends a hybrid iterative and incremental project methodology. This hybrid methodology uses the traditional waterfall approach to design the overall BI framework and architecture while using an agile approach to develop the architecture's underlying components, such as the BI applications.

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How to Write a Discussion Section | Tips & Examples

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The discussion section is where you delve into the meaning, importance, and relevance of your results. It should focus on explaining and evaluating what

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Project/System Development and Report Writing

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Project/System Development and Report Writing

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Chapter 1,2, and some of chapter 3 can be written without the completion of the system development (proposal).

Chapter 4 and 5 can be written after the system development,  testing and evaluation completed.

Therefore, if student fail to develop a system (the core/main function not functioning well), chapter 4 and 5 cannot be written and objective number 2 (to design and develop) and objective number 3 (to test/evaluate) cannot be achieved.

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Best Final Year Innovative Projects Ideas for Computer Science & Information Technology Students

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Final Year Project Ideas for Computer Science in 2020

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Best Final Year Project for Computer Science Student (cse ) 2022 | Innovative | Latest Projects

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"IT IS NOT ABOUT SAYING THE RIGHT THINGS, IT IS ABOUT DOING THE RIGHT THINGS"

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"COMPREHENSIVE SUPERVISION PLATFORM (CSP)" by Hasiah Mohamed, Norizan Mohamad, Norulhidayah Isa,
Mohd Talmizie Amron, COMPREHENSIVE SUPERVISION PLATFORM (CSP), UiTM is licensed under CC BY-NC-ND 4.0

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Join the group for further discussion.

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Join the group for further discussion.

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Join the group for further discussion.

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Join the group for further discussion.

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Join the group for further discussion.

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Join the group for further discussion.

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Overview of CSP600
- Project Formulation

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Choose a Project
a. Area of Interest
b. Identify Stakeholder
c. Research Questions Formulation
d. Problem Statement, Objective(s), Scope and Significance Identification

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1. Norehan Abdul Manaf, Norjansalika Janom, Essentials of Computing Sciences: Project Administration, 2, Universiti Teknologi MARA, 2014, ISBN: 9789670171500
2. Christian Dawson, Projects in Computing and Information Systems 3rd edn: A Student's Guide (3rd Edition), 3rd, Prentice Hall; 3 edition (June 13, 2015), 2015, ISBN: 978-129207346
3. Umberto Eco, How to Write a Thesis, first, The MIT Press; 1 Tra edition (March 6, 2015), 2015, ISBN: 978-026252713
4. Helen Sword, Stylish Academic Writing, Harvard University Press (April 2, 2012), 2012, ISBN: 978- 067406448
5. Lawrence (Larry) A. (Anthony) Machi (Author), Brenda T. (Tyler) McEvoy (Author), The Literature Review: Six Steps to Success, 2nd, Corwin; Second Edition edition (June 8, 2012), 2012, ISBN: 978- 145224088
6. John W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th, SAGE Publications, Inc; 4th edition (March 14, 2013), 2013, ISBN: 978-14522261

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Abstract

·        Types of Abstract

·        Executive

·        Descriptive
·        Informative

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COMPREHENSIVE SUPERVISION PLATFORM IN PDF FORMAT

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COMPREHENSIVE SUPERVISION PLATFORM IN PDF FORMAT

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Interactive platform for sharing information with the students

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Congrats on these efforts!

My suggestion to enhance this platform includes the related form and rubrics  (F1-F13) to let students know what is expected from them and the project expectations. The rubrics help CSP Lecturer / Supervisor / Examiner teach and coordinate instruction and assessment. CSP Lecturer / Supervisor / Examiner can refer to the following form [1].

F1: Mutual Acceptance Form
F2: Project Motivation Evaluation Form
F3: Literature Review Evaluation Form
F4: Methodology Evaluation Form F5: Proposal/Project In-Progress Form
F6 (a): Project Formulation Submission Form
F6 (b): Project Submission Form
F7: Project Formulation Presentation Form
F8: Project Formulation Report Evaluation Form
F9: Project Progress Presentation Form
F10: Final Project Presentation Form
F11: Final Project Report Evaluation Form
F12: Project Confirmation of Correction Form
F13: Lean Canvas Model Evaluation Form

Besides, the latest information on final year students' projects must be registered under the UiTM ethics committee. I suggest utilizing this platform to guide the students on research ethics approval applications.

All the best! :D

:: Ts. Dr. Nurul Hidayah Mat Zain
:: UiTM Cawangan Melaka
:: Kampus Jasin

References:
1. Norehan Abdul Manaf, Norjansalika Janom, Essentials of Computing Sciences: Project Administration, 3, Universiti Teknologi MARA, 2021, ISBN: 9789670171500