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🤖2022 Bronze Innergy Award 2021 Submission Free Robotic Process Automation (RPA) using TagUI-AI Singapore project for Student Learning Space (SLS)

 

Free Robotic Process Automation (RPA) using TagUI-AI Singapore project for Student Learning Space (SLS)

 

 

 

TagUI-AI Singapore RPA project for the Student Learning Space (SLS), highlighting its key themes and important ideas.

Executive Summary:

This document details a successful implementation of Robotic Process Automation (RPA) using the free and open-source TagUI tool by an AI Singapore project team for the Student Learning Space (SLS). The project aimed to automate repetitive, rule-based manual tasks performed by the SLS team and HQ officers, significantly reducing manual effort and improving efficiency. The solution is innovative due to its use of natural language-like scripting, cross-platform compatibility, integration with JavaScript and Python, and its zero-cost, open-source nature, which stands in contrast to expensive commercial RPA alternatives like UiPath. The project has demonstrated significant benefits in terms of cost savings, increased job satisfaction for administrators, personalized communication and fairness for reviewers, and scalability across the Ministry of Education (MOE).

Key Themes:

  1. Addressing Manual Inefficiency: The project directly addresses the time-consuming and repetitive nature of tasks within the SLS workflow, particularly the assignment and review of Community Gallery (CG) lessons. The motivation stemmed from the challenges faced during the COVID-19 pandemic and Home-Based Learning (HBL) when a large volume of reviews was required.
  2. Open-Source and Free Technology: A central theme is the successful adoption and leveraging of a free and open-source RPA tool (TagUI) as a viable and superior alternative to expensive commercial options (UiPath). This highlights the potential for cost savings and perpetual use without licensing fees.
  3. Innovativeness and Adaptability: The TagUI solution is presented as innovative due to its user-friendly, natural language scripting, multi-language support, compatibility with various code editors, cross-operating system functionality, and seamless integration with JavaScript and Python for more complex tasks. This adaptability makes it easier for adoption and scaling within MOE.
  4. Scalability and Knowledge Sharing: A key focus is on the potential for scaling the TagUI RPA solution across different MOE workstreams. The project team actively promotes knowledge sharing through open-sourcing code on GitHub, creating YouTube tutorials, and offering support to colleagues.
  5. Digital Transformation and Skill Development: The project exemplifies digital transformation by automating workflows and highlights the importance of digital literacy and problem-solving skills for MOE officers. It promotes a "Learn for Life" approach to embrace automation.
  6. Improved Workflow and Stakeholder Benefits: The implementation of RPA directly benefits various stakeholders within the SLS ecosystem, including the CG Chief Admin (through automation of mundane tasks and increased accuracy), the project team members (through enhanced job satisfaction and development of digital skills), and the CG lesson reviewers (through personalized and timely communication).

Most Important Ideas and Facts:

  • Problem Solved: The project automated the manual process of crawling data from SLS lessons, assigning HQ officers to review lessons based on their subjects, emailing officers with personalized content, and sending reminders for unreviewed lessons.
  • Tool Used: TagUI - AI Singapore's robotic process automation (RPA) project.
  • Contrast with Commercial RPA: The team initially attempted to use UiPath but faced difficulties with documentation, debugging, and user interface changes in SLS. TagUI was chosen as a free and open-source alternative. The estimated cost of a commercial tool like UiPath is approximately \(420/month per account, while TagUI is free and open-sourced.
  • Key Innovation Features of TagUI:Written in human-readable code / natural language-like syntax in English and convertible to 19 other languages.
  • Can be written in any code editor like Visual Code Studio, Microsoft Word, and Excel via a plugin.
  • Runs on MacOS, Linux, and Windows.
  • Easy to install ("simply unzip, add path and run").
  • Integrates with JavaScript and Python for more complex features.
  • Free and open-sourced, allowing for perpetual use.
  • Automation Flow: The automation involves a combination of TagUI, JavaScript, and Python. "TagUI logs in to SLS and saves lesson data on computer, the JavaScript assigns reviewers to lessons, and Python copies selected data back to a Database-Google-Sheet etc, with one schedule-able click."
  • Specific Tasks Automated:Assigning SLS Community Gallery (CG) lessons pending review to HQ officers.
  • Emailing reviewers with personalized content and hyperlinks to specific CG lessons.
  • Sending gentle email reminders after lessons are unreviewed for more than 5 working days.
  • Adding test questions (Multiple Choice and Free Response) to a question bank from a CSV file.
  • Adding 170 teachers from an Excel Sheet for professional development workshops, with a skipping mechanism for unverified emails.
  • Sharing lessons in SLS "My Drive" with multiple teachers, saving an estimated 4000 clicks for sharing 20 lessons with 20 teachers.
  • Cost Savings: Building a similar automation within the SLS front-end would have cost an estimated \)80,000 upfront and \(5,000 for yearly maintenance. Using TagUI eliminates these costs.
  • Impact on CG Chief Admin: Automates "Mundane and Rule-based Work" such as getting data, assigning lessons, sending emails, and sending reminders. This leads to "Higher job satisfaction and assignment-email with accuracy and fairness."
  • Impact on Reviewers: Receive "personalised emails" with a single click URL to the lesson and "gentle email reminders" after 5 working days.
  • Quantifiable Impact:Estimated 1500 CG lessons were assigned automatically from Apr to Dec 2021.
  • Automation ran 190 times (every work-day) during this period.
  • Estimated time saving of 30-60 minutes per day.
  • Estimated 1700 clicks saved when adding 170 teachers.
  • Estimated 4000 clicks saved when sharing 20 lessons with 20 teachers.
  • Sustainability: The solution is sustained through "Zero dollars and always Evolving/Expanding using GitHub". Knowledge is shared through GitHub, YouTube tutorials, and community engagement.
  • Future Potential: The project aims to automate other "mundane rules-based clicking on SLS" and become a "MOE community champion for use of TagUI-RPA".
  • Community and Support: Technical support from the TagUI AI-Singapore team (Ken and Ruth) was crucial through GitHub issues, Telegram chat, and Zoom sessions.
  • Collaboration and Whitelisting Efforts: The team is actively working on whitelisting TagUI and Python for use in Whole of Government (WOG) and School Standard Operating Environments (SSOE) devices, recognizing the tremendous potential for automation across MOE.
  • Professional Development: The project team is ready to support others in using TagUI and points to readily available resources like documentation, a free beginners, and upcoming intermediate online certification course, emphasizing that no prior programming knowledge is required.
  • Recognition: The project received a 2022 Bronze Innergy Award.

Quotes from the Original Source:

  • "TagUI - AI Singapore’s robotic process automation (RPA) project, has reduced the Student Learning Space (SLS) team’s manual effort on repetitive tasks..."
  • "Our TagUI script is written in natural language-like syntax in English and can be easily understandable..."
  • "Finally, TagUI is free and open-sourced, so MOE can continue to use it perpetually unlike other RPA tools that may cost approximately \)420/month (e.g. UiPath) to maintain per account."
  • "We started by writing simple lines of code in TagUI and gradually added more complex JavaScript to supplement beyond what RPA does..."
  • "Estimated 1500 CG lessons were assigned out automatically using TagUI from Apr to Dec 2021, on every work-day basis (190 times)..."
  • "Building such a full automation on SLS front-end itself might have costed an estimated \(80,000 upfront and \)5,000 for yearly maintenance."
  • "Instead, we used our digital literacy skills in coding, and automate all flows into a single click on the computer."
  • "Zero dollars and always Evolving/Expanding using GitHub"
  • "We are in the process of even whitelisting TagUI and Python for use in WOG and SSOE, because we believe in the tremendous potential to automate rule-based clicks to save human efforts..."
  • "We strive to help all MOE colleagues to digitally transform their own repetitive workflows where possible, using this free RPA and “Learn for Life” for higher job satisfaction."

Conclusion:

The implementation of TagUI-based RPA for SLS demonstrates a successful and impactful approach to automating manual workflows within a government agency. By leveraging a free and open-source tool, the project achieved significant cost savings, improved efficiency, increased job satisfaction, and laid the groundwork for wider adoption and digital transformation across MOE. The focus on knowledge sharing and community building further enhances the sustainability and scalability of the solution.

 

 

  1. In addition to TagUI, what two other programming languages are integrated into the automated workflow, and what are their general roles?
  2. Describe one specific task, besides lesson assignment, that has been automated using TagUI in this project.
  3. How does the TagUI solution contribute to cost savings for MOE?
  4. What is the primary benefit of the TagUI RPA solution for the CG lesson reviewers?
  5. How is the sustainability of the TagUI solution ensured beyond its initial implementation?

Quiz Answer Key

  1. The project aims to reduce the manual effort involved in repetitive tasks for the SLS team, such as assigning lessons, emailing reviewers, and sending reminders.
  2. The task was initially done manually, involving assigning lessons to a large number of colleagues via email.
  3. The team abandoned UiPath due to a lack of proper documentation, difficulty in debugging, the departure of the officer who created the flows, and the tool not working after a major SLS user interface change.
  4. Two key advantages are that TagUI is free and open-source (unlike commercial tools which have recurring costs) and its script is written in human-readable, natural language-like syntax.
  5. The TagUI script is written in natural language-like syntax in English and can be easily converted to 19 other languages. The script can also be written in various code editors or even Microsoft Word and Excel via a plugin.
  6. JavaScript is used for more complex features like automatically assigning reviewers based on their subject interests and levels. Python is used for file manipulation and copying data back to Google Sheets.
  7. Other automated tasks include adding test questions from a CSV file for an adaptive learning system, adding teachers from an Excel sheet for workshops, and automating the sharing of lessons in SLS "My Drive".
  8. Building such a full automation on the SLS front-end itself would have cost a significant amount for upfront development and yearly maintenance. The TagUI solution achieves similar automation using zero dollars.
  9. CG lesson reviewers receive personalized emails with clickable links to lessons and gentle email reminders, making the review process easier and more streamlined.
  10. The sustainability is ensured through the technical know-how of an ETD Lead Specialist, open-sourcing the code and dependencies on GitHub for sharing and collaboration, creating YouTube tutorials, and exploring whitelisting for use on official devices.

Essay Questions

  1. Compare and contrast the initial manual process for assigning SLS Community Gallery lessons with the automated workflow implemented using TagUI, highlighting the benefits and drawbacks of each approach.
  2. Discuss the concept of "digital literacy skills" as mentioned in the text and explain how the development and use of the TagUI RPA solution exemplifies these skills within the MOE context.
  3. Analyze the strategic advantages of adopting a free and open-source RPA tool like TagUI compared to utilizing a commercial, proprietary tool such as UiPath, based on the information provided in the text.
  4. Evaluate the various methods used to ensure the sustainability and scalability of the TagUI RPA project across different MOE work streams and users.
  5. Explain how the TagUI RPA project demonstrates innovation and a "spirit of dare" within the MOE environment, considering the initial challenges and perceived risks.

Glossary of Key Terms

  • Robotic Process Automation (RPA): Software technology that mimics human actions interacting with digital systems and software to perform repetitive, rule-based tasks.
  • TagUI: An open-source RPA tool developed by AI Singapore, known for its human-readable, natural language-like syntax.
  • Student Learning Space (SLS): An online learning platform or system used by MOE (Ministry of Education) in Singapore.
  • Community Gallery (CG) Lessons: Lessons contributed by teachers within the SLS platform that are available for others to use or review.
  • HQ Officers: Headquarters officers, likely referring to personnel at the MOE central office.
  • Human-readable code: Code written in a way that is easily understandable by humans, often resembling natural language.
  • JavaScript: A widely used programming language for front-end and back-end web development, often used to add interactive features.
  • Python: A versatile programming language used for various applications, including data analysis, scripting, and automation.
  • Database-Google-Sheet: Refers to storing and managing data in a Google Sheet that functions like a simple database.
  • Open-sourced: Software with source code that is made freely available and can be modified and distributed by anyone.
  • GitHub: A web-based platform used for version control and collaboration on software development projects.
  • MOE: Ministry of Education (Singapore).
  • WOG (Whole of Government): Refers to systems or initiatives that apply across all government agencies.
  • SSOE (School Standard Operating Environment): The standardized computing environment used in schools.
  • Personal computers (Windows, MacOS, and Linux): Common operating systems for personal computers, indicating the cross-platform compatibility of TagUI.
  • UiPath: A leading commercial RPA tool, often used as a comparison point to TagUI in the text.
  • Debugging: The process of identifying and removing errors from computer hardware or software.
  • User interface (UI): The visual layout and interactive elements of a software application or website.
  • Code editor: A text editor designed specifically for writing and editing source code.
  • Visual Code Studio: A popular free source-code editor.
  • Microsoft Word and Excel: Common office productivity software applications.
  • Plugin: Software that adds specific features or functionality to an existing program.
  • Terminal or command line: A text-based interface for interacting with a computer's operating system.
  • Deployed file: An executable file that can be run directly to initiate a program or workflow.
  • Dependencies: External software components or libraries that a program relies on to function correctly.
  • Libraries: Collections of pre-written code that can be used to perform common tasks.
  • Curriculum subjects: The academic subjects taught in schools (e.g., Mathematics, Science).
  • Adaptive Learning System (ALS): A learning system that adjusts the content and pace of instruction based on a learner's performance.
  • CPDD: Curriculum Planning and Development Division (likely within MOE).
  • CSV file: A comma-separated values file, a simple format for storing tabular data.
  • ETD: Educational Technology Division (likely within MOE).
  • Professional development workshops: Training sessions or programs designed to enhance the skills and knowledge of professionals, such as teachers.
  • Whitelisting: The process of approving specific software or websites for use on a network or device.
  • Rule-based workflows: Processes or tasks that follow a defined set of rules or instructions.
  • Computer vision clicks: Using computer vision technology to identify and click on elements on a computer screen.
  • Chrome browser webpage automation: Automating interactions with websites using the Chrome browser.
  • Digital literacy skills: The ability to use digital technologies, communication tools, or networks to locate, evaluate, use, and create information.
  • Mundane tasks: Routine, repetitive, and often uninteresting tasks.
  • Job satisfaction: The degree to which individuals are content or fulfilled with their work.
  • Accuracy and fairness of assignment: Ensuring that tasks are assigned correctly and equitably.
  • ETD Lead Specialist: A senior specialist in the Educational Technology Division.
  • Source codes: The set of instructions written by a programmer in a programming language.
  • Sensitive data: Information that needs to be protected due to its confidential or private nature.
  • Workplace: Likely refers to the Workplace by Facebook platform used for internal communication and collaboration within an organization.
  • Work streams: The processes or series of activities involved in a particular area of work.
  • Professional Development (PD): Activities designed to enhance the skills and knowledge of professionals.
  • Online certification course: A course offered online that provides a certificate upon completion.
  • Prior programming knowledge: Previous experience or understanding of computer programming.
  • Learn for Life: Likely an initiative promoting continuous learning.

Project Summary

TagUI - AI Singapore’s robotic process automation (RPA) project, has reduced the Student Learning Space (SLS) team’s manual effort on repetitive tasks, like crawling data from SLS lessons, assigning HQ officers to review lessons based on their curriculum subjects, emailing officers with personalised email body and sending reminders after lessons are detected as unreviewed for more than 5 working days.

The RPA is written in human-readable code and when combined with JavaScript and Python, it becomes a fully automated flow of commands. TagUI logs in to SLS and saves lesson data on computer, the JavaScript assigns reviewers to lessons, and Python copies selected data back to a Database-Google-Sheet etc, with one schedule-able click . 

We have open-sourced our “code-in-words” on GitHub and it is easier for MOE to integrate the use of RPA into other rules-based workflows using WOG (recently whitelisted and added codes), SSOE or personal computers (Windows, MacOS and Linux).

Innovativeness of solution 

a)
We were initially given the task to assign SLS Community Gallery (CG) lessons which were pending review to 150 colleagues via emails. We used to do it manually but after reflecting on the experiences and feedback from 2020 when large numbers of SLS CG lessons needed to be reviewed urgently due to COVID-19 disruptions and Home-Based Learning (HBL), we realised there was an opportunity to develop a more scalable approach to save time and effort.
There were some earlier efforts to use UiPath, a leading commercial RPA tool. Having no prior experience using that tool, we still tried to make UiPath run the automation but to no avail. There were no proper documentations to refer to, debugging was difficult and the officer who created the flows had left HQ. After trying for two months, we decided to abandon this UiPath approach which also cannot worked after Jan 2020 after SLS had a major user interface change.
 
b)
Our TagUI solution is innovative and bold because of the following reasons. Our TagUI script is written in natural language-like syntax in English and can be easily understandable and converted to 19 other languages (Chinese, Malay etc) which supports global usage. The same script-flow can also be written in any code editor like Visual Code Studio or even Microsoft Word and Excel via the TagUI plugin for novice users. One can simply run the flow via terminal or command line or double click a ‘deployed’ file. It also runs on all three operating  system MacOS, Linux and Windows, supporting MOE to scale up the use easily unrestricted by computer operating system. It is also easy to install, simply unzip, add path and run, assuming the dependencies  are already on the computers. We also tap on JavaScript and Python integration for tapping on other libraries for more complex features that TagUI doesn’t do well. Finally, TagUI is free and open-sourced, so MOE can continue to use it perpetually unlike other RPA tools that may cost approximately \(420/month (e.g. UiPath) to maintain per account.
We started by writing simple lines of code in TagUI and gradually added more complex JavaScript to supplement beyond what RPA does, like assignment of reviewers to lessons automatically based on reviewers’ 2 subjects-interests and levels (Primary or Secondary). Python was used to do file manipulation and copying of data back to the lesson data Google Sheets intelligently at the desired columns and rows. Having experienced success in RPA, we extended the flow to include searching emails and replying to gently remind reviewers to login to SLS to review the CG lessons, after detecting that the reviewer was taking longer than the Public service commitment of 5 working days. We were able to do so much because of the technical support from the TagUI AI-Singapore team whenever a “blocker” is encountered and we were able to overcome the issues very quickly via GitHub issues -discussions, telegram community chat , or Zoom  sessions with the TagUI maintainer(s)-creator(s).
 

Idea generation and development 

a) 

After failed-experimenting with UiPath for two months, we decided to take the opportunity to find a free and open-source tool which could hopefully do what we wanted to address the abovementioned challenge. Since TagUI is listed as an AI Singapore RPA project, we decided to check it out by ‘installing’ it into our personal computer. We tried to run some of the sample ‘.tag’ files to understand how it works and we were finally able to write simple RPA flows. We began to ask questions via the GitHub issues and tried to make simple flows work using text files with a ‘.tag’ extension. We took calculated risks and showed spirit of dare by using an AI Singapore, open source RPA tool, knowing that there will be resistance from strict MOE IT policy on running TagUI on Whole of Government (WOG) devices and School Standard Operating Environments (SSOE ) computers thus we did it on our personal computers. We had some prior experiences succeeding in using Open-Source Physics tools  so the digital transformation benefits outweighed the risks of being seen as ‘unauthorized’ use of RPA tools. The time spent trying to learn UiPath and TagUI also helped as we were clearer about the lingos, terminologies and missteps so we applied the learning from the failed UiPath attempt.  

 

b) 

We knew from feedback that the reviewers wanted customised emails with hyperlinks to exact CG lessons to ‘return/feature’ thus our RPA captures the URL of the CG lessons and adds it to the emails. Reviewers also requested gentle email reminders just in case they forgot about the review by the due date. 

We collaborated with the TagUI AI Singapore team (Ken and Ruth) to help with some of the syntax, technical know-how and even trouble-shoot how to run TagUI on SSOE Windows computers. We are in the process of even whitelisting TagUI and Python for use in WOG and SSOE, because we believe in the tremendous potential to automate rule-based clicks to save human efforts in clicking on webpages and desktop apps for MOE teachers and officers.

Separately, this project has also automated adding of test questions (Multiple Choice and Free Response) drawing the data from a CSV file , to support population of question bank for Primary Mathematics for CPDD’s adaptive learning system (ALS) efforts. Another automation is to add 170 teachers from an Excel Sheet for ETD’s professional development workshops, with ability to skip if the teacher’s email is not found, , estimated clicks, 170 teachers x 10 clicks = 1700.

For scalability across MOE, we created YouTube  explainers and working scripts-texts  (without sensitive data ) to help other MOE colleagues to learn so that anyone in MOE can “take over” the project and run the workflow or adapt them to suit different MOE work-streams. We even volunteered to help with MOE Olive Fiesta 2021 fund-raising efforts by showcasing our RPA flows as “magic” to excite and inspire.

 

Benefits and impact to stakeholders 

Benefits to MOE, Cost Savings 

Building such a full automation on SLS front-end itself might have costed an estimated \)80,000 upfront and $5,000 for yearly maintenance. Besides, SLS funds are strictly for meeting high number of user needs (35,000 teachers and 500,000 students) for higher return of investments. Spending funds on a time-saving feature like our project would have impacted only 150 MOE-HQ officers serving as SLS CG lesson reviewers, and thus, unlikely to be prioritised for a built soon. This gives a strong case for RPA to come into this part of the SLS workflow.

Benefits to CG Chief Admin: 

    a) Automate Mundane and Rule-based Work

Direct benefits are for the members of the project who have to do the following routine tasks: get the CG lessons data, assignment of CG lessons to reviewers if it is not assigned yet, send emails to reviewers with instructions and URL for quick reviews, send email reminders for reviews than are still in the pending review tab after 5 days after the teacher’s submissions.

   b)  Life-Long Digital Learning

Instead, we used our digital literacy skills in coding, and automate all flows into a single click on the computer. Of course this requires more digital skills, but we argue it is far better a learning experience for us to use our brain to problem-solve and code than to do mundane manual clicking and rule-based assignments.

   c)  Higher job satisfaction and assignment-email with accuracy and fairness

Estimated 1500 CG lessons were assigned out automatically using TagUI from Apr to Dec 2021, on every work-day basis (190 times), and 8 emails approximately every time, with auto email reminders (2 emails daily). If the team had to do all these manually by hand, the job satisfaction would be very low. In addition, the accuracy of the emails and fairness of assignment of lessons to reviewers has been given far better results than if done by hand. We estimate a time saving of 30-60 minutes per day depending on the number of new pending review lessons that come in to CG of SLS.

Benefits to 150 CG lesson reviewers

Our CG lesson reviewers have received personalised emails to ask them to click on a single SLS URL link and can start their review process. The RPA also send emails after detecting number of working days greater than 5 days after teacher’s lesson submission date, to gently email reminders them to remember to do the lesson review by the next working day. 

Sustainability of solution 



Zero dollars and always Evolving/Expanding using GitHub 
The solution would be sustained beyond its implementation phase using zero dollars through an ETD Lead Specialist’s technical know-how. We are in the process of exploring a WOG TagUI whitelisting (using This email address is being protected from spambots. You need JavaScript enabled to view it. instead of a generic email address This email address is being protected from spambots. You need JavaScript enabled to view it. ) Update!! Possible after whitelisting by ITD . With each automation made, we will publish the file *.tag and other dependencies files *.csv, which is just text and code and YouTube URL, to share the source codes with MOE officers on GitHub. 
 
Sharing at MOE-Charity Event
We also shared about TagUI capabilities during the 2021 Sept 09th OLive Fiesta SPICE Carnival - Talent Showcase MOE - Robotic Magic TagUI. The participants were very excited about TagUI’s computer and web automation and we believe we helped MOE colleagues to be aware of the free RPA can offer in terms of higher accuracy, savings in time and cost, ease of writing code etc. 
 
YouTube TagUI MOE playlist for scaling
We created YouTube  playlists of videos and working scripts-texts  (without sensitive data ) to help other colleagues in MOE to learn and share knowledge. We are also continuously experimenting and exploring other use cases of TagUI for routine MOE work streams and we will prepare video tutorials so that anyone in MOE can adapt the project and run the workflow to suit different work requirements.
 
Scalability to other MOE-SLS work
We also automated the adding of test questions (Multiple Choice and Free Response) drawing the data from a CSV file, to support population of question bank for Primary Mathematics for CPDD’s adaptive learning system (ALS) efforts. 
 
Additionally, we automated adding 170 teachers from an Excel Sheet for ETD’s professional development workshops, with ability to skip if the teacher’s email is not found, has saved a lot of mundane – 170 teachers x 10 clicks = 1700 clicks.
 
Lastly, we also automated the sharing of lessons  in SLS “My Drive” to different teachers for co-creation of lessons or activity templates, which becomes painful when sharing 20 lessons to 20 teachers = 400 sharing x 10 clicks = 4000 clicks. 
 
Almost limitless RPA for Desktop Apps and Chrome Browser 
The longer-term plans for the project could be to automate all other mundane rules-based clicking on SLS, and the project can also be the MOE community champion for use of TagUI-RPA for MOE’s digital transformation efforts, such as clicking using computer vision clicks on computer desktops apps and Chrome browser webpage automation etc. 
 
TagUI RPA Professional Development Community-Champion
The team is also ready to support others’ Professional Development in the use of TagUI as well as point them towards readily available resources including reference documentation, a free beginners and soon-to-be-ready intermediate online certification course  to pick up the use of the open-source tool without the need for prior programming knowledge. We strive to help all MOE colleagues to digitally transform their own repetitive workflows where possible, using this free RPA and “Learn for Life” for higher job satisfaction. We can also share on Workplace to help build a community of RPA enthusiasts and transform work processes together.
 
Please attach all your Annexes and supporting documents (e.g. photos, links to website, graphs, etc) here, where applicable. 
 
 
1. TagUI-AI Singapore https//aisingapore.org/tagui/ 
2. Installation Windows, Mac, Linux  https://tagui.readthedocs.io/en/latest/setup.html 
3. UiPath paid RPA https://www.uipath.com/pricing 
4. TagUI GitHub issues-discussions https://github.com/kelaberetiv/TagUI 
5. TeleGram TagUI-RPA https://web.telegram.org/z/#-1299183642" style="color: rgb(0, 158, 184); font-family: "Helvetica Neue Light", HelveticaNeue-Light, "Helvetica Neue", Helvetica, Arial, sans-serif; outline: none; text-decoration: none; transition: color 0.3s ease 0s; display: inline;">chathttps://web.telegram.org/z/#-1299183642 
8. GitHub of SLS TagUI flows https://github.com/lookang/TagUI 
9. TagUI saves id and password on local computer , not visible in *.tag files, increases security https://www.youtube.com/watch?v=5wLWNB_ucbc&list=PLYIwRBA8ZhdPh8W68XCB1gEMv8A0Bl7Qk&index=7 
10. https://github.com/lookang/TagUI/tree/main/SLSvle extract SLS user data to verify user with CG admin roles
11. https://github.com/lookang/TagUI/tree/main/share share many lessons with many different emails, automated
12. TagUI auto post Multi-choice and free response questions in CSV to SLS https://www.youtube.com/watch?v=KTExNaIGDfI&list=PLYIwRBA8ZhdPh8W68XCB1gEMv8A0Bl7Qk&index=12
15. Free AI Singapore TagUI beginner online certification course https://learn.aisingapore.org/courses/learn-rpa-with-tagui-beginners-course/
16. WOG Tagui flows https://github.com/lookang/TagUI/tree/main/login the files has wog text in them
 
 
 
Photo of Sharing Event of 20210909 OLive Fiesta SPICE Carnival - Talent Showcase MOE - Robotic Magic TagUI a free tool for Robotics Process Automation (RPA) to 50 participants from MOE.
 
 

Featured on https://github.com/kelaberetiv/TagUI as a demonstration of sustainability of our solution goes beyond MOE to scale. Lawrence Wee – Second Top Left. 


 
Documentation with YouTube tutorials of full end to end SLS community gallery lesson reviews assignment https://weelookang.blogspot.com/2021/07/almost-full-automation-using-tagui-and.html ensures reproducibility of our RPA to other parts of MOE work-streams.
 
 

 
Documentation on TagUI the RPA for SLS test paper automation posting of questions into SLS https://weelookang.blogspot.com/2021/10/tagui-rpa-for-sls-test-paper-automation.html
 

 
Documentation on SLS Creating a Class Group using CSV using TagUI to automate adding teachers as students https://weelookang.blogspot.com/2022/02/sls-creating-class-group-using-csv.html 
 
WhatsApp discussion with the ex-ETD officer who use UiPath extensively, who also won an innergy award in 2020 for the work on UiPath-RPA, suggesting why TagUI is “more stable” (mostly a text *.tag file and *.csv) while UiPath is “slow and buggy with lots of overheads due to graphics” for computer vision clicking. 
 
Screenshot of RPA email to reviewers with personalised title and clickable URL in table format, easy for lesson reviewers to do their reviews
 
 
Screenshot of RPA email gentle reminder that is trigger after detection of greater than 5 working days after teacher’s lesson submission date from day of email sent. The RPA searches the email and reply all, ensure all data is intact.
 
Screenshot of email from one of the reviewers that they missed out the first assignment email and appreciate our “streaming process” such as RPA lesson review, publishing CG e-Catalogue of all 7000+ CG lessons that we can also RPA out from SLS webpages etc.
https://weelookang.blogspot.com/2022/02/free-robotic-process-automation-rpa.html?view=sidebar

How does the TagUI solution automate the SLS lesson review workflow?

The automation involves a combination of TagUI, JavaScript, and Python. TagUI is used to log in to SLS and collect lesson data. JavaScript is then used to automatically assign reviewers to lessons based on criteria like their subject interests and grade levels. Python is employed for file manipulation and intelligently copying the selected data back to a Google Sheet database. The workflow can be triggered with a single click and scheduled. The automation also includes searching emails and sending gentle reminders to reviewers who haven't completed their reviews within 5 working days.

What are the key benefits of using TagUI for this project?

The TagUI project offers several significant benefits. It saves considerable time and effort for the SLS team by automating mundane and rule-based tasks, leading to higher job satisfaction and allowing staff to focus on more problem-solving activities. It ensures accuracy and fairness in the assignment process and email communications. For the reviewers, they receive personalized emails with direct links and timely reminders. Furthermore, using a free and open-source tool like TagUI is significantly more cost-effective than commercial RPA solutions.

How is the TagUI solution being sustained and scaled across MOE?

The solution is sustained using zero dollars and is continuously evolving through GitHub, where the project's "code-in-words" and dependencies are shared. The team is actively exploring whitelisting TagUI and Python for use on Whole of Government (WOG) and School Standard Operating Environments (SSOE) computers to enable wider adoption within MOE. To facilitate scaling, they have created YouTube explainer videos and provide working scripts-texts on GitHub, enabling other MOE colleagues to learn and adapt the workflow. They also volunteer to support professional development in TagUI usage and aim to build a community of RPA enthusiasts within MOE.

Beyond the SLS lesson review process, what other tasks has TagUI been used to automate within MOE?

The project has successfully automated several other tasks within MOE. This includes adding test questions from a CSV file to populate question banks for adaptive learning systems in Primary Mathematics, and automating the process of adding teachers from an Excel sheet to professional development workshops, skipping if an email is not found. Another automation involves sharing lessons in SLS "My Drive" with different teachers for collaborative lesson creation.

What makes the TagUI solution innovative?

The TagUI solution is considered innovative due to its use of a free and open-source tool in a government setting, overcoming potential IT policy resistances. Its use of natural language-like syntax in English, easily convertible to other languages, makes it accessible. The ability to write scripts in common editors and its compatibility across different operating systems enhance its usability and scalability. The integration with JavaScript and Python allows for handling more complex functionalities. The approach of using a cost-effective, perpetual solution instead of expensive commercial alternatives is also a key innovation.

How did the team overcome the challenges faced during the project?

The team overcame challenges by embracing a spirit of dare and taking calculated risks by exploring a free and open-source tool after their initial attempt with a commercial tool failed. They leveraged their prior experience with open-source tools and applied learnings from the failed attempt. Crucially, they collaborated closely with the TagUI AI Singapore team for technical support, troubleshooting issues through GitHub discussions, Telegram community chats, and Zoom sessions, which allowed them to quickly address blockers and expand the automation's capabilities.

 
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