ILR is the Individualised Learner Record. Learners (students, apprentices etc), mainly 16 to 19 and not covered under the school census or Higher Education funding, enrol with an organisation that has registered to receive ESFA funding and for which they submit regular data on who their learners are via the Submit Learner Data (SLD) portal. The criteria for this XML submission is set by the ILR documents and reflect ESFA policy and funding rules.
Challenge
Challenge
Challenge
The most calls to the help desk are from apprenticeship training providers doing their first few submissions using ESFA’s free Learner Entry Tool (LET). These providers may only discover there is a problem once their funding arrives as they may not have realised that the presence of an error will mean a learner is disqualified. At this point they google for help and approach forums, networks and the ESFA helpdesk. The data teams for FE colleges and large independent training providers (ITP) understand funding and data returns, with each new staff member learning from the previous role holder. New small training providers (possibly employer providers) struggle to know how to submit their ILR data and how to find guidance. Unfortunately, the Territorial Team members are limited in the support time they can provide and a few are not providing clear up-to-date information. There is no central ESFA page that provides updated information on how to do a submission, what the purpose of each document is, where to find the relevant tools and where to find authoritative help for their particular situation.
The most calls to the help desk are from apprenticeship training providers doing their first few submissions using ESFA’s free Learner Entry Tool (LET). These providers may only discover there is a problem once their funding arrives as they may not have realised that the presence of an error will mean a learner is disqualified. At this point they google for help and approach forums, networks and the ESFA helpdesk. The data teams for FE colleges and large independent training providers (ITP) understand funding and data returns, with each new staff member learning from the previous role holder. New small training providers (possibly employer providers) struggle to know how to submit their ILR data and how to find guidance. Unfortunately, the Territorial Team members are limited in the support time they can provide and a few are not providing clear up-to-date information. There is no central ESFA page that provides updated information on how to do a submission, what the purpose of each document is, where to find the relevant tools and where to find authoritative help for their particular situation.
The most calls to the help desk are from apprenticeship training providers doing their first few submissions using ESFA’s free Learner Entry Tool (LET). These providers may only discover there is a problem once their funding arrives as they may not have realised that the presence of an error will mean a learner is disqualified. At this point they google for help and approach forums, networks and the ESFA helpdesk. The data teams for FE colleges and large independent training providers (ITP) understand funding and data returns, with each new staff member learning from the previous role holder. New small training providers (possibly employer providers) struggle to know how to submit their ILR data and how to find guidance. Unfortunately, the Territorial Team members are limited in the support time they can provide and a few are not providing clear up-to-date information. There is no central ESFA page that provides updated information on how to do a submission, what the purpose of each document is, where to find the relevant tools and where to find authoritative help for their particular situation.
Process
Process
Process
As the sole product lead on the UK Government's Education & Skills Funding Agency, I began the design process for the search functionality of rules in filing for funds by organizations looking to receive funding from the government by first identifying the stakeholders involved in the process. This included representatives from the funding organizations, as well as any other relevant parties such as government officials and external consultants.
From there, I worked closely with these stakeholders to determine their needs and requirements for the search functionality and used this information to develop a comprehensive design plan. This plan took into account factors such as the types of information that users would be searching for, the most common use cases for the functionality, and the overall user experience.
Throughout the design process, I maintained ongoing communication and collaboration with the stakeholders to ensure that the final product met their needs and expectations. This included regular feedback sessions, testing and iteration of prototypes, and ongoing user research to continually improve the functionality over time. By working closely with stakeholders throughout the design process, we ensured that the search functionality met the needs of all parties involved and helped to streamline the process of geting information and filing for funds easier.
This collaboration enhanced the user experience and ensured the successful integration of ML algorithms into the search functionality. The deatiled process is defined below:
Research and User Understanding: I started by conducting user research to gain insights into users' queries and pain points. This research involves user interviews, surveys, and usability testing. I collaborated with ML engineers to identify relevant data sources and explore ML techniques to analyze and understand user input queries based on their funding records.
Defining Design Goals and Constraints: I collaborated with ML engineers to define the design goals and constraints based on the ML algorithms' capabilities and limitations. We aligned on factors such as accuracy, diversity of search result output, user control over filtering search results, and the balance between novelty and familiarity in the search results.
Wireframing and Prototyping: I created wireframes and interactive prototypes to visualize the user interface and user flow of the advanced search functionality. I worked closely with ML engineers to understand the data inputs and outputs of the ML models and ensure the design accommodates the ML-driven search results seamlessly.
Designing the Search Functionality Experience: I collaborated with ML engineers to design the advanced search experience. I determined the presentation of options and rules associated with funding, such as age of the learner, aprenticsip or traineeship, or unemplyed or a restart. I ensured the presentation is visually appealing, intuitive, and aligned with the user's mental model.
Iterative Testing and Feedback: I collaborated with ML engineers to integrate the ML algorithms into the product prototype. I conducted iterative testing and gather user feedback to evaluate the effectiveness of the search functionality and refine the design. ML engineers analyze the feedback and make necessary adjustments to the algorithms to improve the accuracy and relevance of search results.
Optimization and Performance: We collaborated to optimize the advanced search functionality's performance and considered factors such as response time, scalability, and computational resources required by the ML algorithms. I ensured that the design does not compromise the system's performance while providing a seamless user experience.
Continuous Improvement: The collaboration between our team extended beyond the initial development phase. We continued to work together to monitor user feedback, evaluate the system's performance, and make iterative improvements. I conducted user research to identify evolving user needs and preferences, feeding into the ML engineers' efforts to enhance the advanced search algorithms.
As the sole product lead on the UK Government's Education & Skills Funding Agency, I began the design process for the search functionality of rules in filing for funds by organizations looking to receive funding from the government by first identifying the stakeholders involved in the process. This included representatives from the funding organizations, as well as any other relevant parties such as government officials and external consultants.
From there, I worked closely with these stakeholders to determine their needs and requirements for the search functionality and used this information to develop a comprehensive design plan. This plan took into account factors such as the types of information that users would be searching for, the most common use cases for the functionality, and the overall user experience.
Throughout the design process, I maintained ongoing communication and collaboration with the stakeholders to ensure that the final product met their needs and expectations. This included regular feedback sessions, testing and iteration of prototypes, and ongoing user research to continually improve the functionality over time. By working closely with stakeholders throughout the design process, we ensured that the search functionality met the needs of all parties involved and helped to streamline the process of geting information and filing for funds easier.
This collaboration enhanced the user experience and ensured the successful integration of ML algorithms into the search functionality. The deatiled process is defined below:
Research and User Understanding: I started by conducting user research to gain insights into users' queries and pain points. This research involves user interviews, surveys, and usability testing. I collaborated with ML engineers to identify relevant data sources and explore ML techniques to analyze and understand user input queries based on their funding records.
Defining Design Goals and Constraints: I collaborated with ML engineers to define the design goals and constraints based on the ML algorithms' capabilities and limitations. We aligned on factors such as accuracy, diversity of search result output, user control over filtering search results, and the balance between novelty and familiarity in the search results.
Wireframing and Prototyping: I created wireframes and interactive prototypes to visualize the user interface and user flow of the advanced search functionality. I worked closely with ML engineers to understand the data inputs and outputs of the ML models and ensure the design accommodates the ML-driven search results seamlessly.
Designing the Search Functionality Experience: I collaborated with ML engineers to design the advanced search experience. I determined the presentation of options and rules associated with funding, such as age of the learner, aprenticsip or traineeship, or unemplyed or a restart. I ensured the presentation is visually appealing, intuitive, and aligned with the user's mental model.
Iterative Testing and Feedback: I collaborated with ML engineers to integrate the ML algorithms into the product prototype. I conducted iterative testing and gather user feedback to evaluate the effectiveness of the search functionality and refine the design. ML engineers analyze the feedback and make necessary adjustments to the algorithms to improve the accuracy and relevance of search results.
Optimization and Performance: We collaborated to optimize the advanced search functionality's performance and considered factors such as response time, scalability, and computational resources required by the ML algorithms. I ensured that the design does not compromise the system's performance while providing a seamless user experience.
Continuous Improvement: The collaboration between our team extended beyond the initial development phase. We continued to work together to monitor user feedback, evaluate the system's performance, and make iterative improvements. I conducted user research to identify evolving user needs and preferences, feeding into the ML engineers' efforts to enhance the advanced search algorithms.
As the sole product lead on the UK Government's Education & Skills Funding Agency, I began the design process for the search functionality of rules in filing for funds by organizations looking to receive funding from the government by first identifying the stakeholders involved in the process. This included representatives from the funding organizations, as well as any other relevant parties such as government officials and external consultants.
From there, I worked closely with these stakeholders to determine their needs and requirements for the search functionality and used this information to develop a comprehensive design plan. This plan took into account factors such as the types of information that users would be searching for, the most common use cases for the functionality, and the overall user experience.
Throughout the design process, I maintained ongoing communication and collaboration with the stakeholders to ensure that the final product met their needs and expectations. This included regular feedback sessions, testing and iteration of prototypes, and ongoing user research to continually improve the functionality over time. By working closely with stakeholders throughout the design process, we ensured that the search functionality met the needs of all parties involved and helped to streamline the process of geting information and filing for funds easier.
This collaboration enhanced the user experience and ensured the successful integration of ML algorithms into the search functionality. The deatiled process is defined below:
Research and User Understanding: I started by conducting user research to gain insights into users' queries and pain points. This research involves user interviews, surveys, and usability testing. I collaborated with ML engineers to identify relevant data sources and explore ML techniques to analyze and understand user input queries based on their funding records.
Defining Design Goals and Constraints: I collaborated with ML engineers to define the design goals and constraints based on the ML algorithms' capabilities and limitations. We aligned on factors such as accuracy, diversity of search result output, user control over filtering search results, and the balance between novelty and familiarity in the search results.
Wireframing and Prototyping: I created wireframes and interactive prototypes to visualize the user interface and user flow of the advanced search functionality. I worked closely with ML engineers to understand the data inputs and outputs of the ML models and ensure the design accommodates the ML-driven search results seamlessly.
Designing the Search Functionality Experience: I collaborated with ML engineers to design the advanced search experience. I determined the presentation of options and rules associated with funding, such as age of the learner, aprenticsip or traineeship, or unemplyed or a restart. I ensured the presentation is visually appealing, intuitive, and aligned with the user's mental model.
Iterative Testing and Feedback: I collaborated with ML engineers to integrate the ML algorithms into the product prototype. I conducted iterative testing and gather user feedback to evaluate the effectiveness of the search functionality and refine the design. ML engineers analyze the feedback and make necessary adjustments to the algorithms to improve the accuracy and relevance of search results.
Optimization and Performance: We collaborated to optimize the advanced search functionality's performance and considered factors such as response time, scalability, and computational resources required by the ML algorithms. I ensured that the design does not compromise the system's performance while providing a seamless user experience.
Continuous Improvement: The collaboration between our team extended beyond the initial development phase. We continued to work together to monitor user feedback, evaluate the system's performance, and make iterative improvements. I conducted user research to identify evolving user needs and preferences, feeding into the ML engineers' efforts to enhance the advanced search algorithms.
Solution
Solution
Solution
A prospective solution that was considered for improving the search functionality of rules in filing for funds by organisations looking to receive funding from the UK Government's Education & Skills Funding Agency. This involved implementing a more advanced search algorithm and user interface.
The algorithm was designed to use machine learning and natural language processing to better understand the user's search queries and provide more accurate results. This was intended to reduce the time and effort required for users to find the information they need, ultimately improving the user experience and increasing efficiency in the filing process.
Additionally, the user interface was redesigned to be more intuitive and user-friendly, with clear navigation and visual cues to guide users through the search process. This included features such as predictive search suggestions, advanced filtering options, and personalized recommendations based on the user's search history and preferences.
Overall, these improvements were intended to simplify the process of filing for funds and make it more accessible to a wider range of organizations.
A prospective solution that was considered for improving the search functionality of rules in filing for funds by organisations looking to receive funding from the UK Government's Education & Skills Funding Agency. This involved implementing a more advanced search algorithm and user interface.
The algorithm was designed to use machine learning and natural language processing to better understand the user's search queries and provide more accurate results. This was intended to reduce the time and effort required for users to find the information they need, ultimately improving the user experience and increasing efficiency in the filing process.
Additionally, the user interface was redesigned to be more intuitive and user-friendly, with clear navigation and visual cues to guide users through the search process. This included features such as predictive search suggestions, advanced filtering options, and personalized recommendations based on the user's search history and preferences.
Overall, these improvements were intended to simplify the process of filing for funds and make it more accessible to a wider range of organizations.
A prospective solution that was considered for improving the search functionality of rules in filing for funds by organisations looking to receive funding from the UK Government's Education & Skills Funding Agency. This involved implementing a more advanced search algorithm and user interface.
The algorithm was designed to use machine learning and natural language processing to better understand the user's search queries and provide more accurate results. This was intended to reduce the time and effort required for users to find the information they need, ultimately improving the user experience and increasing efficiency in the filing process.
Additionally, the user interface was redesigned to be more intuitive and user-friendly, with clear navigation and visual cues to guide users through the search process. This included features such as predictive search suggestions, advanced filtering options, and personalized recommendations based on the user's search history and preferences.
Overall, these improvements were intended to simplify the process of filing for funds and make it more accessible to a wider range of organizations.
© 2023 Lehar. All Rights Reserved.
© 2023 Lehar. All Rights Reserved.