Clusters: Learning space booking service.
Learning spaces promote active learning permitting the discussion of real-world situations and urging involvement in research and debates. This project focused on designing a service that predicts and books formal and informal learning spaces for a higher education institution.
Clusters is a real-time intelligent learning space prediction and booking service. The real-time capability of the service constitutes its ability to learn as situations occur and make predictions and finally, suggestions based on this learned information. It uses real-time GPS locations and predictive analytical algorithms to provide the availability and capacity of learning spaces within a higher education institution.
Figma was used as the main design tool and I made use of user journey mapping techniques, user personas and a usability study to substantiate this project.
The Challenge
This service faces the challenge of making the education process a smoother and more collaborative effort through easy access to learning spaces. Could this be achieved by the user-centred approach of designing focusing on a primary stakeholder, students, primary touch point, learning spaces and a primary service component, a mobile phone with internet access and GPS all the while maintaining the intelligent component of the service?
Requirements
Role: Service Designer, User Researcher
Methods: Interviews, surveys, synthesis wall, ideation, brainstorming, journey map, service blueprint, prototype.
Tools: Figma, Adobe Illustrator
Timeline: 3 Months
Process
Secondary Study
Case study
Low-Fidelity Prototype
Mid-High Fidelity Prototype
User Study and Evaluation
Conclusion
Identify tools for research data collection, create personas and journey maps and user studies to justify service design.
Identify and focus on stakeholders, technologies, services providers, third parties, the web/cloud and the products.
Pinpoint some critical values such as retaining consistency throughout every touch point.
Design and develop interactive user interface prototypes employing the identified elements, material design components, and Figma.
Aims and Objectives
Findings of Secondary Study
Digital literacy is now an essential requirement in education. It has influenced the adaptation of university procedures to the guidelines of The European Higher Education Area. Recognizing the connection between technological advancements, higher education, and economic development leads to a basic understanding of the relevance of an intelligence-based higher education learning space service. The secondary research examined the various literature and knowledge relevant to the various sectors being implemented in the service project.
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Artificial intelligence (AI) is an emerging technology targeted at the creation of computational systems that offer intelligent, predictive, and adaptive behaviours. AI can learn from their environment and situations they have been emersed in, simulating the behaviour of human beings. The implementation of artificial intelligence techniques in education has been the subject of academic research for over 30 years.
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What students learn is significantly influenced by the methods they use to learn. It was discovered that most students who studied, learn best through active, cooperative, small-group associations inside and outside the classroom. The analysis of small-group learning practices suggested that greater time spent partaking in groups leads to more positive attitudes among students overall and that even minimal group work can have positive effects on student achievement. Furthermore, small-group learning can reduce the level of difficulty in STEM courses and programs considerably by 22%.
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Scientists have developed a middleware framework that integrates multiparameter and multiple interfaces that facilitates the monitoring of psychological and physical medical data. This can be called applied in a form dorm of a mobile application for health purposes (mHealth). Understanding the basic architecture of mhealth apps is vital in realizing the requirements for the user interface of the mobile health application to be delivered.
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User-Centred
Service must be experienced using the customer as the primary focus. The service must include all affected by the system including the service providers, all stakeholders and even noncustomers affected by the service.
Co-creative
Services must be designed with the active inclusion and participation of all stakeholders. This collaborative approach emphasises the value of co-creation as dictated by the current user experience economy.
Holistic
Service must be seen as an ecosystem of processes working together to achieve an aim. It is relevant to an institution’s identity and encompasses the complete desires of all stakeholders (Stickdorn, 2018).
Sequencing
A service should be seen as a sequence of related activities and components. We consider the relationships between various steps and touchpoints that constitute the customers’ experience.
Evidencing
Though services are not usually physical in nature, they must be visualized through
tangible artefacts. Services must be based in reality and must be experienced in real
life in order to be deemed valuable.
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Understanding what constitutes an intelligent service, this project with its ability to learn from the environment using AI components, falls into the category of an intelligent service. Throughout the construction of the service design cases, we consider some critical values such as retaining consistency throughout every touch point, facilitating the usability of the service, enhancing the experience for all stakeholders, generating value with the service, and promoting longterm and short-term strategic thinking.
Scoping Project
Stakeholders
University administrators
Students
Learning space administrators
Venders
Students from other universities
Lecturers
Service Components
Machine learning algorithms
Mobile phone
Internet
GPS
Application
Internet service providers
Google play store/App store
Touchpoints
All indoor and outdoor learning spaces.
Advertisement on Social media
Constraints
Integration issues due to students being stuck in their old ways of accessing learning spaces.
Scepticism about the privacy of use of a location-based service.
Personas
They are derived from actual users and data collected from user research. The data used in developing the following personas is derived from reviewed literature and user case studies based on collaborative learning, learning spaces, higher education institutions and observations considering the various stakeholders.
Scenarios
Scenarios 1: Kevin, 25, a master’s student at the University of Leicester, needs a reliable place to study on various days. He is writing his thesis and has no time to go from learning space to learning space in the hopes that the space is available and quiet. He needs a place with academic resources that does not become unusable due to weather conditions. He prefers not to be in a crowded area.
He wants something convenient like a real-time learning space availability prediction and booking services that can secure a learning space for him on the university campus at a period when the space in not crowded. He is a planner and does not mind how far the location is from his home as long as he gets to book in advance. He has reliable internet access but does not like to carry his laptop around.
Scenario 2: Amy, 17, is a newly enrolled student at the University of Leicester. She is unfamiliar with the campus and has not met a lot of people studying her course. She is very introverted and likes to study in groups as it helps her grasp concepts faster. She also wants to make a lot of friends and likes being outdoors.
She wants a convenient service that shows her where a lot of people are gathered to increase her chances of making friends. The more people the better. She however does not feel comfortable travelling a long distance and would like a learning space close to her accommodations. She is not very good at planning and thus wants a service that would tell her what is happening in real time. She likes to go to the learning spaces with her flatmates.
User Journey map
I visually represent a user’s journey through the service, indicating all the different interactions they have. This will allow us to notice what parts of the service the user likes and which parts we have to improve upon.
The student may first become aware of the service by touchpoints such as interacting with administrative staff or from advertisement by university officials or social media.
They then join the service by using service components such as internet, app store or play store and sign up to the service using a software product in a form of a mobile or web application.
After interacting with the application or product, the user books an indoor or outdoor learning space based on their location, number of the students and preference.
The intelligent component, the machine learning algorithm, learns from and matches the students based on preferences, choices, availability, and predictions.
The user then interacts with a stakeholder in the form of the learning space administrator who then proves access to the primary touchpoint, the learning space
Low/Mid Fidelity Prototype
User/Primary Study
Overview
A remote moderated usability test was conducted by a master’s student at the University of Leicester on the 6th of May, 2021, to evaluate a medium-fidelity prototype of Clusters.
Participants
Five university students participated in the study. Each participant performed 3 tasks.
Duration
The duration of each test varied greatly due to the choices made by each participant.
Sessions
Sessions varied greatly due to the type of choices made by each participant. They were handed a questionnaire that required background information and a pre-test questionnaire, and a post-test questionnaire. These were to measure the subjective measure of the participants. The questionnaire also had a section for the performative measurement of the participant by the Tester.
Each of the participants was also asked to rate the experience of the test and provide recommendations on ways through which the service could be improved.
Tasks
Task 1: Register for the service using the Clusters mobile application
Task 2: Make a booking for a learning space of your choosing.
Task 3: Make a booking for a learning space using the map feature of the service component.
Performance measurement metrics
Each participant after each task was rated on their ability to finish the task without being prompted and the speed used the finish the task. It was measured in seconds
Task Completion Success Rate
Time on Task
Overall Metric
Results
All participants were able to complete the tasks without prompting. Most were simply reminded that they had the chance to quit at any time.
Zoom was the software of choice in this test as it was the most readily available platform across the board. Participants finished each task by using a virtual copy of the service component. Task 2 took the longest to complete on average and Task 3 had the shortest time.
The service component seemed to have been a success with the participants. However, there were issues of security and privacy especially concerning the real-time location component of the service component. 100% of all participants seem to rank the overall website a 5 on a Likert scale gauging usability implying the website was indeed very usable
Evaluation and Recommendation
Most participants agreed that they would recommend the service to other students and went on to make the following recommendations:
Include cache history from the user session.
Easier map search and navigation.
Best interface or a more interactive service component with an attractive interface.
Include chatrooms to improve the sociability of the service.
The overarching picture of this intelligent service focuses on making life easier for all stakeholders and university students. The medium fidelity prototype no not completely comprehensive seemed to have conveyed the overall idea of what kind of service clusters are.
The main issue was the problem of privacy and security as with most location-based applications and services. Most students hover agree that such a service would be helpful and welcomed in an ever-changing and interconnected digital world
Conclusion
As there was no extensive development in the way of implementation, the future recommendation would be the full implementation of the design. This document attempted to apply the basic principles used to evaluate a designed service. An intelligent real-time learning space prediction and booking service, Clusters, is described and evaluated using a medium-fidelity paper prototype or wireframe. User research methodologies are adopted and analysed to produce a conclusive result about the usability of the service and recommendations are made based on received feedback..