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Kease

Kease recognises that a business transformation isn’t just about technology. There must be a strong focus on the people that drive the change. 

Problem

The current market does not have a tool that drives behavioural change. Learning management systems are based on consuming content and answering questions. Learning is performed and lost without having any real impact on the organisation.

Solution

We built a responsive web application that helps organisations improve their leadership capability. Users progress through a baseline assessment to determine which areas require upskilling. Based on the results, users can select a learning focus. This is the current skill they are focusing on.

At the end of each learning module, users are tasked with a challenge. A challenge embeds the lessons learned into a practical environment. There are a range of challenge types that vary based on the type of learning. Users are encouraged to journal and take notes while completing challenges to document learnings.
Sarah is the managing director of Kease. She has built a successful business focused on teaching organisations about human centred leadership. This is the first program available on the Kease portal. The variety of programs is always increasing, giving users greater flexibility over their learning journey.

From an organisational perspective, HR have an overview of how employees are interacting with their learning focus. This brings a level of visibility across an organisation and how they are investing in their staff. It also maps out a staff members learning journey should there be an opportunity for a promotion. 

Process

Kease adopted a fixed time, variable scope approach to the project. This meant prioritising the backlog in order to meet a hard deadline. The application needed to be up and running in six weeks to be showcased during a number of keynote presentations. WorkingMouse delivered the project on time and on budget.

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The Kease portal was built by second generation codebot, Lampbot.
  • 79 requirements completed
  • Lines of code written by Lampbot: 112,649 (89.26%)
  • Lines of code written by humans: 13,559 (10.74%)

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