history of the PBL Curriculum
The YEESI Curriculum was initially written by Dr. Fue and then, the meeting to discuss the idea and develop a full curriculum was set.
Several meetings in September 2021 were held. Dr Fue together with Prof Sanga, Prof Mtega, Dr Barakabitze, Dr Banzi, Dr Maurice and Mr Mkwazu took a retreat to develop full Student-centered Problem-Based Learning for Machine Vision in Agriculture.
The developed curriculum was then conveyed in front of the PBL instructors who added ideas and refined the curriculum.
The materials were initially developed by the team but instructors will develop more materials and problem-based exercises to be used by the students
The e-Learning Portal
The e-Learning Portal was deployed at (globally) http://18.104.22.168:8390/ and http://10.10.97.26:8390/ (locally in SUA's LAN) to share materials and knowledge in AI.
Follow the TimeTable: https://docs.google.com/spreadsheets/d/1YaoiOqUnbxR0taPfSx6n6NWbl0A9WBUEYwU61s4caZM/edit?usp=sharing
Programme expected learning outcomes and the associated teaching/learning activities
In Technical Skills
● Design, implement, evaluate and test software, and machine vision systems in agriculture
● Apply mathematical analytics to solve scientific problems related to machine vision in agriculture
● Adhere to professional conduct and ethics in deploying machine vision solutions in Tanzania
● Engage in research and other advanced training in machine learning models
● Analyze business perspectives of machine vision in agriculture and establish start-up companies
● Carry their duties effectively while adhering to safety and accomplish tasks to desired goals
● Be able to work in a team to accomplish the mission and shared goals
● Guide and direct machine vision technicians and personnel
● Communicate effectively in speaking, listening, and writing
● Be able to assume management leadership roles in industrial settings to deliver machine vision projects
● Be able to work under pressure and tight deadlines.
● Possess in-depth understanding of the machine vision systems operations
● Possess a detailed understanding of deploying mobile app solutions in a complex environment
● Possess detailed knowledge of professional digital agriculture
● Demonstrate ability to develop competitive systems under constraints budgets using open-source tools
● Possess excellent time management skills in project management
● Understand the ethical operations of automated machine vision solutions in industries
● Demonstrate advanced skills in software systems development