Active learning ideas
Once a problem is identified, the next step is designing the logic to solve it. This topic focuses on creating algorithms and flowcharts that are efficient, fair, and effective. Students learn to visualize the 'flow' of their solution before touching a keyboard, which is a core requirement of NCCA Learning Outcomes 2.3 and 2.4.
Activity 01
Students act as different parts of an algorithm (e.g., an 'if' statement or a 'loop'). They stand on floor markers and move based on inputs provided by the teacher to see if the logic reaches the intended social outcome.
How do we design an algorithm to address a specific social need?
Activity 02
Groups swap their flowcharts for a community app. They must try to find a 'path' through the logic that would result in an unfair outcome for a specific user, then suggest a fix to the original designers.
What makes an algorithm fair and unbiased?
Activity 03
One student explains their algorithm's logic using only a flowchart, while their partner tries to 'run' the logic on paper. If the partner gets stuck, the designer must refine the flowchart for better clarity.
How can flowcharts help us visualize social processes?
A few notes on teaching this unit
Watch Out for These Misconceptions
Flowcharts are just an extra step and aren't really necessary for coding.
Use a complex logic puzzle to show how easy it is to get lost without a visual map. Students who use flowcharts first usually spend significantly less time debugging their actual code later.
An algorithm is only 'good' if it is the shortest possible path.
In social projects, the 'shortest' path might be unfair or inaccessible. Encourage students to discuss 'trade-offs' between speed and inclusivity during their design phase.
Methods used in this brief
Identifying Community Challenges
Use computational thinking to break down complex social or environmental issues within the local community. Formulate problem statements that can be addressed through technological intervention.
8 methodologies
Analyse Data Sets
Collect and analyse data sets to draw meaningful conclusions for your programming project.
8 methodologies