Active learning ideas
Data-driven programming is where the logic of code meets the structure of databases. Students learn to use Structured Query Language (SQL) to perform CRUD (Create, Read, Update, Delete) operations, allowing their applications to store and retrieve persistent data. This topic bridges the gap between a static program and a dynamic application that can handle thousands of users and complex datasets.
Activity 01
Students work in small groups to solve a 'crime' by querying a large database. They must use complex SELECT statements with JOINs and WHERE clauses to filter through suspects, locations, and times, sharing their successful queries with the class.
How do applications communicate with databases?
Activity 02
Pairs are assigned one of the CRUD operations. They must create a 2-minute 'lightning talk' explaining the syntax and a common pitfall for their command, then demonstrate it by updating a shared class database.
What are the core SQL commands for data manipulation?
Activity 03
Students are shown a simple login form and must work in pairs to 'hack' it using basic SQL injection techniques (in a controlled, local environment). This hands-on approach makes the importance of 'sanitising inputs' and prepared statements immediately clear.
How do we handle data securely in code?
A few notes on teaching this unit
Watch Out for These Misconceptions
SQL is just another programming language like Python.
Students often try to use loops in SQL. Peer-led demonstrations can show that SQL is 'declarative' (telling the computer what you want) rather than 'procedural' (telling it how to do it), which is a major shift in thinking.
You can just concatenate strings to build SQL queries.
This is a dangerous habit that leads to security vulnerabilities. Active 'security audits' of each other's code help students identify why using 'parameterised queries' is the only professional way to handle user data.
Methods used in this brief
Data and information
Students explore how data is collected, stored, and transmitted in digital systems. They analyse the difference between raw data and meaningful information.
8 methodologies
Relational database concepts
Students design relational databases using entity-relationship diagrams and data dictionaries. They understand primary keys, foreign keys, and data integrity.
8 methodologies
Evaluating digital solutions
Students evaluate their data-driven applications against prescribed criteria and user requirements. They propose refinements based on testing outcomes.
8 methodologies