Software Testing and Debugging

Active learning lets students experience the messiness of data collection firsthand, turning abstract ideas into concrete challenges they can troubleshoot. When students design surveys or log sensor data, they grapple with real problems like bias or missing values, which sticks better than reading about them.

• JC 2
• Computing
• 4 activities
• 25 min–45 min

Learning Objectives

1. Design a simple survey questionnaire to collect specific data on a chosen topic.
2. Compare at least two different methods of digital data storage (e.g., spreadsheet, CSV file) for a given dataset.
3. Explain the purpose of data validation in the context of survey responses.
4. Identify potential sources of bias or error in data collection methods like surveys or sensor readings.

Ready-to-Use Activities

Inquiry-Based Learning

⏱ 45 min·Small Groups

Small Groups: Survey Design Challenge

Groups select a topic like 'JC stress factors' and draft 5-7 clear questions. Create a Google Form or Microsoft Form, share the link class-wide, and collect at least 20 responses. Import results into a shared spreadsheet for initial sorting.

Prepare & details

Why is automated testing crucial in software development?

Facilitation Tip: During the Survey Design Challenge, circulate to listen for leading questions in student drafts and ask guiding questions like, 'Who might answer differently if you changed this wording?'

Setup: Tables for small groups, board for evidence

Materials: Phenomenon hook (image, anomaly, demo), Investigation protocol sheet, Data table or observation log, Findings synthesis template

• Apply
• Analyze
• Evaluate
• Self-Management
• Social Awareness

Inquiry-Based Learning

⏱ 30 min·Pairs

Pairs: Sensor Data Logging

Pairs use phone apps or school sensors to log data, such as classroom light levels over 10 minutes. Record readings in a table, then transfer to a spreadsheet. Discuss patterns and add columns for time stamps.

Prepare & details

What is the difference between black-box and white-box testing?

Facilitation Tip: For Sensor Data Logging, have pairs test their sensors before collecting data to ensure they understand the range and limits of their equipment.

Setup: Tables for small groups, board for evidence

Materials: Phenomenon hook (image, anomaly, demo), Investigation protocol sheet, Data table or observation log, Findings synthesis template

• Apply
• Analyze
• Evaluate
• Self-Management
• Social Awareness

Inquiry-Based Learning

⏱ 35 min·Whole Class

Whole Class: Data Storage Relay

Divide class into teams. Each team collects sample data via quick poll, passes to next for spreadsheet entry, then to another for formatting and basic charts. Time the relay and review the final organized file together.

Prepare & details

How does exception handling improve application robustness?

Facilitation Tip: In the Data Storage Relay, assign specific roles like 'naming files' or 'checking folders' to keep the whole class engaged in the process.

Setup: Tables for small groups, board for evidence

Materials: Phenomenon hook (image, anomaly, demo), Investigation protocol sheet, Data table or observation log, Findings synthesis template

• Apply
• Analyze
• Evaluate
• Self-Management
• Social Awareness

Inquiry-Based Learning

⏱ 25 min·Individual

Individual: Personal Data Tracker

Students track daily data like sleep hours or screen time for a week using a template. Store in a personal spreadsheet, apply filters, and note one insight. Share anonymized summaries in a class discussion.

Prepare & details

Why is automated testing crucial in software development?

Setup: Tables for small groups, board for evidence

Materials: Phenomenon hook (image, anomaly, demo), Investigation protocol sheet, Data table or observation log, Findings synthesis template

• Apply
• Analyze
• Evaluate
• Self-Management
• Social Awareness

Teaching This Topic

Focus on iterative improvement rather than perfection. When students collect data, emphasize that first attempts will likely have issues, but the goal is to identify and fix them through discussion and adjustment. Research shows students retain data literacy best when they experience the consequences of poor collection or storage in real time.

What to Expect

At the end of these activities, students will confidently collect varied data types, recognize common errors, and organize files for easy retrieval. They will also explain why validation rules matter and how proper storage prevents data loss.

Watch Out for These Misconceptions

Common Misconception

During the Survey Design Challenge, students may assume their questions always produce accurate data.

What to Teach Instead

Circulate and ask groups to swap drafts with another group to identify leading questions or ambiguous wording before finalizing their surveys. Have them revise based on peer feedback.

Common Misconception

During the Sensor Data Logging activity, students might think sensor readings are always precise.

What to Teach Instead

Ask pairs to compare their sensor readings with a classroom standard, like a digital thermometer, and discuss why values differ. Guide them to set validation rules, such as acceptable temperature ranges.

Common Misconception

During the Data Storage Relay, students may believe files are automatically safe once saved.

What to Teach Instead

During the relay, introduce deliberate errors like saving with incorrect extensions or overwriting folders. Afterward, debrief on how these mistakes affect retrieval and the importance of naming conventions and backups.

Assessment Ideas

Exit Ticket

After the Survey Design Challenge, provide the cafeteria food scenario and ask students to write two survey questions and one sensor type. Collect responses to gauge their ability to design unbiased questions and match data types to collection methods.

Quick Check

During the Data Storage Relay, present students with a small, messy dataset (e.g., mixed date formats, missing values). Ask them to identify one validation rule they would apply and explain its purpose before organizing the data.

Discussion Prompt

After the Personal Data Tracker activity, facilitate a class discussion using the prompt: 'What were two challenges you faced collecting self-reported data, and how did organizing it in a spreadsheet help you analyze patterns or errors?'

Extensions & Scaffolding

• Challenge: Ask students to design a follow-up survey question that would help clean or expand their initial dataset.
• Scaffolding: Provide a partially completed spreadsheet template with mismatched column formats for struggling students to standardize.
• Deeper exploration: Have students research how cloud storage or databases handle large datasets differently than spreadsheets.

Key Vocabulary

Survey	A method of gathering information from a sample of individuals to learn about their opinions, behaviors, or characteristics.
Sensor	A device that detects and responds to some type of input from the physical environment, such as light, heat, motion, or pressure, and converts it into an electrical signal.
Spreadsheet	A computer application that displays data in a grid of rows and columns, often used for organizing, analyzing, and storing numerical data.
CSV (Comma Separated Values)	A simple file format used to store tabular data, such as that from a spreadsheet, where values are separated by commas.
Data Validation	The process of ensuring that data is accurate, complete, and conforms to defined rules before it is stored or processed.