📚 Computing Project in Physical Education | 体育中的计算项目
In Cambridge Physical Education, a computing project gives you the chance to apply digital tools to collect, analyse and present sports performance data. Whether you are tracking heart rate responses, measuring sprint times or comparing technique scores, a well-planned computing project can strengthen your understanding of both sport science and data handling.
在剑桥体育课程中，计算项目让你有机会运用数字工具收集、分析和展示运动表现数据。无论你是在追踪心率反应、测量短跑时间还是比较技术评分，一个精心设计的计算项目都能加深你对运动科学和数据处理的理解。
This guide explains how to plan, carry out and report a computing project in Physical Education, using spreadsheets and simple programming where appropriate. It follows the Cambridge approach to independent investigation and is suitable for both IGCSE and AS/A Level students.
本指南将解释如何在体育学科中规划、实施和报告一个计算项目，在适当的地方使用电子表格和简单编程。它遵循剑桥独立调查的学习方法，适用于IGCSE和AS/A Level学生。
1. What is a Computing Project in PE? | 什么是体育中的计算项目？
In a Physical Education context, a computing project is not a pure programming exercise. Instead, you use computing tools to answer a sport-related question. The focus is on data: collecting it, cleaning it, analysing it and presenting conclusions.
在体育学科背景下，计算项目并不是纯粹的编程练习。相反，你使用计算工具来回答一个与运动相关的问题。重点在于数据：收集、清理、分析并展示结论。
For example, you might compare the average sprint times of two groups of athletes before and after a training intervention. The computing part involves creating a dataset, calculating descriptive statistics and building charts that support your findings.
例如，你可能会比较两组运动员在训练干预前后的平均短跑时间。计算部分包括创建数据集、计算描述性统计量并构建支持研究结果的图表。
2. Choosing a Research Question | 选择研究问题
A strong research question is specific, measurable and realistic within your school environment. Avoid questions that are too broad, such as “What makes a good athlete?” Instead, focus on one variable that can be recorded reliably.
一个好的研究问题是具体、可测量且在学校环境中现实可行的。避免过于宽泛的问题，例如 “什么造就了一名优秀运动员？”。相反，关注一个能够可靠记录的变量。
Examples of suitable questions include:
合适的问题示例包括：
- Does a 4-week core stability programme reduce 20-metre sprint time? / 为期4周的核心稳定性训练能否缩短20米短跑时间？
- How does heart rate recovery after a step test differ between trained and untrained students? / 台阶测试后，受过训练和未受过训练学生的心率恢复有何不同？
- Is there a correlation between hand grip strength and shot put distance in Year 12 athletes? / 12年级运动员的握力与铅球投掷距离之间是否存在相关性？
Before finalising your question, check that you can access enough participants, equipment and time to collect valid data. A computing project is only as good as the data behind it.
在确定问题之前，请确认你能够获得足够的参与者、设备和时间来收集有效数据。一个计算项目的好坏取决于其背后的数据。
3. Data Collection Methods | 数据收集方法
In PE investigations, data usually comes from one of three sources: direct measurement, questionnaires or existing records. Direct measurement is often the most reliable because you control the conditions.
在体育调查中，数据通常来自三个来源之一：直接测量、问卷或现有记录。直接测量通常最可靠，因为你可以控制条件。
When collecting quantitative data, use standardised protocols. For example, always measure standing long jump in centimetres from the same starting line, and record heart rate after exactly 60 seconds of recovery. Keep a paper or digital logbook during every session.
收集定量数据时，请使用标准化流程。例如，始终从同一起跳线以厘米为单位测量立定跳远，并在恢复恰好60秒后记录心率。每次测试时都要保存纸质或数字记录本。
Table 1 shows common variables and suitable tools in a PE computing project.
表1列出了体育计算项目中的常见变量和合适工具。
| Variable / 变量 | Tool / 工具 | Unit / 单位 |
|---|---|---|
| Sprint time / 短跑时间 | Timing gates or stopwatch / 计时门或秒表 | seconds (s) |
| Heart rate / 心率 | Heart rate monitor / 心率监测器 | beats per minute (bpm) |
| Vertical jump height / 纵跳高度 | Jump mat or Vertec / 跳跃垫或Vertec | centimetres (cm) |
| Agility time / 灵敏素质时间 | Illinois agility test / 伊利诺伊灵敏测试 | seconds (s) |
Always record the date, participant ID, condition and any confounding factors such as fatigue or weather. These details will help you explain outliers later.
务必记录日期、参与者编号、条件以及任何混杂因素（如疲劳或天气）。这些细节将有助于你之后解释异常值。
4. Using Spreadsheets for Data Entry | 使用电子表格录入数据
A spreadsheet is the most accessible tool for storing PE project data. Use separate columns for each variable and one row per participant per trial. This structure is called tidy data and makes later analysis much easier.
电子表格是存储体育项目数据最方便的工具。每个变量使用单独的列，每个参与者每次试验占一行。这种结构称为整洁数据，会让后续分析容易得多。
For example, your columns might be: Participant_ID, Group, Pre_Test_Sprint_s, Post_Test_Sprint_s, Heart_Rate_bpm. Avoid merging cells or placing multiple observations in one cell.
例如，你的列可以是：参与者编号、组别、前测短跑秒数、后测短跑秒数、心率。避免合并单元格或将多个观测值放在一个单元格中。
Use data validation tools to restrict input ranges. For heart rate, set a rule that only allows numbers between 30 and 220 bpm. This reduces typing errors before they enter your analysis.
使用数据验证工具来限制输入范围。对于心率，设置规则只允许输入30到220 bpm之间的数字。这样可以减少在进入分析之前发生的输入错误。
5. Basic Statistics for Performance Analysis | 运动表现分析的基础统计学
Descriptive statistics summarise your data so that patterns become visible. The three most useful measures are the mean, median and standard deviation.
描述性统计可以总结数据，使模式变得可见。最有用的三个指标是平均值、中位数和标准差。
The mean is the arithmetic average and is calculated by adding all values and dividing by the number of observations.
平均值是算术平均数，计算方法是将所有数值相加后除以观测值个数。
Mean = Σx ÷ n
where Σ
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