📚 General Description of the Computing Project in Physical Education | 体育计算项目概述
In Cambridge Physical Education, a computing project is a structured piece of independent investigation. It asks learners to use digital tools and numerical data to answer a sport-related question, rather than simply describing training or performance in words.
在剑桥体育课程中，计算项目是一项结构化的独立探究任务。它要求学生使用数字工具和量化数据来回答与运动相关的问题，而不仅仅是用文字描述训练或运动表现。
This type of project brings together sport science, data handling and critical thinking. It is increasingly important because coaches, analysts and athletes now rely on evidence from wearable devices, video analysis and performance databases.
这类项目将运动科学、数据处理和批判性思维结合在一起。随着教练、分析师和运动员越来越依赖可穿戴设备、视频分析和运动表现数据库提供的证据，这类项目也变得越来越重要。
This article explains what the computing project involves, how it is structured, which variables and tools are used, how data should be processed and presented, and what examiners look for when marking the final report.
本文将说明计算项目包含哪些内容、如何搭建结构、会用到哪些变量与工具、数据应如何处理与呈现，以及考官在评阅最终报告时关注的重点。
1. What is a Computing Project in Sport? | 体育中的计算项目是什么
In a sport and physical education context, the computing project is not a pure programming exercise. It is a data-driven investigation of human performance, training load or movement technique. The learner chooses a sport-related question, gathers numerical evidence and presents conclusions based on analysis.
在体育与运动教育背景下，计算项目不是纯粹的编程练习，而是对运动表现、训练负荷或动作技术进行数据驱动的探究。学生选择一个与运动相关的问题，收集量化证据，并根据分析得出结论。
Typical projects examine measurable variables such as sprint time, heart rate, jump height, shot accuracy or distance covered. Qualitative factors like motivation or perceived effort may be recorded, but they are usually converted into numerical scales for analysis.
典型项目会考察可测量的变量，例如冲刺时间、心率、纵跳高度、投篮命中率或跑动距离。动机、主观用力感等定性因素也可以记录，但通常会被转换成数值量表以便分析。
The essential feature is the chain of reasoning: a clear question leads to a measurable variable, the variable produces data, the data produces evidence, and the evidence supports or rejects a hypothesis. Every stage must be transparent enough for another student to repeat.
其核心特征是一条完整的推理链：明确的问题引出可测量的变量，变量产生数据，数据产生证据，证据支持或否定假设。每个环节都必须足够透明，使其他学生能够重复该过程。
A simple example makes the logic clear. A learner asks whether a six-week plyometric programme improves vertical jump. The independent variable is the training programme, the dependent variable is jump height in centimetres, and the comparison is between pre-test and post-test scores for the same group of athletes.
一个简单的例子可以说明这一逻辑。学生提出问题：为期六周的增强式训练是否能提高纵跳高度？自变量是训练方案，因变量是以厘米为单位的纵跳高度，比较对象是同一组运动员前测与后测的成绩。
2. Why Data Matters in Modern Sport and PE | 为什么数据在现代体育中如此重要
Elite sport has become an evidence-based industry. Coaches use load monitoring to reduce injury risk, analysts use video and tracking data to prepare tactics, and athletes use feedback from sensors to refine technique. A PE student who can handle performance data is therefore practising the same skills used in professional environments.
精英体育已成为以证据为基础的行业。教练通过负荷监控降低受伤风险，分析师利用视频与追踪数据制定战术，运动员借助传感器反馈改进技术。因此，能够处理运动表现数据的体育课程学生，实际上在练习与职业环境相同的技能。
Data also improves the quality of personal training decisions. Instead of saying "I feel faster this term", a learner can say "my 20 m sprint time fell from 3.28 s to 3.14 s, a 4.3 percent improvement". Quantified statements are more convincing, more comparable and easier to act upon.
数据还能提升个人训练决策的质量。与其说"我觉得这学期变快了"，学生可以表述为"我的 20 米冲刺成绩从 3.28 秒降至 3.14 秒，提高了 4.3%"。量化陈述更有说服力、更易比较，也更容易转化为行动。
Finally, working with numbers develops critical thinking. Learners must ask where data came from, how accurate the device is, whether the sample is large enough and whether an observed change is real or simply normal day-to-day variation.
最后，处理数字能培养批判性思维。学生必须追问：数据从哪里来？设备精度如何？样本量是否足够？观察到的变化是真实提升，还是日常波动的结果？
3. Where It Fits in the Cambridge PE Course | 在剑桥体育课程中的位置
The computing project normally sits within the coursework or independent study element of the qualification. It is internally supervised and externally moderated, which means the marking follows published criteria rather than personal preference.
计算项目通常属于课程作业或独立研究部分。它由校内教师指导，并由外部机构审核，因此评分依据公开的标准，而不是个人偏好。
Because it is an independent investigation, the project rewards planning and organisation as much as sporting knowledge. A learner who collects clean, well-documented data and analyses it honestly will usually outperform a learner who attempts an ambitious but poorly controlled study.
由于它是独立探究，项目对计划性和组织性的奖励不亚于对体育知识的奖励。一个采集到干净、记录完整的数据并诚实分析的学生，通常比一个选题宏大但控制糟糕的学生得分更高。
The project draws on several areas of the specification at once:
该项目会同时涉及课程大纲中的多个领域：
- Anatomy and physiology - heart rate, muscle function and energy systems provide the biological basis for the variables chosen.
解剖与生理学——心率、肌肉功能与能量系统为所选变量提供生物学基础。 - Biomechanics - joint angles, force and velocity explain movement efficiency.
生物力学——关节角度、力与速度解释动作效率。 - Exercise physiology and training - load, adaptation and periodisation shape the intervention.
运动生理与训练——负荷、适应与周期化决定干预设计。 - Skill acquisition - accuracy, consistency and practice design link to learning theory.
技能习得——准确性、稳定性与练习设计联系到学习理论。 - Sports psychology - motivation, arousal and perceived effort are converted into numerical scales.
运动心理学——动机、唤醒水平与主观用力感被转换为数值量表。
4. Choosing a Sport-Related Research Question | 选择与运动相关的研究问题
The quality of the whole project depends on the question. A good question is narrow enough to be completed with the time, equipment and participants available, but wide enough to be worth answering.
整个项目的质量取决于研究问题。好的问题要足够聚焦，能在现有时间、设备和受试者条件下完成；同时又要足够有价值，值得回答。
A useful test is to check that the question contains a measurable variable and a comparison. "Does sleep affect performance?" is vague. "Does reducing sleep to six hours change 30 m sprint time in academy rugby players?" is measurable because it names the population, the intervention and the unit of measurement.
一个实用的检验方法是确认问题中包含可测量的变量与比较对象。"睡眠会影响运动表现吗？"过于笼统。"将睡眠减少到六小时后，青训橄榄球运动员的 30 米冲刺时间是否改变？"则是可测量的，因为它明确了人群、干预措施和测量单位。
Table 1 shows how weak questions can be rewritten.
表 1 展示了薄弱问题如何被改写。
| Weak question | Problem | Improved question |
| Is football the best sport? | Not measurable, opinion based | How does 6 weeks of small-sided games affect repeated sprint ability in under-16 footballers? |
| Does training make you fitter? | Too vague, no defined variable | What is the effect of 8 weeks of interval training on resting heart rate in sedentary students? |
| Is video analysis useful? | No numerical outcome | Does frame-by-frame video feedback improve free-throw accuracy over 5 sessions? |
| Why do injuries happen? | Far too broad for a school project | Is there a relationship between weekly training load and reported knee soreness in school netball players? |
Once the question is fixed, write a hypothesis and a null hypothesis. The alternative hypothesis predicts a difference or a relationship; the null hypothesis states that no difference exists, and statistical testing is used to decide between them.
确定问题后，要写出研究假设与虚无假设。备择假设预测存在差异或关系；虚无假设则声明不存在差异，并通过统计检验在两者之间做出判断。
5. Key Variables and Performance Indicators | 关键变量与表现指标
Every project must identify variables clearly. The independent variable is what the learner deliberately changes. The dependent variable is what is measured as a result. Controlled variables are kept constant so that they do not distort the outcome.
每个项目都必须清晰界定变量。自变量是学生有意改变的因素；因变量是作为结果被测量的指标；控制变量则被保持不变，以免干扰结果。
Extraneous variables cannot always be eliminated - sleep, hydration, motivation and time of day all affect performance. The honest approach is to record them, acknowledge them in the evaluation and, where possible, standardise them.
额外变量并非总能消除——睡眠、补水、动机和测试时间都会影响运动表现。诚实的做法是记录它们、在评估部分说明它们，并尽可能使其标准化。
| Variable type | Example | Unit or scale |
| Independent | Plyometric programme versus normal warm-up | Two groups (nominal) |
| Dependent | Countermovement jump height | cm |
| Controlled | Warm-up duration, footwear, testing time | min, h, category |
| Extraneous | Sleep, hydration, perceived motivation | h, ml, Likert 1-5 |
| Qualitative converted | Rating of perceived exertion | Borg scale 6-20 or RPE 1-10 |
Training load illustrates how a subjective feeling becomes a number. Session load is often calculated as:
训练负荷说明了主观感受如何转化为数字。单次课负荷通常按下式计算：
Session Load (AU) = RPE (1-10) × Duration (min)
If an athlete rates a 60-minute session as 7 out of 10, the session load is 420 arbitrary units. Weekly totals can then be compared, and a sudden rise above roughly 1.5 times the previous week's load can be flagged as a risk factor.
如果运动员把 60 分钟的训练课评为 10 分制中的 7 分，那么该次课负荷为 420 任意单位。之后可以比较每周总负荷，若某周负荷突然超过前一周的约 1.5 倍，就可被标记为风险因素。
6. Data Collection Tools and Technologies | 数据采集工具与技术
Digital tools make the project possible, but each tool has limits. A learner must know what the device actually measures, how often it samples and how accurate it is.
数字工具让项目成为可能，但每种工具都有局限。学生必须了解设备的实际测量原理、采样频率以及精度。
| Tool | Measures | Typical unit | Notes |
| Timing gates | Sprint split times | s, m·s⁻¹ | Resolution about 0.01 s |
| GPS vest | Distance, speed zones | m, m·s⁻¹ | Sampling 5-10 Hz; indoor accuracy lower |
| Heart rate monitor | Exercise intensity | beats·min⁻¹, %HRmax | Chest strap more reliable than wrist optical |
| Video analysis | Joint angles, technique | ° | 60-240 fps; camera position must be fixed |
| Force plate | Ground reaction force | N, N·kg⁻¹ | High sampling rate, usually 1000 Hz |
| Stopwatch and app | Simple time or count | s, repetitions | Human reaction error about ±0.2 s |
Good practice is to standardise the protocol before data collection begins. Write down the warm-up, the number of trials, the rest interval and the exact instructions given to participants, then keep them identical for every session.
良好的做法是在采集数据前先标准化流程。写明热身内容、试做次数、休息间隔以及对受试者的具体指令，并在每一次测试中保持一致。
A pilot test with two or three participants identifies practical problems early: equipment that will not connect, a test that takes too long, or a measurement that is too variable to be useful.
先用两三名受试者进行预试，可以尽早发现实际问题：设备无法连接、测试耗时过长，或某项测量波动太大而无法使用。
7. Data Processing and Statistical Analysis | 数据处理与统计分析
Raw data alone prove nothing. Processing begins with cleaning - removing incomplete records, checking for transcription errors and deciding how to treat obvious outliers. Any excluded value must be explained, not silently deleted.
原始数据本身不能证明任何结论。处理从清洗开始——删除不完整记录、检查录入错误，并决定如何处理明显异常值。任何被排除的数值都必须说明理由，而不能悄悄删除。
Descriptive statistics summarise the sample. The mean gives the average, the standard deviation shows how spread out the scores are, and the coefficient of variation expresses that spread as a percentage so that variables with different units can be compared.
描述性统计用于概括样本。平均值给出集中趋势，标准差反映数据的离散程度，变异系数则把离散程度表示为百分比，使单位不同的变量之间可以比较。
Mean: x̄ = Σx ÷ n
Standard deviation: s = √(Σ(x − x̄)² ÷ (n − 1))
Coefficient of variation: CV% = (s ÷ x̄) × 100
Percentage change: Δ% = ((final − initial) ÷ initial) × 100
Inferential statistics test whether a result is likely to be real. A paired t-test compares two sets of scores from the same group, an unpaired t-test compares two different groups, and the Pearson correlation coefficient r describes the strength of a linear relationship.
推断统计用于检验结果是否可能真实存在。配对 t 检验比较同一组的前后两次成绩，非配对 t 检验比较两个不同组别，皮尔逊相关系数 r 则描述线性关系的强度。
Paired t: t = (x̄d − 0) ÷ (sd ÷ √n)
Correlation: r = Σ((x − x̄)(y − ȳ)) ÷ √(Σ(x − x̄)² × Σ(y − ȳ)²)
Interpret r cautiously. Values near +1 or −1 indicate a strong relationship, values near 0 indicate almost none. A probability value below 0.05 is conventionally described as statistically significant, but significance is not the same as practical importance - a 0.02 s improvement may be statistically detectable yet meaningless to a coach.
解读 r 时要谨慎。接近 +1 或 −1 的数值表示关系强，接近 0 表示几乎无关。概率值低于 0.05 通常被视为具有统计显著性，但显著并不等于实践意义——0.02 秒的提升可能在统计上可检出，对教练却毫无意义。
8. Presenting Results: Tables, Graphs and Visualisation | 呈现结果：表格、图表与可视化
Presentation is where many projects gain or lose marks. Every table needs a title, labelled columns and units in the heading rather than repeated in each cell.
呈现方式是许多项目得分或失分的关键。每张表格都需要标题、标注清楚的列名，并把单位写在表头而不是在每个单元格里重复。
The choice of graph must match the data type:
图表的选择必须与数据类型匹配：
- Bar chart - compares mean values between discrete groups, such as two training groups.
柱状图——比较离散组别之间的平均值，例如两个训练组。 - Line graph - shows change over time, such as weekly sprint times across eight weeks.
折线图——显示随时间的变化，例如八周内每周的冲刺成绩。 - Scatter graph - shows the relationship between two continuous variables, such as training load against soreness score.
散点图——显示两个连续变量之间的关系，例如训练负荷与酸痛评分。 - Box plot - summarises median, quartiles and outliers, useful for comparing consistency between players.
箱线图——概括中位数、四分位数与异常值，适合比较运动员之间的稳定性。
Error bars are strongly recommended. They show the spread or uncertainty of each mean and prevent readers from over-interpreting small differences between two bars.
强烈建议添加误差线。它显示每个平均值的离散程度或不确定性，避免读者过度解读两根柱子之间的微小差异。
Every figure should also carry a short caption that states what is shown and what the reader should notice. A graph without a caption forces the reader to guess, and guessing is exactly what a data project should avoid.
每张图还应配简短图注，说明展示的内容以及读者应注意什么。没有图注的图表会迫使读者猜测，而猜测恰恰是数据项目应当避免的。
9. Interpreting Findings and Linking to Sport Science Theory | 解读结果并与运动科学理论联系
Analysis describes what happened; interpretation explains why. This is where the project stops being a spreadsheet exercise and becomes sport science.
分析描述发生了什么，解读则解释为什么发生。正是在这里，项目从表格练习变成真正的运动科学。
A jump improvement might be explained through neural adaptation, increased motor unit recruitment and better stretch-shortening cycle efficiency in the first weeks, with structural changes contributing later. A drop in resting heart rate might reflect increased stroke volume and improved cardiac output.
纵跳成绩的提升可以用神经适应来解释：最初几周运动单位募集增加、拉长—缩短周期效率提高；结构性的改变则在后期发挥作用。安静心率的下降可能反映每搏输出量增加与心输出量改善。
A change in free-throw accuracy after video feedback links to observational learning theory and the
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