Report and presentation

Example 1 Mission setup time before and after the battery status system

Time from arriving on site to ready for take-off (minutes), for ten missions before and ten after (hypothetical data).

import statistics as st

before = [34, 41, 29, 52, 38, 45, 31, 60, 36, 40]
after = [27, 30, 25, 33, 28, 29, 26, 35, 31, 27]
for name, xs in (("before", before), ("after", after)):
    print(f"{name:<6}: mean {st.mean(xs):5.1f} min, median {st.median(xs):4.1f}, max {max(xs)}, sd {st.stdev(xs):4.1f}")
change = (st.mean(after) - st.mean(before)) / st.mean(before) * 100
print(f"mean change {change:+.1f}%, spread (sd) {st.stdev(before):.1f} -> {st.stdev(after):.1f} min")
print(f"missions over 40 min: {sum(x > 40 for x in before)} -> {sum(x > 40 for x in after)}")

before: mean  40.6 min, median 39.0, max 60, sd  9.6
after : mean  29.1 min, median 28.5, max 35, sd  3.2
mean change -28.3%, spread (sd) 9.6 -> 3.2 min
missions over 40 min: 4 -> 0

The mean fell by about 28%, and more importantly the variation fell sharply: missions over 40 minutes disappeared. The report must also state limitations, such as ten missions being few, better weather in the later period, and the team trying harder because it knew it was being measured.

Example 2 Final score from rubrics

Each party scores levels 1–4 against the rubric criteria (hypothetical values).

rubrics = {   # assessor: (weight, {criterion: level 1–4})
    "workplace mentor": (0.40, {"technical work": 4, "safety & quality": 3, "teamwork": 4, "punctuality": 3}),
    "supervising lecturer": (0.30, {"learning plan": 3, "reflection": 3, "site visit discussion": 4}),
    "report & presentation": (0.30, {"structure": 3, "evidence & data": 4, "confidentiality": 4, "delivery": 3}),
}
MAX_LEVEL = 4
total = 0.0
for who, (w, scores) in rubrics.items():
    pct = sum(scores.values()) / (MAX_LEVEL * len(scores)) * 100
    total += w * pct
    low = [k for k, v in scores.items() if v < MAX_LEVEL]
    print(f"{who:<22} {pct:5.1f}% x {w:.2f}  to improve: {', '.join(low)}")
print(f"final score {total:.1f}%")

workplace mentor        87.5% x 0.40  to improve: safety & quality, punctuality
supervising lecturer    83.3% x 0.30  to improve: learning plan, reflection
report & presentation   87.5% x 0.30  to improve: structure, delivery
final score 86.2%

The final score gives the overall level, but the more useful part is the criteria not yet at full marks, which show what to develop before real work. These results also map onto the four learning-outcome domains, for example safety to ethics and punctuality to character.