Coding classes in Saxony

Sources: Sächsisches Staatsministerium für Kultus, VwV Abiturprüfung 2027 of 6 May 2025 (as published on revosax.sachsen.de, version of 1 August 2025), read 3 November 2026; Informatik-Monitor 2026/27, Sachsen page, which adds SQL documentation to the permitted aids in part B. The same rule sets the maths Leistungskurs at 300 minutes, with part A within 110 minutes, and the maths Grundkurs at 255 minutes. We could not fetch the Saxon 2027 exam dates, so we print none; your school has them.

import csv
from collections import defaultdict

counts = defaultdict(lambda: defaultdict(int))
with open("fahrzeugbestand_kraftstoff.csv", encoding="utf-8") as f:
    for row in csv.DictReader(f, delimiter=";"):
        counts[int(row["Jahr"])][row["Kraftstoff_Energiequelle"]] += int(row["Anzahl"])

first, last = counts[2016]["Elektro"], counts[2025]["Elektro"]
print("Electric 2016:", first, "2025:", last)
print("Growth factor:", round(last / first, 1))
print("Percent change:", round(100 * (last - first) / first), "%")

From 251 to 6,988 is a growth factor of 27.8: almost 28 times as many. As a percent change that is 2,684 %, a number so large it is hard to picture. Both are correct. Neither says how common electric vehicles are.

def share(year):
    return 100 * counts[year]["Elektro"] / sum(counts[year].values())

print("Share 2016:", round(share(2016), 2), "%  share 2025:", round(share(2025), 2), "%")
print("Change:", round(share(2025) - share(2016), 2), "percentage points")

In 2025 electric vehicles were 2.74 % of all motor vehicles in the file, up from 0.1 % in 2016. The share rose by 2.64 percentage points. Writing "rose by 2.64 %" would be wrong: a rise of 2.64 % from 0.1 % would barely move it. Percentage points measure the difference between two percentages; percent measures change relative to the start.

for year in range(2017, 2026):
    before, now = counts[year - 1]["Elektro"], counts[year]["Elektro"]
    print(year, "added", now - before, "growth", round(100 * (now - before) / before, 1), "%")

Data: Landeshauptstadt Dresden, Fahrzeugbestand 2016ff.: Kraftfahrzeuge nach Kraftstoff-Energiequelle ab Stadtbezirk, Datenlizenz Deutschland Namensnennung 2.0, downloaded 3 November 2026. All factors, percentages and differences are our own calculations from the file; it gives no reasons for the changes, and neither do we.

Version 3 adds a fourth truth. The yearly growth rate fell from 81.9 % in 2020 to 25.0 % in 2025, yet more vehicles were added in 2025 (1,399) than in 2020 (582). A falling percentage does not mean slower growth in numbers; it means the base got bigger. That is the habit to take away: before you compare two numbers, say whether they are counts, percents or percentage points.

1. Ages 10 to 12, by hand. A class had 2 pupils with a library card out of 20; now 6 out of 20 do. Work out the growth factor, the percent change and the change in percentage points.
2. Ages 12 to 14, Python. Run Version 1, then change it to the Hybrid fuel type and compare the factor with the electric one.
3. Ages 14 to 16, Python. Write Version 2 for each district and find where the electric share rose the most in percentage points.
4. Going further. Fit a straight line and an exponential curve to the electric counts. Which describes 2021 to 2025 better, and how would you test that fairly?