In the audience, a student rereads the statement, then sighs softly. He knows Python, but the page remains blank. The exam requires an algorithm to be written by hand, without a computer. He suddenly realises that the keyboard is not the problem.

Course leaders in several Belgian faculties have made this clear. Many assessments are reverting to paper, to check actual understanding. This trend also follows the massive use of AI tools by students. When the publisher disappears, only the logic remains.

This reality changes the preparation in Python in other computer languages and useful support. Passing the programming exams depends less on syntax. It depends mainly on modelling, limiting cases and simple proofs. And that's often when things start to click.

Why the BA1 filters so much into IT

Recent figures confirm a difficult transition to university. In 2024-2025, a large proportion of BA1 students will not validate their credits. The overall validation rate is around 55% in the Wallonia-Brussels Federation. This is not a detail, it is an academic alarm bell.

This pressure is increasing still further with the rules on financeability. Under the Décret Glatigny, missing the first few credits costs more. Not reaching sixty credits on time can lead to non-financiability. In IT, the prerequisite courses become non-negotiable.

Successful programming exams start before the code

Many students think they don't know enough code. In reality, what they really struggle with is representing the problem. They read, they code, and then they hope it will pass. But universities expect clear, stable algorithmic thinking first.

A simple example will help you to understand this frequent blockage. “Counting duplicates” seems easy on the keyboard at first. But on paper, you have to define the input, the output and the invariant. Without this definition, even a correct loop can become false.

Passing programming exams therefore requires a method. You start by reformulating the statement in simple words. Then you choose a plausible, but not perfect, data structure. Finally, you write short steps, then justify each choice.

Paper-based exams: how to prepare effectively

In several universities, written assessment is making a strong comeback. The aim is to assess skills without the assistance of an IDE. In a context where many students use AI to generate code, this becomes a test of understanding. The hand quickly reveals the fuzzy areas.

So you need to practise as you would for a logic dictation. You take a statement, write the algorithm, then simulate by hand. You follow the variables line by line, without “correcting by feel”. This work may seem slow, but it stabilises your thinking.

One question can guide the rereading without panicking: “What happens in a borderline case? This simple reflex prevents many mistakes. It also prepares you for automated assessments. And it directly reinforces the success of programming exams.

Hidden tests and platforms: the “it works for me” trap”

In several courses, correction is done via automated platforms. The code must pass both visible and hidden tests. An “almost good” result may receive a zero mark if a case fails. This rule imposes a rigour that will surprise beginners.

The right strategy is not to try a thousand variants at random. You need to learn structured debugging, like an investigation. You isolate a hypothesis, create a mini-test, then observe. Then you decide, correct and calmly retest.

A small example illustrates the logic expected with edges. An average function fails if the list is empty. An array search fails on an out-of-bounds index. Hidden tests love these situations, even when the statement barely suggests them.

Mini scenario: a classic edge case

Input: n = 0
Expected: manage the case without dividing by zero
Frequent error: total / n without checking

This kind of oversight is very costly on a platform. On a paper copy, it also costs points. In both cases, success in programming exams depends on a reflex: checking the extremes. This reflex needs to be practised, like an athletic automatism.

Java, Python, and the basics are everywhere

In BA1, Python is still very much in evidence for getting started and manipulating quickly. Java often dominates object-oriented courses and structured projects. This creates recurring expectations: types, loops, collections, classes and methods. Students save time if they master these building blocks early on.

But beware of the dangerous illusion of “I know how to write a class”. Understanding the object means understanding responsibilities and state. Understanding a list means understanding the complexity of operations. This understanding avoids slow or fragile solutions, especially under test.

Discrete mathematics: the foundation often underestimated

Many people think that computer science is all about being at ease on the computer. Yet the difficulties often lie in discrete logic: sets, propositions and graphs. Academic analyses strongly link success in computing to these skills. Without them, the algorithm looks like a copied recipe.

A student who understands implications and quantifiers reads a statement better. They can distinguish “for all” from “there is” without making mistakes. He formalises a stopping condition without ambiguity. This precision is immediately reflected in the points gained.

We can relate this to programming in a very concrete way. A badly placed logical “and” changes an entire filter. An order condition on a graph changes an entire path. Every little logical error becomes a functional error, then a lost note.

Simple work plan to aim for success in programming exams

To give a clear structure, you need a short routine. It should be repeated during the week, not just in blockades. It should also mix paper and machine, to cover both formats. And it must remain realistic, even with other heavy courses.

• Write two algorithms a week, entirely by hand, then simulate.
• Create a list of borderline cases and apply it to each exercise.
• Repeat a test failure, isolate the cause and write a mini-test.
• Link each exercise to a discrete concept: logic, sets, graphs.

This routine has a very specific aim: to make understanding visible. It reduces dependence on the EID and automatic suggestions. It trains thinking before typing. And it directly boosts success in programming exams.

If a student can't explain their algorithm in pen, they haven't really mastered programming yet. Contact us without delay, we'll help you.