Stride: With a little help from my editor

In the third part of our series introducing Stride and frame-based editing, we look at the various ways in which the editor can provide you with help in writing your program. If you consider using Stride, it will come handy to know these sources of information.

Stride is implemented in the Greenfoot environment, available from here.

What’s in a symbol? Assignment revisited

I was at a Dagstuhl seminar recently, about programming education, and one of the things we discussed were common misconceptions of novice programmers. One of these, reported by several seasoned programming teachers, was the difficulty in interpreting the assignment symbol in C-style languages:

number = 42

There are several things that can be misinterpreted here. Firstly, the equality operator is non-directional. Some students have problems remembering which way it operates. Consider this statement:

a = b

Which way does it assign? To all of us who have programmed for some time this may seem a silly question – we get used to the right-to-left semantics so thoroughly that we can hardly imagine it to be different. But for beginners? Not so clear. (And, of course, the direction is arbitrary; language designers could just as well have decided the other way.)

The second possible misconception is related to the previously learned meaning of the equals symbol: to express equality. This has been used in maths for centuries, and taught to most pupils before they ever encounter programming, and is quite different from assignment. Consider this code fragment:

a = 0;
b = a;
a = 7;

What, now, is the value of b? For learners holding the equality misconception, it will be 7. This is not entirely unreasonable; it is indeed an internally consistent mental model. The misconception is that variables, once expressed as equal, remain linked. So the equals symbol is interpreted as equality (“b is the same as a“), which might then remain true for the future.

Many people have commented for a long time that the C-style equality-as-assignment is not ideal. Many languages do better. Algol and Pascal, for example, already used a different symbol in the 1960s, quite explicitly to express the directionality more clearly, and to distinguish from equality:

number := 42

I have always like this much better than the single equals and have always been mildly annoyed by the C-style syntax.

So when we designed our language Stride recently, the question surfaced again. What should we choose?

We had two competing goals: On the one hand, we wanted the syntax to be clear and expressive, but on the other hand we want Stride to be a pathway into Java, so there is also a strong incentive to be consistent with Java syntax. (Java, of course, uses the C-style syntax, so these two goals are in conflict.)

At first, we prioritised the Java-compatibility argument and chose the equality symbol. The first release of Stride had this as the assignment operator. But then the Dagstuhl discussion happened, and I started to think again.

In Stride we have an advantage: Because the representation of the program is not directly typed in, but produced by the system in reaction to command keys, we are not restricted to characters that can easily be typed on a standard keyboard. So initially I considered going back to a syntax that Smalltalk used (in early versions, before it fell back onto the := variant), a left arrow:



I liked it for its clarity of expression, but it has one disadvantage in a teaching language that is intended to lead to Java and similar languages: it does not directly prepare learners for the equal symbol so widely used for assignment in other languages. After we discussed this in one of our team meetings, Neil Brown, one of our team members, came up with what I think is a brilliant compromise:


This is what assignment now looks like in Stride. The double arrow still has a visual link to an equals sign, but is clearly distinct from it; it is also clearly directional. We still use the equals key as a command key to enter an assignment, further reinforcing the link for the future.

Here’s what our assignment looks like in context:



I am very happy with it. I think it reads well and is the closest we can get to reaching two competing goals.

Stride: Creating a game in 7 minutes

This video is mostly for those who want to get a first impression of Greenfoot. We write a simple game in seven minutes (realtime; no cuts!), using Stride. For those who already know Greenfoot with Java: You will see that writing in Stride is not very different to writing in Java. For those new to Greenfoot: This is a quick introduction to Greenfoot and Stride at the same time.

As always, Greenfoot is available for free download.

Stride – A new programming language for beginners

At the Greenfoot headquarters, we – that is: Neil Brown, Amjad Altadmri and myself – have recently worked on creating a new language within the Greenfoot environment: Stride.

The interesting thing about Stride is not so much the language design itself, but its interaction design: editing programs involves different interactions than existing editors.

The goal is that Stride sits halfway between block-based systems (such as Scratch, AppInventor, PencilCode, Alice, etc.) and text-based editors (such as Java or Python), maintaining advantages of both.

I will start a series of posts (text and/or video) here over the next few weeks trying to tell you what Stride is and why you should care. In the meantime, you can get Greenfoot, install it, and have Stride ready to go when the introduction here starts. Stride is built into Greenfoot from version 3 onwards.

Check back soon for the first overview video.

Java 7 for introductory programming — does it make a difference?

The recently released BlueJ 3.0.5 brought full support for Java 7. While it is always good to be up-to-date with the latest versions — what does that actually mean for introductory programming teaching?

Java 7 brought a considerable list of new features to the Java system. Most of them deal with fairly advanced or specialised topics, and will have no influence on introductory teaching with Java. Some, however, will become visible even close to the beginning of programming. Here, I give a short overview of what’s new, and what you — as a teacher of introductory programming — should be aware of.

Java 7 – What’s new

Most of the new features in Java 7 are under the hood or in specialised libraries. These are very unlikely to affect an introductory programming course.

Improvements to the internals of the JDK include changes to the VM to support dynamically typed languages and improvements to the class loader architecture. On the library side, there are new frameworks for concurrency, cryptography and new versions of JDBC and Unicode. All these will not be relevant for most introductory courses.

The two areas that are relevant are “Project Coin”, the code name given to a project defining several small language enhancements, and NIO.2, a new library for I/O. We’ll discuss these in more detail in a moment.

Equally important, what’s not included in this release includes support for closures (known as “Project Lambda”). This would have been the most significant change to Java since the introduction of generics in Java 5 or, arguably, since the original definition of the Java language in 1995. Project Lambda has, however, been deferred to JDK 8 in what’s known as Plan B. JDK 8 is currently scheduled for release in late 2012.

So, which of the new Java 7 features are actually relevant for our intro programming course?


The I/O classes in Java have been improved several times over the years. The release of JDK 1.4 in 2002 brought some significant improvements in the somewhat short-sightedly named NIO library. The naming is rather less than ideal, since it stands for “New I/O”, and the passing of time dictates that everything new will eventually get old.

Which brings us to Java 7 and the New New I/O. So what will this be called now — NNIO? Well, the designers settled on NIO.2.

NIO.2 brings a number of new classes and interface, some with very useful methods. If you are doing any programming that accesses the file system, it’s worth familiarising yourself with these. Particularly interesting are the Files and Paths classes, and the Path interface, which you can find in the java.nio.file package.

All other relevant new features are part of Project Coin. They are diamond notation, strings-in-switch and improved exception handling.

Project Coin

Diamond notation

Generic type details on the right hand side of an assignment can now be inferred. For example, where in Java 6 we had to write

   HashSet<String, Monster> monsters = new HashSet<String, Monster>();

to declare a HashSet variable and initialise it with a fresh HashSet object, in Java 7 we can write

   HashSet<String, Monster> monsters = new HashSet<>();

In other words: We do not have to repeat the generic types of the HashSet on the right hand side, and can instead just write <>. (This syntax is the reason that this construct is known as the “diamond notation”). The effect of this is exactly as the Java 6 version above. It is a purely syntactic shortcut. The compiler will fill in the generic types by copying them from the variable declaration on the left, and all behaves just as before. Of course, the old notation is still allowed.

Strings in switch statements

The variable used in switch statements can now be of type String. For example

   switch (command) {
       case "go":
       case "help":

Before Java 7, strings could not be used in switch statements.

Improved exception handling 1: multi-catch

It is now possible to catch multiple exceptions in a single exception handler. For example:

   try {
      file = new File("readme.txt");
   catch (FileNotFoundException | UnsupportedEncodingException ex) {

As you can see in this example, the catch clause can list multiple types of exception in its header, separated with an OR symbol, to catch any of these types of exception.

Improved exception handling 2: try-with-resources

The second new feature relating to exceptions is called try-with-resources. It solves a hard problem: It was previously surprisingly hard to correctly guarantee that resources (such as files or network connections) were correctly closed in the case of an exception.

The new construct solves this. Look at this example:

   try (FileWriter writer = new FileWriter(filename)) {
   catch (IOException e) {

Before Java 7, the standard way to close a resource (a FileWriter in this example) would have been in a finally block, which followed the catch block. In Java 7, the FileWriter is opened in round brackets following the try keyword, marking it as a resource to be auto-closed. Once the try/catch block is completed, the resource will automatically be closed by the Java runtime system.

For classes to be used with the auto-close mechanism, they must implement the new AutoCloseable interface. All the relevant classes in the Java library have been retrofitted to implement this interface.

That already sums up the relevant Java 7 changes. As we can see — not much to worry about here. (This will be different next year, when JDK 8 will bring us closures. I expect we will have to have a lively discussion then about how to treat these in a first-year course. But we can leave that for a while.)

To find out more about the new constructs in Java 7, see this summary. The next version of the Object First book (5th edition, to be released in late October) includes discussion of these new features.

Note: This post was first published in the Blueroom.

Comparing Scratch, Alice and Greenfoot

logosAt ITiCSE 2009 and again at SIGCSE 2010, we had panel session: Comparing Alice, Greenfoot and Scratch. The session came about because all three development teams – the Scratch, Alice and Greenfoot teams – were regularly asked one question: What’s the difference?

All three systems aim to let young people learn about programming. Many teachers (as well as parents or kids) have heard of more than one of them, but deciding which one to use can be difficult. While there are clear differences, the time it takes to evaluate all three of them is not trivial.

The panel session turned out to be very popular. The room was packed full, and we got plenty of questions afterwards. So we decided to create a written version of this session. And it’s now available.

We wrote a set of papers, which have now been published in a special issue of the ACM Transactions of Computing Education (TOCE). Each paper was written by a key member of the design team of one of the environments. The papers are more extensive and more in depth than the panel was (we all took the chance to write about various aspects our systems, which we had intended for some time, but never got around to doing), but they also aim to record some of the discussion that we had at the time.

Target age groups for Alice, Scratch and Greenfoot

The TOCE special issue consists of an introduction, three papers (one each) about the three environments, and a discussion section at the end. They are

(NOTE: All papers are available in the ACM digital library. ACM allows authors to also publish the papers on their own web sites. I will link to those freely available copies when they have been made available by the authors. So check back in a little while if you could not get all you were interested in.)

Quality-oriented teaching of programming

One of the great mysteries of introductory programming education is what’s commonly known among computing education practitioners as the double hump.

The double hump refers to the marks distribution in a typical introductory programming course. Here is an example:


Figure 1: The double hump distribution of marks in a programming course

This graph shows a typical distribution of final marks in a first programming course. We have a whole bunch of students getting very good marks and a lot of them failing. Mark Guzdial, in a blog post, calls it the 20% Rule:

“In every introduction to programming course, 20% of the students just get it effortlessly — you could lock them in a dimly lit closet with a reference manual, and they’d still figure out how to program. 20% of the class never seems to get it.”

The surprising thing about this distribution is how constant it seems to be. It has been observed in programming courses all over the world, largely independent of geographical or social context, and over a long period of time: The same pattern that we observe now existed 10 years ago, and 20 years ago, and 30 years ago.

(And an ironic side note is that we as teachers tend to react to this by aiming our teaching at the medium level—thus teaching to a group that contains hardly any students at all…)

So what is this telling us?

For programming teachers, this poses some interesting questions and challenges. One conclusion has been that the ability to understand programming is somehow intrinsic. That there are people who are just good at it, and others how simply cannot get it. This has led to a whole lot of research about programming ability predictors: ways in which we can predict, by looking at people’s prior activities, performance, social context, or any other aspect of their lives, or with a hopefully simple test, whether or not they will be successful in learning programming before we make them go through it.

Some people really believe in these predictors, some do not. I am generally in the second category. Let’s say, I am at least highly sceptical. The first paper linked above, for example, draws what I believe to be severely invalid conclusions. It interprets the data in a way that the data just does not support. But that’s a story for another day.

What I really want to talk about is an idea that I picked up from a fascinating seminar that Anthony Robins gave a few months ago in our department: That the cause of the high failure rate that we are observing might not be any kind of intrinsic capability, but caused by the sequential nature of the material we are teaching.

What is going on might be this: The material in programming courses is highly hierarchical. Every topic strongly builds on the topics previously covered. Thus, if you don’t understand one section of the course, you will likely also struggle in the following sections, unless you spend extra time to catch up and make up for what you missed before.

Therefore, programming courses are self-amplifying systems. If you fall behind a bit towards the beginning, for whatever reason, you are likely to fall more and more behind in the following weeks and months. If you get ahead early, you are likely to stay ahead, or even move further ahead. The short of it is that this dependancy of performance on prior performance in the course will automatically divide the class into two distinct groups whose performance drifts further apart as time goes on.

Voilá, the double hump is born.

Anthony, in his seminar, made some very interesting points that provided strong indicators that this might be what’s going on. He is, as far as I know, in the process of publishing these findings, so I won’t go into too much detail here. (I will update this post with a link once his paper is out.) We can leave discussion of whether we belive this or not for another day.

For today, I’ll simply state that I find it entirely plausible that this is at the root of the problem. So for now, I will work with the premise that the double hump is not caused by some intrinsic personal capability, but by the strong hierarchical nature of the material we teach.

The question that I asked myself then is: What should we do about it?

My conclusion is that we need to shift from quantity-oriented teaching to quality-oriented teaching. Here is what I mean.

In a typical programming course at a university today, we may have a number of assessments. (For the purpose of this argument it does not matter what that number is—it might be a smaller number of large assessments or a larger number of small ones.) Students get marked on every assessment, and they pass the course if their average grade is above some defined pass level. Figure 2 shows a student in this scheme.


Figure 2: A good student attempts six assessments and passes most of them

In this example, there are six assessments and the student is doing okay: The average mark is above the pass level. The pattern for a weak student might look like Figure 3: The student also attempts six assessments, but mostly fails.

Figure 3: A weak student attempts six assessments and mostly fails

And this is a problem. It is not only a problem that the student is failing, it is a failure to teach sensibly if we believe in the hierarchical nature of our material. Once the student severely fails Assessment 2, there is really no point to let him/her move on to Assessment 3. In fact, it’s ridiculous. We are essentially saying: “We have just established that you did not understand concept X, so now go on to study concept Y, which is harder and builds on concept X.”

Now, that’s just plainly stupid.

We have essentially demonstrated that you have no chance to move to something harder, and then make you move on to something harder. Without giving you a break to catch up. The problem is: This is exactly what we usually do.

So what can we do about it? The solution might be to re-orient the pass line in our diagram (Figure 4).

Figure 4: Re-thinking the pass level

Typically, in our courses, the x-axis (number of assignment students do) is fixed, while the y-axis (quality of submissions) is variable. And we define success as reaching a given level of quality (see Figure 3).

We should turn that around. We should fix the y-axis and make the x-axis variable, thus re-defining success as successfully completing a certain number of assessments. This means that every student has to achieve a defined minimum level of quality in each assessment before they are allowed to move on to the next assessment. The differentiation between students then is not what quality level they have achieved, but how many stages of assessment they have managed to complete. In a diagram, it looks like this: Figure 5 shows a good student at the end of the course. Nine assessments have been completed, beyond the pass level of assessment 6.

Figure 5: A good student completing enough assessments to pass

The graph for a struggling student then looks like this (Figure 6).

Figure 6: A a struggling students failing to reach pass level

This student has not reached pass level and fails the course. Note, however, the difference here to our student in Figure 3: There are no assessments recorded below the minimum accepted quality level. (Every submission attempt with insufficient quality is simply judged as “not completed”.)

I don’t believe that this scheme will solve all our problems, but I do believe that it has a number of advantages:

  • Students may have a higher chance of success. Instead of early failure leading almost automatically to a sequence of further failures, they have a chance to recover.
  • Students who learn at different speeds can all survive.
  • Especially good students can go even further than they did in the past, because we can allow them to move forward according to their own capabilities.
  • More flexibility in dealing with temporary problems: If a student misses the first three weeks of class, be it because of illness or any other reason, they may still pass the class instead of having no chance.
  • Even failing students learn something. Instead of failing to understand every topic (Figure 3), they can at least spend their time understanding a few topics (Figure 6).

There are a number of problems and challenges as well, of course. The obvious difficulty is that we must be able to allow different students to work on different material at the same time. Every student effectively progresses at their own personal pace, and the teaching must support this.

Is this organisationally possible? I think so.

It is not easy, and it requires us to severely change how we teach, but I think it’s worth it. It will require different form of instruction (away from big lectures to the whole class) and different form of assessment (away from giving everyone the same task and assessing it by just submitting source code). Otherwise there would be a problem with plagiarism. We might need to assess by interviewing students to actually test their understanding!

Does this create work? Sure. But I think it’s worth it. And I actually believe that, once the change is made, the workload for teachers will be comparable to what we have now.

In an ideal world, this way of learning would have the result that different students learn the same material in a different amount of time. In reality, we will not get away from fixed length courses for the time being.

This means, that—at the end of a course—different students will have studied different amounts of material. Some students who move on to the next course will have covered the advanced material, some have not.

There will be an objection from other teachers that we let students pass who have not seen all the material, and that they do not know some concepts that they should know.

While this is true, I don’t believe that this is any different from our current situation. Which teacher would really claim that all students who passed their course have understood all concepts they were teaching? If one of my current student just barely clears the pass mark, there is a lot that we have covered that they don’t know. Claiming that they know more material because I talked over their heads about more advanced concepts for longer is nonsense.

I would rather have a student who properly understands 50% of the concepts than a student who half understands (and half misunderstands!) everything.