How is programming taught in code clubs? We asked the instructors, and this is what we found

(reposted from here)

Programming is taught everywhere: at schools, independent code clubs, Code Clubs, CoderDojos, Girls Who Code sessions, after school programs, coding summer camps, workshops in libraries and museums. At schools, the lessons are given by teachers, following national curricula. However, what about the lessons at code clubs? Who teaches, what is taught, what material is being used, and how are children learning programming? What do children find difficult, and are there any differences between boys and girls?

To find answers to our questions, we designed and run a survey. We invited code club instructors to answer through Twitter, Facebook groups of code clubs, Slack channels, newsletters and even direct emails. We collected 98 responses, we analysed them, and this is what we found.

Participants, lesson material and style

Many of the code club instructors who answered our survey have a main degree related to computer science (49%) or other STEM-related field (10%). Only a small group has a background education (18%). Almost half have no education experience, while the majority of the instructors (90%) can program in at least one commercial programming language.

Most code clubs have a small number of participants of varying ages. Female students are under-represented at code clubs; the average percentage of female students is 30%.

Most of the instructors replied that their code club is part of a program, mainly CoderDojo (36%) or Code Club (31%). The students attending Code Club classes are younger and more often female than in CoderDojo clubs.

The most commonly taught language is Scratch (89%), followed by Python in almost half of the code clubs, followed by Arduino, Mindstorms, Micro:bit, HTML, Java, JavaScript, Blockly, C-like languages (e.g. ArduC, RoboC or NXT-C). Within the taught languages we also found Sonic Pi, Blender, Snap!, Swift, MBlock, Spheros, Flowol4, Crumble, Codebug, Node JS, Lightbot and A.l.e.x.

Regarding lesson material, half of the teachers responded not using a specific lesson plan. Some declared using lessons or the Scratch Creative Computing Handbook. The students mostly work independently on their own projects (71%) and, in some code clubs, the teachers give plenary sessions. Formal assessments and grading are rare; more common are stickers or badges for achievements (47%) and other forms of formative assessment.

What do the students struggle with?

Instructors most commonly identified debugging/error messages and abstract thinking as the main difficulties. A teacher whose students are from 8 to 12 years old related the age of the students to that, reporting that (translated from Dutch) “sometimes children that register for my workshops are too young and find abstract thinking too difficult to really understand what they are doing.”. In terms of programming concepts, variables and functions were identified as the most difficult for their students.

Instructors also identified struggles related to creativity, for example “thinking for themselves instead of blindly following the tutorial” and “design something for themselves and implement that.” Related to concentration, a teacher reported that students get “distracted by playing games.” Another teacher reported the student focus on language as a learning barrier, as students “often become focussed on learning Scratch itself, rather than building higher-order skills.”

How are boys different than girls?

Teachers identified gender differences especially for two traits, namely confidence and concentration. In the question “Who is more confident?”, 50% of the responses lean towards boys. A teacher added that “I get initial “I will never understand this” reactions way more from girls than from boys. Completely invalidated after an hour or so of course, but still saddens me.” This is worrying, because gender differences related to confidence could have several implications. In prior work we have found that, especially for female elementary school students, self-efficacy is strongly correlated with how attractive they view computer science as a career path.

On the other hand, in the question “Who seems to concentrate better?” 65% of the responses lean towards girls. A teacher explained that  “Girls tend to stay on-task more, whereas some boys can be easily distracted”.

Gender differences were also reported in the preferred type of projects, with girls preferring  storytelling and visual/creative exercises, and boys preferring to implements games. Girls were identified as more responsive to instruction, whereas “Boys just start blindly without reading lessons and then run into trouble pretty quickly, then call for help. Girls tend to focus more, start reading and ask questions when they’re really stuck.”

Collaboration skills are described to be increased for girls, as “Girls tend to talk and discuss more when working in partnerships whereas boys tend to have one who takes the lead”, as well as focus (“All of the girls in my club have always been more careful and methodical. They seem to want to understand what they are doing more and don’t mind taking their time.”).

Want to know more? Read our paper, or come and see us present it at Koli Calling.

“When I grow up I want to become a programmer!”, Elli (11) – How?!

(the answer is at the end of the post)

Computer Science is not the typical profession of choice for elementary school students. This might be because most children at this age do not yet understand what computer science is. This is about to change, since an increasing number of countries is currently enriching their elementary school education with computing and programming courses. But, what will make those young students perform well in programming classes? And what will their view of programming as a career path be after they have followed their first programming course?

Finding out

To answer our questions, with Felienne Hermans we run experimental programming courses, teaching Scratch to four groups of 8 to 12 year-old students of two elementary schools in the Netherlands. We gave a total of eight lessons to each group, following the lesson plan of the Scratch MOOC on edX. During the lessons, we measured factors that have been shown to affect adult, university-level students. Those factors include:

  • Self-efficacy, or the students’ beliefs in their abilities. In education research, self-efficacy is recognized as one of the most important factors related to learning performance, and has been found to affect the choice of college major. (“I will become what I am good at.”)
  • Extrinsic (external) motivation, or motivation inspired by influences outside of the individual (for example, from other people). (“I will choose what is trendy/what my peers choose.”)
  • Intrinsic (internal) motivation, or motivation that comes from within the individual as a self-desire to learn. (“I will choose it because I like it.”)
  • The stereotypes that students assume for computer scientists, and their fit within those. We examined four stereotypical traits that have been found to apply to computer scientists [17]: Singularly focused, indicating that computer science requires an obsession with it, asocial, indicating that computer scientists have limited social skills, competitive, and male. (“I will choose what my personality fits in.”)
  • The characteristics of the students, including their age, gender and programming experience prior to the course.


We found that having previous programming experience was a strong factor, correlated to extrinsic motivation, self-efficacy and CS career orientation. While it is known that prior programming experience has a  strong effect on the performance of university-level students, in our study there is an additional reason for this observation: that programming experience before our experimental course could have been obtained only through home-based or extra-curricular activities. Therefore, having prior programming experience indicates that the students had sought to learn programming themselves, or were encouraged by their environment towards programming, and this proved to give them a significant head start.

Our study highlighted gender differences, with the CS career orientation of the girls being significantly correlated with their self-efficacy, an effect which was not as strong for boys.

We also found that students’ intrinsic and extrinsic motivation are important factors, strongly correlated with their self-efficacy and, for the case of intrinsic motivation, their inclination towards a CS career.

Equally interesting is what we did not observe: course performance and stereotypical beliefs for computer scientists had no significant effect on CS career orientation. Students actually appeared unaffected by the four stereotypical traits that we studied. Even the Male trait was not assumed, with students favoring neutral or their own gender as typical for the profession. This might be attributed to their age; we could assume that they are yet too young to believe anything in particular about stereotypical traits of computer scientists. It also leaves us hopeful for the future of women in CS.

The answer

To sum up, Elli(11), probably did not wait for school to teach her programming, she had started already by herself. She likes the challenges of programming and she believes that she is good at it. Also, she has not watched movies about programmers yet, and she better leave it for after she chooses college major.

Are you interested to know more? Read our paper or come see us present it at SIGCSE 2019.

***Reposted from here***