What’s next in computational thinking in higher education?

Xiaoling Zhang Education, healthcare, and governance – modern society is becoming ever more digitalised, making computational skills a crucial asset in both normal and work life. But that doesn’t mean everyone should learn to program or obtain a master’s degree in computer science. Xiaoling Zhang researches the embedding of computational thinking in higher education.

A loop with a twist
Computational thinking is a skill set that human beings have utilized for problem-solving since long before the first computers arrived at the scene. Its application therefore is also anything but limited to computer science. ‘Sharing many similarities with problem-solving, computational thinking can be applied everywhere - from your daily life to your professional life,’ Xiaoling says. ‘But where a computer scientist may call something a “loop” or “iteration”, other domains may apply these concepts calling them differently, or even without naming them. The same holds for other aspects of computational thinking; conditionals, data, abstraction and modularising, to name a few.’

‘People must be aware of both the advantages and limitations of computational thinking,’ she says. ‘The lowest level is that students should be able to talk to people who use and create the corresponding technology. A master’s degree in computer science – understanding fundamental operational aspects of computers, the computational resource limits for implementing an algorithm, you name it – that is the other extreme. In between, I would like to create common understandings, or evaluate if people have common understandings.’

    Sharing many similarities with problem solving, computational thinking can be applied everywhere

Mind the gap
Just like the expected proficiency level, teaching of computational thinking skills also differs between the various higher educational domains. In some, they may be explicitly embedded in a programming or machine learning course. In other domains, such as architecture or biology, they may be more implicitly taught. ‘It is also important to realise that most of the time, theory is lagging behind what is happening in, and needed by, society,’ Xiaoling adds. ‘It is time for us to sit down, identify any gaps in existing and required skills, and come up with feedback strategies that promote the required computational thinking mindset.’

Computational and educational skills
Coming from a rural area in China, Xiaoling herself had her first hands-on experience with computers and electronics at the age of ten. It set her off on her own journey into mastering computational skills – through towns, cities, MS Office, the internet – ultimately leading to a master’s degree in computer science. ‘At university, I learned that you can use computational technology to do whatever you are limited to do in real life.’ Along the way, she helped her younger sister with educational problems, thereby learning about instructional ideas and the educational theory underlying them. She now applies both of these skills in her PhD project.

   We must first identify any gaps in existing and required computational thinking skills

The strategy
Her literature review showed that most of the research on computational thinking has been focused on teaching and learning programming, but not the underlying skills. ‘The relationship between them has seldom been researched,’ she says. ‘I am currently designing a survey, targeted at Leiden-Delft-Erasmus lecturers in general – from construction mechanics to English. I want to know their understanding of computational thinking skills. What is the frequency in which they use various concepts, and which concepts do they find important for their students to understand?’

She will subsequently design a digital framework to assess if students’ understanding matches the intentions of the lecturers. ‘The framework will likely provide the definition of various concepts, an example, and a test. Most importantly, it will provide feedback about their level of knowledge, and feed-forward as to where they may find more information if needed. ‘Unfortunately, this is probably how far I’ll get during my PhD,’ Xiaoling says. ‘But a next step could be to provide workshops on computational thinking to teachers. Or to organise a panel discussion with experts to select certain concepts everyone should understand given the current societal demands.’

Perfect at making things better
Only a few years ago, Xiaoling could have been quite disappointed by not reaching an ultimate goal. ‘At the beginning of my PhD I was dead set on developing a unified framework, something standardized,’ she says. ‘But that has changed, thanks to conversations with my colleagues at LDE-CEL. We are such a diverse group, so many nationalities and backgrounds, so many different perspectives – I learned that there is no one single best solution for everyone.’ But even without striving for perfection, she will continue to try and make the world a better place. ‘It could be in industry or in academia, but it will certainly stay in the field of education, communication and technology.’