Every teacher, lecturer, and professor needs to be fluent in computational thinking!

Scaling educational innovation in higher education is difficult. Besides of managerial support and organizational commitment, developing a practice of learning design as process modelling is among its core success factors. With the increasing digitization and globalization of industrial societies, increasingly there is a demand to develop relevant skills. Among the many slogans related to these new basic skills is computational thinking. In this article, I focus on the question, why is computational thinking not just a learning objective for students but also a capacity of educators?

Computational thinking is a mindset not a subject

One of the main misconceptions I find frequently that computational thinking is handled like a subject like mathematics or languages. Because of the term "computational", the interpretation of computational thinking tends to lead towards computer science and programming - and many teachers and lecturers consider it therefore out of scope for their subject, discipline, or area of interest. The connotation as a subject makes this separation even easier, because it allows arguing that computational thinking is actually part of a different discipline than one's own. However, computational thinking is a mindset for understanding and describing processes and interactions. This mindset is at the core to describe and study the relations within systems for transparency, optimization, automation, and eventually innovation.

Computational thinking is not programming

For discussing computational thinking, it is important that it is not the same as programming. While programming refers to expressing algorithms so computing devices can execute them, computational thinking, on the other hand, emphasizes on understanding systems and processes in ways that allow identifying patterns and algorithms. This is quite different to programming.

The key core elements of computational thinking are systemic abstraction, understanding of processes, and data awareness. Systemic abstraction refers to the ability to think in, out of and across boxes. Understanding processes is essential for creating links between the boxes and being able to define trajectories and flows. Data awareness is necessary for setting the input and getting the output of a system's boxes as well as for defining indicators that help measuring the state of the processes in the system.

Why educators need to be fluent in computational thinking?

The core aspects of computational thinking are not specific to learning or education. So why do I claim that every teacher, trainer or professor should be fluent in computational thinking? Computational thinking links to learning and education in the form of learning design. Many educators know the core principles of learning design as "curriculum development" and "teaching plans". However, learning design goes beyond these concepts by expanding beyond conceptual and sequential consistency to the level of the learners' experiences.

Learning design is computational thinking!

The activity of designing learning experiences includes the preparation of informing and verifying learning achievement from the enactment of the learning activities. Educators know this as the desired and actual learning outcomes, which are the foundation for assessment and grading. Learning design also includes the planned arrangement and interactions of social forms, learning, tools and technologies, as well as the practices related to the learning objective.

In order to master learning design, I argue, computational thinking is crucial. This is independent from digitizing learning and education. However, if digital technologies are included into the learning experiences, educators will find it hard to achieve useful results without computational thinking. This is particularly the case for supporting education and human learning through learning analytics and AI. Educators at all levels need to be capable of designing and arrange their educational units in ways so they can generate meaningful data on their students' learning paths.

Learning activity design is not just about selecting learning resources

Educators need to comprehend learning activities as systems as they shape the individual learning. This requires a systemic as well as systematic understanding of the alignment and the dependencies of learning objectives, activities, outcomes, environments, resources, and tools. Consequently, a typical unit of learning is not just a single system but is a collection of systems with specific functions for the learning and teaching process.

Affordance, action and feedback

Learning designers consider learning activities as the functional building blocks of the learning process. These building blocks always have the same internal structure:

  • Affordance or task description
  • The generation of the activity outcomes by the learners
  • Feedback on the learning outcomes

It helps to think of these elements as functions and not as a process on its own: the affordance informs the learners to perform an activity and present the result, which means that a learning activity requires means to collect these results in a format that allows producing a meaningful feedback. Leaving one of these steps breaks the learning activity system. Thinking of these elements as inseparable activity functions rather than sequences is the key to avoid over scripting: Any step that does not have an output that generates a feedback, is not a learning activity that should not get included into a learning design.

All aspects of learning experiences are part of the design process

While learning designs should be limited towards the granularity of the learning activities, they need to consider the logistics of educational processes. This includes the administrative and organizational activities that do not directly contribute to the learning experience. While most modern learning management systems emphasize on supporting these activities, educators are often unaware of them. I experience that educators find it hard to identify these logistical tasks because they tend to hide them within other activities without highlighting their specific functions and procedural dependencies.

Conventionally, the systemic perception of learning and education considers the arrangement of learning resources, tools and the learning environment and often exclude the role of the facilitators, social interactions, internal rules of an activity, and the feedback. These additional elements are often set outside of the system.

Modularization is the key to personalisation and new ways for assessing and scaling education

Within curricula and teaching plans, educators arrange learning activities into processes. In the analog realm, such processes remain mostly sequences with minor variations. Only at the higher curricular levels of study programs, we find procedural variations more often. The understanding of processes includes that educators are able to analyze their teaching plans as arrangements of the functional systems that define the individual learning activities. This includes clustering and sequencing these activities as well as deconstructing these activities into sub-processes. At this level, educators need to be aware about the different concepts of controlling the educational processes.

Educational design theory often underestimates the data awareness as a central competence for educators. Data awareness fuels the learning process. In the processes of learning design, this implies that educators need to be able to define the prerequisites (input) and learning outcomes (output) of the learning activities, because they are the key for monitoring and controlling learning processes as well as for automating them. Moreover, data awareness is the foundation for new forms of digital assessment as well as for scaling up personalized learning.

Understand how data characterizes different types of learning and achievement

Data awareness includes not only awareness of different data formats that students produces as their learning outcomes, but also awareness about the traces that are characteristic for different types of learning and achievement level. This is not only relevant in technology-enhanced learning but expands to conventional forms of education. Being aware about the characteristic traces of learning is beneficial for focusing on the relevant aspects even if no technology is involved.

Identifying the data that the different learning activities generate is key for learning analytics, because we can only analyze what we collect. This leads to the major misconception of many educators that learning analytics only considers the access of resources and clicks and that this is used by a management for controlling and evaluation their teaching. This perception is partly due to the default behavior of many learning management systems that primarily feed resource accesses and clicks into their analytical engines. Another aspect of this misconception is that many educators do not fully understand the relations of data and their educational designs. Therefore, data awareness implies that educators know about the kind and granularity of data the activities in their learning designs generate and collect.

Transferring educational practices depends on a computational thinking mindset

Learning design refers to the design of complex social and interactive processes. Abstracting these processes for digital and non-digital learning solutions is not easy. However, this kind of abstraction is essential for expanding concepts and practices and transfer them to other subject domains or to new communication and mediated modes and technologies. The success of such transfers is dependent on a mindset that is rooted in computational thinking. This mindset has little to do with the ideas of programmed instruction or software programming. It seems closer related to creative activities such as design or modelling. These are skills, which all educators need to be capable to apply to their own practice and not just expect their students to develop.

Further reading

  • CIRCLE Center Webinar on Computationational Thinking (double feature, with additional resources) (Feb. 2018)
  • CIRCLE Center Computational Thinking Primer
  • Pierre Dillenbourg: Orchestration Graphs. EPFL Press, 2015.
  • Diane Laurillard: Teaching as a Design Science: Building Pedagogical Patterns for Learning and Technology. Routledge, 2012.
  • Rob Koper & Colin Tattersall: Learning Design, A Handbook on Modelling and Delivering Networked Education and Training. Springer, 2005.
  • Marion Gruber et al: Orchestrating Learning using Adaptive Educational Designs in IMS Learning Design. Proceedings of EC-TEL, 2010. online at
  • Pierre Dillenbourg: Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In: P. A. Kirschner. Three worlds of CSCL. Can we support CSCL?, Heerlen, Open Universiteit Nederland, pp.61-91, 2002. online at