Designing Learning Trajectories for Computational ThinkingAllard StrijkerPetra Fisser

National Institute For Curriculum Development NetherlandsNetherlands

a.strijker@slo.nlp.fisser@slo.nl

Abstract: In 2015 a curriculum framework for Computational Thinking was developed for lower secondary education in the Netherlands. The framework is based on ISTE and CSTA, translated for the curriculum in the Netherlands and validated by experts, teachers, teacher educators and publishers. Based on this validated curriculum a new learning trajectory was designed for primary education, in collaboration with teachers and principals from three schools. The first version of the learning trajectory consisted of intermediate goals around coding and programming with unplugged learning activities to make implementation as easy as possible for teachers with little experience with CT. Many schools adopted the learning trajectory for coding and programming enthusiastically. The goal for next year is to design and validate a further developed learning trajectory for primary and secondary education, covering all topics of CT and including both unplugged and hands-on plugged experiences learning activities. Keywords: Computational Thinking, Learning Trajectory, Digital Literacy, Curriculum Framework, Primary Education, Secondary EducationComputational Thinking

According to Wing (2008) CT is a competency that should be added to every child’s repertoire of thinking abilities. Identifying how and when children learn this ability and how it should be taught needs research. The idea that CT should be incorporated in education has been taken up by several professional bodies such as The Computer Science Teachers Association (CSTA) and the International Society for Technology in Education (ISTE). ISTE and CSTA worked together to develop materials to help educators understand, value, and implement computational thinking in K–12 education.

Research on implementing CT in a K-12 setting is still in progress (Yadav, Zhou, Mayfield, Hambrusch, & Korb, 2011; Yadav, Good, Voogt & Fisser, 2017). Barr and Stephenson (2011) argue that students need to be able to process and analyze data to create real and virtual artefacts. In their view the core concepts for CT in K-12 education are: data collection, data analysis, data representation, problem decomposition, abstraction, algorithm & procedures, automation, parallelization and simulation. Grover and Pea (2013) provide similar concepts for CT, describes as a problem solving process with the following characteristics: 1. Formulating problems in a way that enables us to use a computer and other tools to help solve them. 2. Logically organizing and analyzing data; 3. Representing data through abstractions such as models and simulations; 4. Automating solutions through algorithmic thinking (a series of ordered steps); 5. Identifying, analyzing, and implementing possible solutions with the goal of achieving the most efficient and effective combination of steps and resources; and 6.Generalizing and transferring this problem solving process to a wide variety of problems (Grover & Pea, 2013). These kind of concepts clearly extend cognitive activity of CT from the action of merely using digital technologies such as working on a computer or other digital device, word processing and/or creating webpages.

A learning trajectory for CTThe question remains where CT should be situated (separate subject or a cross-curricular subject), and what CT concepts should be learned in the different stages of schooling (Voogt et al., 2015). Key stages or learning trajectories have to be designed to help teachers teach the concepts of CT from primary and secondary education. Learning trajectories give the opportunity to personalize learning by adjusting learning goals and related learning activities to the possibilities of the learners. Learning trajectories are defined as "a reasoned structured set of intermediate objectives and content leading to a certain core objective" (Strijker, 2010). While designing a learning trajectory for CT, the core concepts of Barr and Stephenson where translated for the curriculum in the Netherlands. Based on these core concepts (data collection, data analysis, data representation, problem decomposition, abstraction, algorithm & procedures, automation, parallelization and simulation), the core objectives where defined for lower secondary education. These core objectives were designed in terms of can do statements and validated by experts, teachers, teacher educators and publishers in 2015. During the validation process several requests were made for the design of learning trajectories starting from primary education ongoing to secondary education. Honoring this request meant designing the intermediate objectives for primary education that lead to the core objective of secondary education.

Primary education in the Netherlands starts at the age of 4 and takes 8 years, so children are approximately 12 years old when they switch to secondary education. Lower secondary education takes 2 or 3 years. For primary education three levels were defined to design intermediate objectives for: beginner (age 4-6), intermediate (age 7-9) and advanced (age 10-12). This continues in lower secondary education with an expert level (age 13-14). So for the learning trajectory there are four levels of learning objectives that al cover an period of ages and are used as an indication for the use for a certain age, because learners vary in skills that they contain. The variation in skills can be in personal interest, attitude and capabilities. Bringing learning trajectories in practiceDefining concepts and objectives as a curriculum framework give schools the ability to use this framework for designing lessons or select learning resources that fit the (intermediate and core) objectives.

The first version of the learning trajectory consisted of intermediate goals around coding and programming with unplugged learning activities to make implementation as easy as possible for teachers with little experience with CT. The selected learning resources where mainly unplugged learning activities from unplugged.org and the learning activities where translated in Dutch. The goal for next year is to design and validate a further developed learning trajectory for primary and secondary education, covering all topics of CT and including both unplugged and hands-on plugged experiences learning activities. Schools will be involved in the design process to ensure a successful implementation.

Teachers and curriculum designers work together in teacher design teams to describe learning goals and lessons that fit with both the curriculum framework and school-specific interests. Choices are made in relation to how learning activities are used within existing subjects such as Math or languages or as a separate subject like programming. Also the way of testing, coherence and consistency within the learning trajectory and the time spent on CT are decisions to be made. Principles support teachers in development time, financial resources, pedagogical experts and required curriculum changes.

This way of working in teacher design teams increases the success of implementation of CT at the schools that are involved in the design process. At the same time this way of working is a further validation of the CT learning trajectory which may lead to a real nation-wide learning trajectory for Computational Thinking.

ReferencesBarr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54.

Grover, S., & Pea, R. (2013). Computational thinking in K-12: A review of the state of the field.Educational Researcher, 42(1), 38–43.

ISTE & CSTA (2011). Computational thinking. Teacher resources.http://csta.acm.org/Curriculum/sub/CurrFiles/472.11CTTeacherResources_2ed-SP-vF.pdf

Strijker, A. (2010). Leerlijnen en vocabulaires in de praktijk (Learning trajectories and vocabularies inpractice) (in Dutch). Stichting Leerplanontwikkeling (SLO), Enschede.

Wing, J. (2008). Computational Thinking and thinking about computing, Philosophical Transactions of the Royal Societ, 366. doi: 10.1098/rsta.2008.0118 , published 28 October 2008.

Yadav, A., Good, J., Voogt, J., & Fisser, P. (2017). Computational thinking as an emerging competence domain. In: Mulder, M. (Ed). Competence-based vocational and professional education, pp. 1051-1067. Springer

Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J. T. (2011). Introducing computational thinking in education courses. In Proceedings of ACM Special Interest Group on Computer Science Education, Dallas, TX..