This paper describes an unplugged computational thinking (CT) resource for primary and secondary schools developed from Bebras tasks. In Ireland, CT is not part of the primary school curriculum or mandatory in secondary schools. However, the National Council for Curriculum and Assessment is in the process of revising the primary school curriculum to include aspects of CT. Our aim for creating this CT Obstacle Course is to introduce teachers (and pupils) without formal computer science training to the subject of CT. This is done in a manner that informs and motivates, and gives them the confidence to deliver CT materials in the classroom. We also want to find out from teachers how useful and important this type of resource is for developing problem-solving skills, and if our unplugged activity can support learning at various skill levels. Our CT Obstacle Course includes 14 Bebras tasks for primary schools and an additional 6 Bebras tasks for secondary schools. The activity is suitable for indoors and outdoors and is completed in groups, promoting teamwork and communication. We have delivered it to 146 primary school classes during 38 school visits between May 2021 and June 2022. It has been undertaken by 3,445 pupils and 195 teachers and other school staff. This paper describes our CT resource in detail, and reports teacher feedback from primary schools.

In this poster, we highlight computational thinking resources for schools from the PACT team at Maynooth University, Ireland. The resources are derived from tasks from the Bebras international computational thinking initiative. The different modalities work together throughout the school year to provide initial exposure to computational thinking, and include an obstacle course, seasonal tasks, and a workbook.

This paper describes our process of creating computational thinking (CT) resources for primary school teachers in Ireland. The National Council for Curriculum and Assessment has proposed a revised primary mathematics curriculum with an emphasis on CT skills and problem solving, and some teachers would like to introduce it already on an informal basis. However, CT is not yet part of teacher training. Our motivating question has been: how can teachers without a computer science background deliver CT at primary level in Ireland? Our process involves third-level computer science academics co-creating resources with in-service and pre-service teachers during workshops. The resources comprise a workbook and lesson plans. Our resources are based on tasks from the International Bebras Challenge, a well-known large-scale international CT contest with a reasonably gender-neutral profile of school-age participants. The workbook consists of ten Bebras tasks, each followed by a page of original activities on the theme of the task. A set of ten lesson plans accompanies the workbook. Each lesson plan has information about how to use the corresponding workbook activities in the classroom, where the activity might fit into the existing curriculum, categorisation of the task in terms of eight CT topics, differentiation, and extension activities. This paper explains our process of workshop planning, workbook creation, and lesson plan co-creation. Preliminary evaluation of our process uses teacher feedback.

This work piloted an international replication and generalization study on an existing prediction model called PreSS. PreSS has been developed and validated over nearly two decades and can predict student performance in CS1 with nearly 71% accuracy, at a very early stage in the learning module. Motivated by a prior validation study and its competitive modelling accuracy, we chose PreSS for such an international replication and generalization study. The study took place in two countries, with two institutions in Ireland and one institution in the US, totalling 472 students throughout the academic year 2020-21. In doing so, this study addressed a call from the 2015 ITiCSE working group for the educational data mining and learning analytics community: systematically analyse and verify previous studies using data from multiple contexts to tease out tacit factors that contribute to previously observed outcomes. This pilot study achieved 90% accuracy, which is higher than the prior work's. This encouraging finding sets the foundations for a larger scale international study. This paper describes in detail the pilot replication and generalization study and our progress on the larger scale study which is taking place across six continents.