The tech industry is a fast-growing field, with many companies facing issues in finding skilled workers to fill their open vacancies.At the same time, many people have limited access to the quality education necessary to enter this job market.To address this issue, various small and often volunteer-run non-profit organisations have emerged to up-skill capable learners.However, these organisations face tight constraints and many challenges while trying to design and deliver high-quality education to their learners.In this position paper, we discuss some of these challenges and present a preliminary version of a curriculum packager addressing some of these issues.Our proposed solution, inspired by first-hand experience in these organisations as well as computing education research (CER), is based on a combination of micromaterials, study lenses and a companion mobile application.While our solution is designed for the specific context of small organisations providing vocational ICT training, it can also be applied to the broader domain of learning environments facing similar constraints.

The field of Computing Education Research (CER) produces important insights into learning and notable interventions, yet due to the research/practice divide these do not have the desired impact on learners or practitioners. Even within CER, Computing Education (CE) learning theories have limited influence on learning designs due to the theory/design divide, which is unfortunate given that the goal of CER is to impact learners and broaden access to computation.

Learning to program can often be a daunting task. The syntax of new programming languages might be intimidating and on top of that compilers are unforgiving. The combination of these things can be discouraging for new students. Recently there has been a push in the field of Computing Education Research to apply the Predict-Run-Investigate-Modify-Make (PRIMM) approach when teaching programming concepts. The aim is to push students to practise reading and investigating existing code before going on to write their own code, as a way to build self-confidence and boost their understanding. We present an educational game that aiming to help students practise their code prediction abilities. The game uses a simple story set in a medieval fantasy kingdom to create a welcoming, less intimidating environment. Users are presented with randomly generated pieces of JavaScript code that modify four core Boolean variables (shield, sword, cape and helmet). By making use of the state tables, students can manually walk through the code and predict the final result. A user ends the round by selecting the hero that matches the final state of all four variables. The presented heroes are further randomly assigned physical characteristics such as gender, skin tone and hair colour, promoting inclusivity and lowering the bar of entry via representation. The game allows users to indicate which language features they already know, thereby generating JavaScript code snippets that are understandable by a broad variety of students.

The learning of new knowledge and skills often requires previous knowledge, which can lead to some frustration if a teacher does not know a learner's exact knowledge and skills and therefore confronts them with exercises that are too difficult to solve.We present a solution to address this issue when teaching techniques and skills in the domain of table tennis, based on the concrete needs of trainers that we have investigated in a survey.We present a conceptual model for the representation of knowledge graphs as well as the level at which individual players already master parts of this knowledge graph.Our fine-grained model enables the automatic suggestion of optimal exercises in a player's so-called zone of proximal development, and our domain-specific application allows table tennis trainers to schedule their training sessions and exercises based on this information.In an initial evaluation of the presented solution for personalised learning environments, we received positive and promising feedback from trainers.We are currently investigating how our approach and conceptual model can be generalised to some more traditional educational settings and how the personalised learning environment might be further improved based on the expressive concepts of the presented model.