For chess players to sharpen their tactical skills effectively, they train on chess puzzles with a fitting difficulty level. This paper presents an approach to estimate the difficulty level of chess puzzles using a deep neural network. The proposed approach achieved second place in the IEEE BigData Cup 2024 competition: Predicting chess puzzle difficulty. For the design of our network architecture, we take inspiration from the human problem-solving process for chess puzzles. We train the model to predict the correct move as an auxiliary task to improve the training process. We also predict themes, which are patterns in chess puzzles as a second auxiliary task. Finally, we use the uncertainty in the position, i.e. how incorrect the model’s move prediction is, as a further input to guide the estimation of the puzzle difficulty.

In this paper, we present a method for generating faded worked examples as personalized exercises aimed at bridging the gap between knowledge of theoretical concepts and their application in the real world, which is particularly important in vocational education. Previous works suggest that faded worked examples are effective learning material that can also adapt to learners of different levels. Yet, there is no formulated method for automatically generating faded worked examples personalized to different learners in real-time. We develop a method for generating faded worked examples from scenarios, changing the faded positions and degree of fading based on the targeted skills and the learner’s proficiency level. We evaluate our method through a user study involving 13 computer science students from a German university, who practice specific computer networking skills. The results indicate significant improvement in the targeted skill over the untargeted one, highlighting the potenti al of our approach in vocational education settings. Our study is an early but promising step towards the future of personalized learning, paving the way for further research in adaptive and personalized vocational training.

Difficulty adjustment in practice exercises has been shown to be beneficial for learning. However, previous research has mostly investigated close-ended tasks, which do not offer the students multiple ways to reach a valid solution. Contrary to this, in order to learn in an open-ended learning task, students need to effectively explore the solution space as there are multiple ways to reach a solution. For this reason, the effects of difficulty adjustment could be different for open-ended tasks. To investigate this, as our first contribution, we compare different methods of difficulty adjustment in a user study conducted with 86 participants. Furthermore, as the practice behavior of the students is expected to influence how well the students learn, we additionally look at their practice behavior as a post-hoc analysis. Therefore, as a second contribution, we identify different types of practice behavior and how they link to students’ learning outcomes and subjective evaluation measures as well as explore the influence the difficulty adjustment methods have on the practice behaviors. Our results suggest the usefulness of taking into account the practice behavior in addition to only using the practice performance to inform adaptive intervention and difficulty adjustment methods.