Future jobs will require students to handle interdisciplinary knowledge that goes beyond computing techniques when it comes to mathematics education. The traditional approach to education insufficiently develops the needed mathematical skills and mathematical literacy and there is a need to devise new methods of teaching and learning. We introduce game-based learning in mathematics education through carefully designed activities aiming at interdisciplinarity and increasing students’ motivation, mathematical and digital literacy, physical activity, level of knowledge, etc. We propose the treasure hunt cantered around cryptography as an example activity that satisfies the described criteria. Parts of the clues leading to the treasure are hidden using different ciphers, following the historical development of cryptography. The treasure hunt is time-limited to one hour and, to successfully complete the search, students need to learn, adapt, and apply different methods of encryption and decryption. To complete the hunt in time, students must work as a team and effectively divide the tasks among themselves. Students have at their disposal all instructions and manuals in written form, and they are compelled to read with comprehension. We conducted the activity with several groups of high school students with no previous knowledge and skills in the field of cryptography. Students were asked to take a pre-test and post-test survey about their experience and familiarity with cryptography concepts. Most teams completed the search and achieved all learning outcomes. In the paper, we will describe the activity in detail, analyse the test results and give additional examples of game-based activities in mathematics education.

The game [ai] explore! was developed to introduce the algorithm behind the popular science exhibition. The idea behind the [ai] explore! exhibition was to show the application of mathematics and computer science in solving real engineering problems by creating the exhibit using the mathematical algorithm Heat Equation Driven Area Coverage (HEDAC). The algorithm is based on scientific research and has many applications: exploration and search in unknown space, painting, planning agricultural spraying, scanning 3D objects, etc. The exhibits show the process by which the algorithm paints objects with a virtual brush and explores their shape. By surveying the high school students, it was shown that new mathematical and engineering results can be explained and turned into a playable game. After playing the game, students respond positively to the game presented and state that they have a good understanding of the HEDAC algorithm presented and how it works. This motivated us to investigate [ai] explore! as an educational game for teaching mathematics and computer science. The aim of the research described in this paper is to investigate the potential of the developed game as an educational game. The game-based learning (GBL) approach is increasingly used to increase student motivation and engagement in STEM lessons. The main research question is: How do primary school teachers and students perceive the usefulness of the game [ai] explore! in supporting teaching and learning of computer science concepts? We have developed and implemented teaching scenarios on how the game [ai] explore! can be used as an educational game in computer science lessons. We show that the game has great potential to teach primary school students programming. In addition, the game promotes algorithmic thinking and computational thinking skills in general, which are valuable not only in computer science but also in other fields such as mathematics, where a systematic and analytical approach to problem-solving is required.

By a suitable extension of incidence matrices, orbit matrices, and submatrices of orbit matrices of weakly $ p $-self-orthogonal $ 1 $-designs, in this paper we describe a construction of LCD codes over a finite field. To illustrate the construction, we determine some binary LCD codes from self-orthogonal $ 1 $-designs which are invariant under the transitive non-regular action of the alternating group of order $ 60 $. With the described construction, we have obtained $ 6 $ optimal codes and $ 3 $ near-optimal codes.

This study examines programming achievement among Grade 4 students participating in a structured Game Development-Based Learning (GDBL) approach compared with students receiving traditional programming instruction. The intervention used a single coherent problem context derived from the [ai] explore! game to support the progressive introduction of programming concepts in Scratch. A total of 104 students aged 9–10 from five classes in a Croatian elementary school participated. Two classes were assigned to the experimental condition and three to the control condition. Students in the experimental group progressively developed a playable game over seven weeks, while the control group followed traditional programming instruction. An initial knowledge test was used to assess baseline programming knowledge, and a post-intervention assessment served as the primary outcome measure. Because the instructional condition was assigned at the class level, the primary analysis used an exact class-level randomization test. The experimental classes had higher average post-intervention scores than the control classes, with an observed difference of 0.140 and a two-sided exact p-value of 0.20. A secondary student-level Mann–Whitney U comparison also showed higher post-intervention scores in the experimental group (U = 581.0, p < 0.001, rank-biserial correlation = 0.560). Teacher observations provided contextual information but were not treated as validated measures of motivation or engagement. The findings show a descriptive pattern of higher programming achievement in the GDBL classes, while the class-level inferential evidence remains limited. The study also provides practical design principles for classroom implementation.