Our second hypothesis – that Solver`s training and profession would be primarily in scientific or computer fields rather than in language-related fields – was also supported. More than half of the programs were devoted to MINT disciplines and just over a quarter in the De Wordsmith disciplines. Post-graduate careers in mints and finance have continued this trend. In total, almost a quarter of all participants worked in IT and almost a third in Group S increased to almost one-third. Indeed, the probability that participants studied mathematics and worked in the fields of computer science or banking/accounting was significantly higher than in the other groups. In addition, the code “IRE” (z.B computer science, mathematics, engineering, chemistry) in data collection through the prism of RIASEC encoding has been greatly prized and developed with expertise: clearly in the field of education. Educational coding and professional coding as a whole have shown a very strong propensity for the RIASEC “I” code, generally considered analytical, scientific, scientific and research-oriented individuals; This was also particularly important for S-Solver. A comparison with U.S. labour market standards showed that the level of employment in the “I” occupations of the survey population was four times higher than expected.
Finally, the Solvers also opted in their spare time for leisure activities, which were put forward on intellectually stimulating activities (“I”); and Sudoku`s logical challenge (RIASEC code “IC,” 9.8% hobby responses) was more popular than puzzles, languages and writing (RIASEC code “AI,” 7.3% of hobby responses). It is interesting to note that the game of Scrabble and the resolution of non-cryptic crossword puzzles were relatively unpopular pastimes. Cryptic crossword puzzles usually consist of two elements: a straight definition and cryptic instructions to assemble the necessary solution – the “word game” (box 1). It is not always easy to say what the point of what purpose is, and there is often no clear separation between the two parties (Schulman, 1996; Greer, 2001; Aarons, 2012; Sutherland, 2012; Manley, 2014). In addition, the setter can frame the surface reading of the index as a perfectly plausible but misleading sentence, and thus deliberately capture the careless solver in a “red herring” based on the linguistic ambiguities inherent in English (Aarons, 2012).