This paper attempts to provide a detailed overview of the latest advancements in the field of game difficulty adaptation and experience personalization within the years 2005–2021 from the perspective of dynamic difficulty adjustment and procedural content generation. To this end, a systematic review process was followed in order to search in the database Scopus for relevant work within the addressed research scope. This process resulted in 109 relevant papers, which in turn were taxonomized according to the type of adaptation/personalization approach they employ in order to tailor games to each individual player. The aim of this survey is to address research concepts and problems that are related to the dynamic modeling of the players’ characteristics, the impact of adaptive games on their experience and the dissemination of the relevant research. Findings of this review revealed that the adaptive and personalized games can improve and enhance the experience of the players/students and the rehabilitation of patients through the dynamic modeling of distinct personality characteristics, e.g., personal interests and exhibited emotions.

The aim of this paper is to survey recent research publications that use Soft Computing methods to answer education-related problems based on the analysis of educational data ‘mined’ mainly from interactive/e-learning systems. Such systems are known to generate and store large volumes of data that can be exploited to assess the learner, the system and the quality of the interaction between them. Educational Data Mining (EDM) and Learning Analytics (LA) are two distinct and yet closely related research areas that focus on this data aiming to address open education-related questions or issues. Besides ‘classic’ data analysis methods such as clustering, classification, identification or regression/analysis of variances, soft computing methods are often employed by EDM and LA researchers to achieve their various tasks. Their very nature as iterative optimization algorithms that avoid the exhaustive search of the solutions space and go for possibly suboptimal solutions yet at realistic time and effort, along with their heavy reliance on rich data sets for training, make soft computing methods ideal tools for the EDM or LA type of problems. Decision trees, random forests, artificial neural networks, fuzzy logic, support vector machines and genetic/evolutionary algorithms are a few examples of soft computing approaches that, given enough data, can successfully deal with uncertainty, qualitatively stated problems and incomplete, imprecise or even contradictory data sets – features that the field of education shares with all humanities/social sciences fields. The present review focuses, therefore, on recent EDM and LA research that employs at least one soft computing method, and aims to identify (i) the major education problems/issues addressed and, consequently, research goals/objectives set, (ii) the learning contexts/settings within which relevant research and educational interventions take place, (iii) the relation between classic and soft computing methods employed to solve specific problems/issues, and (iv) the means of dissemination (publication journals) of the relevant research results. Selection and analysis of a body of 300 journal publications reveals that top research questions in education today seeking answers through soft computing methods refer directly to the issue of quality – a critical issue given the currently dominant educational/pedagogical models that favor e-learning or computer- or technology-mediated learning contexts. Moreover, results identify the most frequently used methods and tools within EDM/LA research and, comparatively, within their soft computing subsets, along with the major journals relevant research is being published worldwide. Weaknesses and issues that need further attention in order to fully exploit the benefits of research results to improve both the learning experience and the learning outcomes are discussed in the conclusions.