Gamification has been widely used to design better educational systems aiming to increase students’ concentration, motivation, engagement, flow experience, and others positive experiences. With advances in research on gamification in education, over the past few years, many studies have highlighted the need to tailor the gamification design properties to match individual students’ needs, characteristics and preferences. Thus, different studies have been conducted to personalize the gamification in education. However, the results are still contradictory and need to be better understood to advance this field. To provide a complete understanding of this research domain, we conducted a systematic literature review to summarize the results and discussions on studies that cover the field of tailored gamified education. Following a systematic process, we analysed 2108 studies and identified 19 studies to answer our research questions. The results indicate that most of the studies only consider students’ gamer types to tailor the systems, and most of the experiments do not provide sufficient statistical evidence, especially regarding learning performance using tailored gamified systems. Based on the results, we also provided an agenda with different challenges, opportunities, and research directions to improve the literature on tailored gamification in education. Our study contributes to the field of gamification design in education.

Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design and analysis of gamified strategies. This paper analysed the game elements employed in gamified learning environments through a previously proposed and evaluated taxonomy while detailing and expanding this taxonomy. In the current paper, we describe our taxonomy in-depth as well as expand it. Our new structured results demonstrate an extension of the proposed taxonomy which results from this process, is divided into five dimensions, related to the learner and the learning environment. Our main contribution is the detailed taxonomy that can be used to design and evaluate gamification design in learning environments.

Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design of gamified strategies. This paper aims to create a taxonomy for the game elements, based on gamification experts' opinions. After a brief review from existing work, we extract first the game elements from the current state of the art, and then evaluate them via a survey with 19 gamification and education experts. The resulting taxonomy taxonomy included the description of 21 game elements and their quantitative and qualitative evaluation by the experts. Overall, the proposed taxonomy was in general well accepted by most of the experts. They also suggested expanding it with the inclusion of Narrative and Storytelling game elements. Thus, the main contribution of this paper is proposing a new, confirmed taxonomy to standardise the terminology used to define the game elements as a mean to design and deploy gamification strategies in the educational domain.

Human locomotion is regulated by the central nervous system (CNS). The neurophysiological changes in the CNS due to amyotrophic lateral sclerosis (ALS) may cause altered gait cycle duration (stride interval) or other gait rhythm. This article used a statistical method to analyze the altered stride interval in patients with ALS. We first estimated the probability density functions (PDFs) of stride interval from the outlier-processed gait rhythm time series, by using the nonparametric Parzen-window approach. Based on the PDFs estimated, the mean of the left-foot stride interval and the modified Kullback-Leibler divergence (MKLD) can be computed to serve as dominant features. In the classification experiments, the least squares support vector machine (LS-SVM) with Gaussian kernels was applied to distinguish the stride patterns in ALS patients. According to the results obtained with the stride interval time series recorded from 16 healthy control subjects and 13 patients with ALS, the key findings of the present study are summarized as follows. (1) It is observed that the mean of stride interval computed based on the PDF for the left foot is correlated with that for the right foot in patients with ALS. (2) The MKLD parameter of the gait in ALS is significantly different from that in healthy controls. (3) The diagnostic performance of the nonlinear LS-SVM, evaluated by the leave-one-out cross-validation method, is superior to that obtained by the linear discriminant analysis. The LS-SVM can effectively separate the stride patterns between the groups of healthy controls and ALS patients with an overall accurate rate of 82.8% and an area of 0.869 under the receiver operating characteristic curve.