# Evaluating Learner Engagement with Gamification in Online Courses
###### Abstract
Additionally, the literature presents gamified experiences that mostly adopt the one-fits-all approach [13], that is, an approach that is standard and not tailored to individual needs, considering thus that all users have the same gaming profile. In contrast, adaptive gamification has emerged as a means for adapting the gamified experience to user profiles [14; 15]. These adaptive approaches use a _static_ profile of the player, which is computed at the beginning of the experience through the use of a player type questionnaire. Then, the gamified system presents the users with game elements that fit their initial (static) playing profile. In a previous research paper [16], we presented an adaptive gamification method that took this a step further, considering that the results of the player type questionnaire can be imprecise and also that player profiles can be dynamic. Thus, our method takes player profiles as initial information but also considers how these profiles change over time based on users' interactions and opinions. Then, the users are provided with a personalized experience through the use of game elements that correspond to their _dynamic_ playing profile. In [17], we presented a case study on the nanoMOOCs educational platform that provides the users with gamified knowledge pills, and performed a preliminary evaluation of the approach using a simulator with bots. In this paper, we experimentally evaluate the contribution of the dynamic adaptive approach to participants' experience with gamification. Concretely, we want to know if our method has an impact on the hedonic dimension of user experience (UX). To do so, we compared Dynamic Adaptive Gamification (DynamicAG) and Static Adaptive Gamification (StaticAG) in terms of the metrics that help us to measure user engagement with gamification. Note that this study is concerned with how students experienced (felt about) the gamification, i.e. : to what extent did they interact with the game elements that appear throughout the gamified course? Did students like the look and feel of the game elements? Therefore, the study of the impact of gamification on course completion and engagement with educational activities are beyond the scope of this study. We evaluated our method with 63 students at schools in the city of Viladecans, Barcelona (Spain). Our results show that the dynamic adaptive gamification group interacted significantly more than the group in the static adaptive gamification condition, with an increase of 40% in the mean number of interactions with game elements. Moreover, the dynamic adaptive strategy revealed a broader range of game elements to the users. Since the game elements were the same in both conditions, as expected, students in both groups had almost equal opinions regarding the elements' look and feel. The rest of the paper is structured as follows. The related work section reviews research works that aimed at providing the users with gamified personalized learning experiences. Next, we describe two adaptive gamification strategies, one that considers the profile of the player, but using a static player profile throughout the course, and our approach, which considers this profile as dynamic. The next section details the strategy followed to situate the game elements along the activities in the course. Then, the rewards logic defines the integration of students' progress in the MOOC in terms of the gamification element rewards. The Materials and Methods section describes the details of the experiment to compare the StaticAG and DynamicAG approaches. The following sections present metrics, analyse the data gathered, and reflect upon the findings and limitations of our research. The last section provides conclusions drawn from this research and establishes directions for future work.