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Flow experience is one of the most ambitious targets of any user interface designer. However, it has remained elusive to evaluate how well user interfaces give rise to flow experience outside conducting invasive self-reporting-based questionnaires, which remove the users from the flow experience and can't be massively applied. At the same time, otherwise, well-built systems do track the behaviour of users on the interface, and therefore, user behaviour data could act as a reliable proxy for assessing the experience of users. Currently, there is little empirical research or data about which indices of user behaviours might correspond with having a flow experience as well as the different psychological constituents of the flow experience. Therefore, facing the challenge of using users' behaviour data to model users' experience, we investigated the associations between users' behaviour data (e.g. mouse clicks, activity time in the system, and average response time) and their self-reported flow experience by using data mining (i.e. associations rules) analysing data from 204 subjects. Results demonstrate that the speed of users' actions negatively affects the flow experience antecedents while also positively affecting the loss of self-consciousness. Our study advances the literature, providing insights to identify users' flow experience through behaviour data. [ABSTRACT FROM AUTHOR]

The flow experience (i.e., challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, concentration, sense of control, loss of self-consciousness, transformation of time, and "autotelic" experience) is an experience highly related to the learning experience. One of the current challenges is to identify whether students are managing to achieve this experience in educational systems. The methods currently used to identify students' flow experience are based on self-reports or equipment (e.g., eye trackers or electroencephalograms). The main problem with these methods is the high cost of the equipment and the impossibility of applying them massively. To address this challenge, we used behavior data logs produced by students during the use of a gamified educational system to predict the students' flow experience. Through a data-driven study (N = 23) using structural equation modeling, we identified possibilities to predict the students' flow experience through the speed of students' actions. With this initial study, we advance the literature, especially contributing to the field of student experience analysis, by bringing insights showing how to step towards automatic students' flow experience identification in gamified educational systems.

Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students' performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine-grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016-18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine-grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices.

One of the main goals of gamification in educational settings is to increase student motivation and engagement. To facilitate the design of gamified educational systems, in recent years, studies have proposed various approaches (e.g., methodologies, frameworks and models). One of the main problems, however, is that most of these approaches are theoretical, and do not provide a "proof-of-concept." This paper advances the state of the art by providing a practical way to help implement this kind of system. In this study, we present, for the first time, how one can apply gamification elements in a learning system using the "Design Sprint" method, to guide designers and developers on replicating this process. Additionally, as starting point, we use a taxonomy composed of 21 game elements, proposed to be used within learning environments, organised into five game element categories, according to their goal/usage. Our main contribution is to present "how to systematically implement the gamification elements focused on educational ends," which is of special value to practitioners, designers and developers.

Interactive or Dynamic Geometry System (DGS) is a tool that help to teach and learn geometry using a computer-based interactive environment. Traditionally, the interaction with DGS is based on keyboard and mouse events where the functionalities are accessed using a menu of icons. Nevertheless, recent findings suggest that such a traditional model of interaction has a steep learning curve and is inadequate to develop DGS for devices with multi-touch screens. Thus, we propose a new interaction model for DGS based on a gesture dictionary which enables the construction and manipulation of geometric objects without the need of accessing a menu of icons. The dictionary is divided into three types: (i) kernel gestures: which are the basis for defining gestures; (ii) navigation gestures: related to the manipulation and editing of geometric objects; and (iii) basic gestures of construction: to construct geometric objects. To validate our interaction model, a DGS for mobile device, GeoTouch, has been developed using the proposed dictionary. Usability tests were performed comparing GeoTouch with three other DGS for mobile devices. The results indicate that the GeoTouch interface has fewer usability problems than the other three DGS software and it is easier to learn and interact with.