Teachers can benefit from the information provided by learning analytics data for multiple purposes. Visual learning analytics dashboards provide near real-time information while more complex offline tools are commonly used to synthesize and transform the data gathered into interpretable information for teachers. The extended use of Learning Management Systems in universities, such as Moodle or Canvas, provides a rich environment to capture learning analytics data from students' interactions while they are progressing in their courses. In this paper, we present two different learning analytics tools aimed at teachers to obtain information about students' progress and results using data from the Moodle LMS at different stages of their learning process: (1) a progress visualization plugin for Moodle, which provides teachers with real-time information about the progress achieved by students in their courses, and the different goals set for their plans; and (2) an analytics Jupyter Notebook tool with a pre-defined set of analysis and visualizations to apply to data gathered from default activities in Moodle. The plugin is in an initial validation stage, while the analysis tool has been tested in a case study in a university course. Combined, both contributions can enrich the information that teachers have during and after the academic year, adapting their classes to better fit students' progress and needs, as well as providing overall results and comparison between groups after the course has finished.

Recognition of learning techniques such as badges and micro credentials are broadly used in education. Both LMSs and MOOCs incorporate these techniques to inform learners of their achievements. This meta-review study aims to provide an overview of recognition of learning in both LMSs and MOOCs, by gathering previous literature reviews and overview studies in this field. The studies reviewed show multiple applications, mainly using badges and gamification in MOOCs. Results of the studies have been broadly positive and, together with the recommendations and lessons learned in previous research, encourage the future research in recognition of learning.

Data science applications in education are quickly proliferating, partially due to the use of LMSs and MOOCs. However, the application of data science techniques in the validation and deployment of serious games is still scarce. Among other reasons, obtaining and communicating useful information from the varied interaction data captured from serious games requires specific data analysis and visualization techniques that are out of reach of most non-experts. To mitigate this lack of application of data science techniques in the field of serious games, we present T-Mon, a monitor of traces for the xAPI-SG standard. T-Mon offers a default set of analysis and visualizations for serious game interaction data that follows this standard, with no other configuration required. The information reported by T-Mon provides an overview of the game interaction data collected, bringing analysis and visualizations closer to non-experts and simplifying the application of serious games.

Serious games have proven several advantages when used in education improving students learning. However, games are still complex to deploy in the class for average teachers. Many teachers still do not see games as a powerful tool to improve their teaching work. To this end, it is essential to humanize the game technology making the use of games more transparent to teachers in a way that they get the benefits and avoid most of the game deployment complexity. We consider that Game Learning Analytics is one of the keys to help teachers in the application of serious games in the classrooms. Game Learning Analytics allows to capture data from students' interactions with games and derive information that simplify teachers' tasks. Doing it in a transparent way within the game environment (i.e. stealth assessment) can provide evidence-based data about the learners' knowledge at each point of time. Combining both game learning analytics in near real-time and offline, and stealth assessment for games, it could be possible to leverage their use in classroom settings at real-time making their use easier for teachers.

Serious games validation is a highly complex and burdensome process. To ensure that games meet their intended educational goals, it is necessary to have a clear experimental design and the necessary tools to minimize the errors that may appear in the process. In this article, after describing the most common problems that we have found while validating our own games, we present Simva, a tool designed to simplify the process of validating serious games with formal questionnaires and relating them with learning analytics data, reducing time, cost, and error rates.

Game learning analytics has a great potential to provide insight and improve the use of games in different educational situations. However, it is necessary to clearly establish what the learner's requirements are and to set realistic expectations about the learning process and outcomes. Application of game learning analytics requires pedagogically informed policies that settle the learning goals and relate them to analysis and visualization; and a supporting infrastructure that provides the mechanism on top of which it is executed. Both concerns can be addressed separated: on the one hand, there is a Learning Analytics Model (LAM) which describes how the analysis is carried out, interpreted as learning, and presented to stakeholders; and on the other hand, an underlying analytics system that concentrates on performance, security, flexibility and generality. An important advantage of this separation is that it allows LAM authors to concentrate on their area of expertise, limiting their exposition to the actual mechanism used underneath. However, LAMs built for a single game fail to account for the frequent case where games and their analytics are aggregated into larger, overarching plots, games or courses. This work describes an extension to an existing game learning analytics system, used in RAGE and BEACONING H2020 projects, which manages multilevel analytics through improvements to both policy and mechanism; and introduces meta-Learning Analytic Models, which characterize learning in hierarchical structures.

Applying games in education provides multiple benefits clearly visible in entertainment games: their engaging, goal-oriented nature encourages students to improve while they play. Educational games, also known as Serious Games (SGs) are video games designed with a main purpose other than pure entertainment; their main purpose may be to teach, to change an attitude or behavior, or to create awareness of a certain issue. As educators and game developers, the validity and effectiveness of these games towards their defined educational purposes needs to be both measurable and measured. Fortunately, the highly interactive nature of games makes the application of Learning Analytics (LA) perfect to capture students' interaction data with the purpose of better understanding or improving the learning process. However, there is a lack of widely adopted standards to communicate information between games and their tracking modules. Game Learning Analytics (GLA) combines the educational goals of LA with technologies that are commonplace in Game Analytics (GA), and also suffers from a lack of standards adoption that would facilitate its use across different SGs. In this paper, we describe two key steps towards the systematization of GLA: 1), the use of a newly-proposed standard tracking model to exchange information between the SG and the analytics platform, allowing reusable tracker components to be developed for each game engine or development platform; and 2), the use of standardized analysis and visualization assets to provide general but useful information for any SG that sends its data in the aforementioned format. These analysis and visualizations can be further customized and adapted for particular games when needed. We examine the use of this complete standard model in the GLA system currently under development for use in two EU H2020 SG projects.