Technology has become an essential part of our everyday life, and its use in educational environments keeps growing. In addition, games are one of the most popular activities across cultures and ages, and there is ample evidence that supports the benefits of using games for assessment. This field is commonly known as game-based assessment (GBA), which refers to the use of games to assess learners' competencies, skills, or knowledge. This paper analyzes the current status of the GBA field by performing the first systematic literature review on empirical GBA studies. It is based on 65 research papers that used digital GBAs to determine: (1) the context where the study has been applied; (2) the primary purpose; (3) the domain of the game used; (4) game/tool availability; (5) the size of the data sample; (6) the computational methods and algorithms applied; (7) the targeted stakeholders of the study; and (8) what limitations and challenges are reported by authors. Based on the categories established and our analysis, the findings suggest that GBAs are mainly used in K-16 education and for assessment purposes, and that most GBAs focus on assessing STEM content, and cognitive and soft skills. Furthermore, the current limitations indicate that future GBA research would benefit from the use of bigger data samples and more specialized algorithms. Based on our results, we discuss current trends in the field and open challenges (including replication and validation problems), providing recommendations for the future research agenda of the GBA field.

Technology has become an integral part of our everyday life, and its use in educational environments keeps growing. Additionally, video games are one of the most popular mediums across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment, and educators are mainly supportive of using games in classrooms. However, we do not usually find educational games within the classroom activities. One of the main problems is that teachers report difficulties to actually know how their students are using the game so that they can analyze properly the effect of the activity and the interaction of students. To support teachers, educational games should incorporate learning analytics to transform data generated by students when playing useful information in a friendly and understandable way. For this work, we build upon Shadowspect, a 3D geometry puzzle game that has been used by teachers in a group of schools in the US. We use learning analytics techniques to generate a set of metrics implemented in a live dashboard that aims to facilitate that teachers can understand students' interaction with Shadowspect. We depict the multidisciplinary design process that we have followed to generate the metrics and the dashboard with great detail. Finally, we also provide uses cases that exemplify how teachers can use the dashboard to understand the global progress of their class and each of their students at an individual level, in order to intervene, adapt their classes and provide personalize feedback when appropriate.

Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader adoption in classrooms is the lack of actionable assessment data, the fact that teachers often do not have a clear sense of how students are interacting with the game, and it is unclear if the gameplay is leading to productive learning. To address this gap, we seek to provide sequence and process mining metrics to teachers that are easily interpretable and actionable. More specifically, we build our work on top of Shadowspect, a three-dimensional geometry game that has been developed to measure geometry skills as well other cognitive and noncognitive skills. We use data from its implementation across schools in the U.S. to implement two sequence and process mining metrics in an interactive dashboard for teachers. The final objective is to facilitate that teachers can understand the sequence of actions and common errors of students using Shadowspect so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate.

El presente estudio analiza el Electronic Word Of Mouth (eWOM) en el sector turísticoa través de TripAdvisor y Booking.com. Este trabajo pretende comprobar si las críticas sobrela falta de veracidad de los comentarios de TripAdvisor están fundamentadas, comparando losrankings de hoteles en cinco ciudades españolas con los del portal de reservas Booking.com.Los resultados revelan que existe una correlación entre ambos rankings, indicando asíque la falsificación y la manipulación de opiniones en TripAdvisor no es característica aligual que se constata que a mayor categoría hotelera mejores posiciones en el ranking y quela cantidad de comentarios influye en el ranking.

Nowadays, the use of technology in continuously increasing, making a significant impact in almost every area, including education. New areas have gained much popularity in the last years in educational technology (EdTech), such as Massive Open Online Courses (MOOCs) or computer-supported collaborative learning. In addition, research and interest in this area have also been growing over the years. The quantity of research and scientific publications in EdTech is constantly increasing, and trying to analyze and extract information from a set of research papers is often a very time-consuming task. To make this process easier and solve these limitations, we present Fontana, a framework that can quickly perform trend and social network analysis using any corpus of documents and its metadata. Specifically, the framework can: 1) Discover the latest trends given any corpus of documents, using Natural Language Processing (NLP) analysis and keywords (bibliometric approach); 2) Discover the evolution of the trends previously identified over the years; 3) Discover the primary authors and papers, along with hidden relationships between existing communities. To test its functionality, we evaluated the framework using a corpus of papers from the EdTech research field. We also followed an open science methodology making the entire framework available in Open Science Framework (OSF) easy to access and use. The case study successfully proved the capabilities of the framework, revealing some of the most frequent topics in the area, such as “EDM,” “learning analytics,” or “collaborative learning.” We expect our work to help identifying trends and patterns in the EdTech area, using natural language processing and social network analysis to objectively process large amounts of research.