ReaderBench goes Online: A Comprehension-Centered Framework for Educational Purposes

In this paper we introduce the online version of our ReaderBench framework, which includes multi-lingual comprehension-centered web services designed to address a wide range of individual and collaborative learning scenarios, as follows. First, students can be engaged in reading a course material, then eliciting their understanding of it; the reading strategies component provides an in-depth perspective of comprehension processes. Second, students can write an essay or a summary; the automated essay grading component provides them access to more than 200 textual complexity indices covering lexical, syntax, semantics and discourse structure measurements. Third, students can start discussing in a chat or a forum; the Computer Supported Collaborative Learning (CSCL) component provides indepth conversation analysis in terms of evaluating each member’s involvement in the CSCL environments. Eventually, the sentiment analysis, as well as the semantic models and topic mining components enable a clearer perspective in terms of learner’s points of view and of underlying interests.

Making sense of game based user data: learning analytics in applied games

Digital learning games are useful educational tools with high motivational potential. With the application of games for instruction there comes the need of acknowledging learning game experiences also in the context of educational assessment. Learning analytics provides new opportunities for supporting assessment in and of educational games. We give an overview of current learning analytics methods in this field and reflect on existing challenges. An approach of providing reusable software assets for interaction assessment and evaluation in games is presented. This is part of a broader initiative of making available advanced methodologies and tools for supporting applied game development.

Amplifying applied game development and uptake

The established (digital) leisure game industry is historically one dominated by large international hardware vendors (e.g. Sony, Microsoft and Nintendo), major publishers and supported by a complex network of development studios, distributors and retailers. New modes of digital distribution and development practice are challenging this business model and the leisure games industry landscape is one experiencing rapid change. The established (digital) leisure games industry, at least anecdotally, appears reluctant to participate actively in the applied games sector (Stewart et al., 2013). There are a number of potential explanations as to why this may indeed be the case including ; A concentration on large-scale consolidation of their (proprietary) platforms, content, entertainment brand and credibility which arguably could be weakened by association with the conflicting notion of purposefulness (in applied games) in market niches without clear business models or quantifiable returns on investment. In contrast, the applied games industry exhibits the characteristics of an emerging, immature industry namely: weak interconnectedness, limited knowledge exchange, an absence of harmonising standards, limited specialisations, limited division of labour and arguably insufficient evidence of the products efficacies (Stewart et al., 2013; Garcia Sanchez, 2013) and could, arguably, be characterised as a dysfunctional market. To test these assertions the Realising an Applied Gaming Ecosystem (RAGE) project will develop a number of self contained gaming assets to be actively employed in the creation of a number of applied games to be implemented and evaluated as regional pilots across a variety of European educational, training and vocational contexts. RAGE is a European Commission Horizon 2020 project with twenty (pan European) partners from industry, research and education with the aim of developing, transforming and enriching advanced technologies from the leisure games industry into self-contained gaming assets (i.e. solutions showing economic value potential) that could support a variety of stakeholders including teachers, students, and, significantly, game studios interested in developing applied games. RAGE will provide these assets together with a large quantity of high-quality knowledge resources through a self-sustainable Ecosystem, a social space that connects research, the gaming industries, intermediaries, education providers, policy makers and end-users in order to stimulate the development and application of applied games in educational, training and vocational contexts. The authors identify barriers (real and perceived) and opportunities facing stakeholders in engaging, exploring new emergent business models ,developing, establishing and sustaining an applied gaming eco system in Europe.

Chapter 2: Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis

Nistor, N., Dascalu, M., Stavarache, L.L., Tarnai, C., & Trausan-Matu, S. (2015). Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis. In Y. Li, M. Chang, M. Kravcik, E. Popescu, R. Huang, Kinshuk & N.-S. Chen (Eds.), State-of-the-Art and Future Directions of Smart Learning (Vol. Lecture Notes in Educational Technology, pp. 13–17). Berlin, Germany: Springer-Verlag Singapur

Newcomer Integration in Online Knowledge Communities: Exploring the Role of Dialogic Textual Complexity

Using online knowledge communities (OKCs) as informal learning environments poses the question how likely these will integrate newcomers as peripheral participants. Previous research has identified surface characteristics of the OKC dialog as integrativity predictors. Yet, little is known about the role of dialogic textual complexity. This contribution proposes a comprehensive approach based on previously validated textual complexity indexes and applies it to predict OKC integrativity. The dialog analysis of N = 14 blogger communities with a total of 1937 participants identified three main components of textual complexity: dialog participation, structure and cohesion. From these, dialog cohesion was higher in integrative OKCs, thus significantly predicting OKC integrativity. This result adds to previous OKC research by uncovering the depth of OKC discourse. For educational practice, the study suggests a way of empowering learners by automatically assessing the integrativity of OKCs in which they may attempt to participate and access community knowledge.

D3.3 – First Social Gamification Assets

This deliverable is software, as such this document is abridged to be as succinct as possible, the extended descriptions and detailed documentation for the software are online. The document consists of two parts, part one describes the first bundle of social gamification assets developed in WP3, part two presents mock-ups of the RAGE ecosystem gamification. In addition to the software outline, included in part one is a short market analysis of existing gamification solutions, outline rationale for combining the three social gamification assets into one unified asset, and the branding exercise to make the assets more developer friendly.Online links to the source code, binaries, demo and documentation for the assets are provided. The combined assets offer game developers as well as a wide range of software developers the opportunity to readily enhance existing games or digital platforms with multiplayer gamification functionalities, catering for both competitive and cooperative game dynamics. The solution consist of a flexible client-server solution which can run either as a cloud-based service, serving many games or have specific instances for individual games as necessary.

Designing Massive Open Online Learning Processes: The importance of the social element

This is a pre-print for personal use only. Please refer to the Springer website for the official, published version http://www.springer.com/978-3-662-52923-2

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our data analytics tool based on k-means clustering and on SSE, silhouette, Dunn index and Xi-Beni index quality metrics. Experiments performed on a dataset obtained from our online e-learning platform show that the extracted interaction patterns were representative in classifying learners. Furthermore, the performed monitoring activities created a strong basis for generating automatic feedback to learners in terms of their course participation, while relying on their previous performance. In addition, our analysis introduces automatic triggers that highlight learners who will potentially fail the course, enabling tutors to take timely actions.

Integrating Scientific Publication into an Applied Gaming Ecosystem

Salman, M. et al. (2016). Integrating Scientific Publication into an Applied Gaming Ecosystem. GSTF Journal on Computing (JoC), Volume 5 (Issue 1), pp. 45-51.

Game Learning Analytics: Learning Analytics for Serious Games

Video games have become one of the largest entertainment industries, and their power to capture the attention of players worldwide soon prompted the idea of using games to improve education. However, these educational games, commonly referred to as serious games, face different challenges when brought into the classroom, ranging from pragmatic issues (e.g. a high development cost) to deeper educational issues, including a lack of understanding of how the students interact with the games and how the learning process actually occurs. This chapter explores the potential of data-driven approaches to improve the practical applicability of serious games. Existing work done by the entertainment and learning industries helps to build a conceptual model of the tasks required to analyze player interactions in serious games (gaming learning analytics or GLA). The chapter also describes the main ongoing initiatives to create reference GLA infrastructures and their connection to new emerging specifications from the educational technology field. Finally, it explores how this data-driven GLA will help in the development of a new generation of more effective educational games and new business models that will support their expansion. This results in additional ethical implications, which are discussed at the end of the chapter.