An implementation architecture for scenario-based simulations

The past years have witnessed an increased use of applied games for developing and evaluating communication skills. These skills benefit from in-terpersonal interactions. Providing feedback to students practicing communica-tion skills is difficult in a traditional class setting with one teacher and many students. This logistic challenge may be partly overcome by providing training using a simulation in which a student practices with communication scenarios. A scenario is a description of a series of interactions, where at each step the player is faced with a choice. We have developed a scenario editor that enables teachers to develop scenarios for practicing communication skills. A teacher can develop a scenario without knowledge of the implementation. This paper presents the implementation architecture for such a scenario-based simulation.

D5.5 – First Version Scenario Arrangement Document – round 1

This deliverable outlines the implementation plan for each of the first-round studies of the RAGE pilots. The main goal of these pilots is to perform a small-scale test of the RAGE games with end-users and intermediary stakeholders in five different non-leisure domains to guide the further development of the games for the final validation studies. At the same time the pilots implement the pre-testing of the research instruments and methodology for answering the main evaluation questions in the five areas of investigation identified in D8.1: 1) usability, 2) game experience, 3) learning effectiveness, 4) transfer effect and 5) pedagogical costs and benefits. Finally, the pilots are aimed at collecting preliminary results for a first formative evaluation of the games and game technologies, with the goal of feeding back useful information to development for the final versions of games and assets. The results of the first pilot will be compared with the results of the final evaluation studies to demonstrate improvements of the game and game effects from first to final version. A revision of the deliverable will be done in the next few months to produce the final arrangement document (D5.1, due at M21).

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.

D5.1 – Scenario Arrangement Document – round 1

This deliverable is a confirmation and update of 'D5.5 - First Version Scenario Arrangement Document – round 1' after a revisions with game developers. It outlines the implementation plan for each of the first-round studies of the RAGE pilots. The main goal of these pilots is to perform a small-scale test of the RAGE games with end-users and intermediary stakeholders in five different non-leisure domains to guide the further development of the games for the final validation studies.