Hub Kurvers

EMERGO: A methodology and toolkit for developing serious games in higher education

Societal changes demand educators to apply new pedagogical approaches. Many educational stakeholders feel that serious games could play a key role in fulfilling this demand, and they lick their chops when looking at the booming industry of leisure games. However, current toolkits for developing leisure games show severe shortcomings when applied to serious games. Developing effective serious games in an efficient way requires a specific approach and tool set. This article describes the EMERGO methodology and generic toolkit for developing and delivering scenario-based serious games that are aimed at the acquisition of complex cognitive skills in higher education. Preliminary evaluation results with case developers using the EMERGO methodology and toolkit and with learners using EMERGO cases are presented.

Encouraging contributions in learning networks using incentive mechanisms

We investigate incentive mechanisms to increase active participation in Learning Networks (LNs). The LN under study is LN4LD, an LN for the exchange of information about the IMS Learning Design specification. We examine how to encourage learners in LN4LD to contribute their knowledge, and whether incentive mechanisms can increase the level of active participation. We describe an incentive mechanism based on constructivist principles and Social Exchange Theory, and experimentation using the mechanism designed to increase the level of active participation. The incentive mechanism allows individual learners to gain personal access to additional information through the accumulation of points earned by making contributions. Repeated measurements according to a simple interrupted time series with removal design show that the level of participation was indeed increased by the introduction of the reward system. It can therefore be considered worthwhile to use incentive mechanisms in LNs.

Scripted collaboration in serious gaming for complex learning: Effects of multiple perspectives when acquiring water management skills

Hummel, H. G. K., Van Houcke, J., Nadolski, R. J., Van der Hiele, T., Kurvers, H., & Lohr, A. (2011). Scripted collaboration in gaming for complex learning: Effects of multiple perspectives when acquiring water management skills. British Journal of Educational Technology, 42(6), 1029-1041. doi:10.1111/j.1467-8535.2010.01122.x

Swarm-based sequencing recommendations in e-learning

Open and distance learning (ODL) gives learners freedom of time, place and pace of study, putting learner self-direction centre-stage. However, increased responsibility should not come at the price of over-burdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to recommending the sequencing of e-learning modules for distance learners based on self-organization theory. It describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The article includes initial results from a large-scale experiment designed to validate the approach.

Facilitating Participation: From the EML Web Site to the Learning Network for Learning Design

This article investigates conditions for increasing active participation in on-line communities. As a case study, we use three generations of facilities designed to promote learning in the area of Educational Modelling Languages. Following a description of early experience with a conventional web site and with a community site offering facilities for collaboration, we describe a pilot implementation of a Learning Network. Preliminary participation data (both passive and active) is reported, together with lessons learned while setting up the pilot. Early experiences reveal that clear policies, usability and reward systems are of importance when facilitating a Learning Network. Our “lessons learned” are phrased in terms of recommendations which will be used to guide subsequent Learning Network implementations.

Computer-Supported Training of Psycho-Diagnostic Skills.

This paper outlines and discusses a self-contained multimedia-training program on psychological diagnostics. The program aims to play an intermediate role between theory and working practice, while offering students a simulated environment to prepare for practical work with real humans. The program simulates the entire diagnostic cycle in convincing, authentic cases while offering extended support facilities. Design principles of the program strongly rest on theories of constructivism and experiential learning; it combines freedom of movement for the students to allow explorative learning strategies, while it offers extended and intelligent feedback facilities that comment on the students’ performances. The functional program design is presented and substantiated from a pedagogical perspective. Preliminary results of a survey amongst students are presented and discussed.

ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language

Automated Essay Scoring has gained a wider applicability and usage with the integration of advanced Natural Language Processing techniques which enabled in-depth analyses of discourse in order capture the specificities of written texts. In this paper, we introduce a novel Automatic Essay Scoring method for Dutch language, built within the Readerbench framework, which encompasses a wide range of textual complexity indices, as well as an automated segmentation approach. Our method was evaluated on a corpus of 173 technical reports automatically split into sections and subsections, thus forming a hierarchical structure on which textual complexity indices were subsequently applied. The stepwise regression model explained 30.5% of the variance in students’ scores, while a Discriminant Function Analysis predicted with substantial accuracy (75.1%) whether they are high or low performance students.

Automated essay scoring in applied games: Reducing the teacher bandwidth problem in online training

Abstract This paper presents a methodology for applying automated essay scoring in educational settings. The methodology was tested and validated on a dataset of 173 reports (in Dutch language) that students have created in an applied game on environmental policy. Natural Language Processing technologies from the ReaderBench framework were used to generate an extensive set of textual complexity indices for each of the reports. Afterwards, different machine learning algorithms were used to predict the scores. By combining binary classification (pass or fail) and a probabilistic model for Precision, a trade-off can be made between validity of automated score prediction (Precision) and the reduction of teacher workload required for manual assessment. It was found from the sample that substantial workload reduction can be achieved, while preserving high precision: allowing for a precision of 95% or higher would already reduce the teachers workload to 74%; lowering precision to 80% produces a workload reduction of 50%.