Johannes Konert

An authoring tool for adaptive digital educational games

Digital educational games, especially those equipped with adaptive features for reacting to individual characteristics of players, require heterogeneous teams. This increases costs incurred by coordination and communication overhead. Simultaneously, typical educational games have smaller budgets than normal entertainment games. In order to address this challenge, we present an overview of game development processes and map these processes into a concept for an authoring tool that unifies the different workflows and facilitates close collaboration in development teams. Using the tool, authors can create the structure of a game and fill it with content without relying on game programmers. For adding adaptivity to the game, the authoring tool features specific user support measures that assist the authors in the relatively novel field of creating non-linear, adaptive educational experiences. Evaluations with users recruited from actual user groups involved in game development shows the applicability of this process.

PeerLA - Assistant for Individual Learning Goals and Self-Regulation Competency Improvement in Online Learning Scenarios

While online learning is already a part of university education and didactics, not all students have the necessary self-regulation competency to really learn on their own efficiently and effectively. In classroom a teacher can take over a moderating part, set intermediate goals and give feedback to ones progress, but participants of online learning courses (e.g. in blended scenarios or Massive Open Online Courses (MOOCs)) face a higher demand of self-regulation competency. This paper presents a course and content independent assistant, PeerLA, which assists in improving self-regulation competency. PeerLA allows setting of long-term goals, breakdown into intermediate goals and keeps track of knowledge increase or time needed. A graphical feedback allows comparison of existing and aimed level of knowledge or time investments. PeerLA adds peer comparison to the visualization charts for social frame of reference. This comparison is course-wide or only with similar learners (close in goals and knowledge levels). PeerLA is implemented as a Learning Management System (LMS) plugin to support learning progress in mixed formal and informal learning scenarios. PeerLA was evaluated with 83 students in an online mathematics preparation course over four weeks. Results indicate the benefits of such a self-regulation assistance, especially for university freshmen.

Towards a social game interaction taxonomy: a social gaming approach towards peer knowledge sharing and participation in serious games

Serious Games for Learning are often designed as singleplayer, storytelling-based games. Even though immersion into the story and adaption to the players abilities are pedagogically well designed, players can have misconceptions or get stuck with game quests. Then they seek for assistance from friends or online. Accessing hints, solutions and help of others directly in the gaming context can improve the game play and learning experience. Additionally the users from Online Social Networks can be connected to the game as a valuable resource of know-how if they are provided with participation possibilities. The concept of Peer Education is valuable for teaching and assessment among peers with similar learning targets. Thus in this paper an approach towards Social Serious Games is presented. Existing Social Media interaction patterns and singleplayer, story-based game situations are brought together respecting the Interaction Mapping Patterns1:1 and 1:n. The resulting three dimensional Social Game Influence Taxonomy is presented as well as the technical implementation as a middleware to connect existing Serious Games with Online Social Networks for Peer Knowledge Sharing and participation.

Multiplayer Serious Games

This chapter covers the topic of multiplayer serious games. Multiplayer games are discussed in terms of game types and forms, genres and techniques, as well as their impact on the use of multiplayer games. Based on that, this chapter will show how different types of multiplayer genres and techniques can be used for various serious game purposes. This chapter further provides an introduction to the topic of collaborative learning and collaborative multiplayer games—and their use for game-based collaborative learning. We discuss how collaborative learning concepts are inherently used by some massive multiplayer online games, and how those concepts can be used more thoroughly by using the multiplayer paradigm for game-based collaborative learning. Further, it is shown how various multiplayer design aspects like number of players, persistency, matchmaking, interaction, or social aspects need to be considered in the design phase of a multiplayer game.

Game Adaptation and Personalization Support

Personalization and individualization of gameplay experience can be done by metrics and data, retrieved from social media platforms. Yet, such attributes can also be realized by interaction and influences directly made by connected individuals not playing at the moment, but whom are notified via social media applications’ news feeds (both aspects are visualized in Fig. 5.1). As such, befriended people from the surrounding social network can contribute content to the game-experience of the (known) player. A suitable infrastructure allows the users to be creative and generate new and unique gameplay experiences. Additionally, the infrastructure can be used to integrate an assessment of creative game solutions and solutions to open-format problems by other humans when computer algorithms cannot cope with the degree of freedom for the tasks. In this chapter game adaptation (Sect. 5.1) by social media metrics is first examined before social game interactions (Sect. 5.2) are subsequently addressed, as they appeared to be of more interest and potential for research. Details about the corresponding designed API methods can be found in Sect. A.1.

MoodlePeers: Factors Relevant in Learning Group Formation for Improved Learning Outcomes, Satisfaction and Commitment in E-Learning Scenarios Using GroupAL

High-scale and pure online learning scenarios (like MOOCs) as well as blended-learning scenarios offer great possibilities to optimize the composition of learning groups working together on the assigned (or selected) tasks. While the benefits and importance of peer learning for deep learning and improvement of e.g. problem-solving competency and social skills are indisputable, little evidences exist about the relevant factors for group formation and their combination to optimize the learning outcome for all participants (in all groups). Based on the GroupAL algorithm, MoodlePeers proposes an plugin solution for Moodle. Evaluated in a four-week online university mathematics preparation course MoodlePeers proved significant differences in submission rate of homework, quality of homework, keeping up, and satisfaction with group work compared to randomly created groups. The significant factors from personality traits, motivation and team orientation are discussed as well as the algorithmic key functionality behind.

GroupAL: ein Algorithmus zur Formation und Qualitätsbewertung von Lerngruppen in E-Learning-Szenarien / GroupAL: an algorithm for group formation and quality evaluation of learning groups in e-learning scenarios

Zusammenfassung Der Wissensaustausch Lernender untereinander ist für E-Learning-Systeme und computer-gestütztes Lernen generell ein wichtiger Baustein zur Förderung der Motivation, der Lernzielerreichung sowie der Verbesserung der Problemlösekompetenz. Die positiven Effekte dieses Austausches hängen jedoch stark von der Eignung der Lernpartner in einer gebildeten Lerngruppe ab. In diesem Artikel werden Kriterienkategorien vorgestellt, die ein Gruppenformationsalgorithmus für Lerngruppen berücksichtigen sollte, sowie die existierenden algorithmischen Lösungen verwandter Arbeiten. Für die gleichzeitige Berücksichtigung aller dieser Kriterien wird der Algorithmus GroupAL vorgestellt. Dieser erlaubt beispielsweise die Verwendung mehrdimensionaler Kriterien, die wahlweise homogen oder heterogen ausgeprägt sein sollen, sowie die Bildung einheitlich guter Gruppen einer gesamten Kohorte von Lernenden. Die GroupAL-Architektur ermöglicht die Verwendung verschiedener Algorithmen zur Gruppenformation und definiert ein normiertes Gütemaß für Lerngruppen, welches den Vergleich verschiedener Gruppenformationen über Kriterienvariationen und Kohortenänderungen hinweg erlaubt. Die abschließend dargestellte Evaluation zeigt, dass GroupAL unter den gewählten Bedingungen bessere Ergebnisse liefert als bisherige Ansätze und umfassendere Anwendungsmöglichkeiten zur Lerngruppenbildung bietet. Summary Fostering knowledge exchange among peers is important aspect for motivation, achievement of learning goals as well as improvement of problem solving competency in elearning environments or for computer-based learning. Still, the positive effects of such an exchange depend strongly on the suitability of the selected peers in a learning group. This article describes categories of criteria to be considered by a group formation algorithm for learning groups. Additionally, existing algorithmic solutions from related work will be compared concerning several imposed requirements. For simultaneous consideration of all these requirements, the GroupAL algorithm is introduced. It supports the use of multi-dimensional criteria that are either expected to be matched homogeneous or heterogeneous among participants while aiming for equally good group formation for the whole cohort of participants to be matched. The underlying GroupAL architecture various group formation algorithms and defines a normed metric for learning group formations. This metric allows comparison of different created group formations and is robust against variations on number of used criteria or changes in the underlying cohort of participants. Finally, the presented evaluation reveals the advantages and widespread applicability of GroupAL in comparison to the investigated algorithmic solutions from related work. The approach chosen for GroupAL results in better cohort performance indices and group formation quality under the chosen conditions and with the selected data sets.

Social Serious Games: Wie Social Media den Wissensaustausch in Lernspielen unterstützen

Social Media ermöglichen Anwendern den leichten Austausch von Erfahrungen, Einsichten und persönlichen Neuigkeiten. Aktive Partizipation durch das Teilen erstellter Inhalte, die Diskussion, Bewertung und das Vernetzen mit Personen oder Inhalten sind Kernaspekte moderner Internetanwendungen. Diese Inhalte und Verknüpfungen spiegeln auch die Expertise, Interessen und Meinungen der einzelnen Anwender wider. Daher ist die Nutzung von Social Media ebenfalls sehr reizvoll für die Vernetzung und den Austausch von Spielenden des gleichen LernComputerspiels (engl.: Educational Games). Social Media-Inhalte können dann als Lernressourcen genutzt werden, die von anderen erstellt, bearbeitet und verteilt werden. Auf diese Weise unterstützen Social Media-Anwendungen die gegenseitigen Vermittlung von Lerninhalten (engl.: Peer Education) in den Spielen, beispielsweise in Form von Lösungshinweisen zu Aufgaben, gegenseitigen Bewertungen und dem Austausch von Feedback. Basierend auf einem interdisziplinären Forschungsansatz, welcher die Gebiete Pädagogik, Serious Games und Social Media verbindet, werden in der hier vorgestellten Dissertation drei Hauptbeiträge entwickelt: 1 die Unterstützung des Spielekontext-bezogenen Inhaltsaustausches der Lernenden untereinander, 2 die Personalisierung und Adaption des Spiels basierend auf Social Media-Daten oder Interkationen zwischen Spielendem und vernetzten Nutzern (in Social Media-Anwendungen) und 3 die algorithmische Formation kleiner Lerngruppen, um eine bestmögliche gegenseitige Unterstützung der Lernendenbei der Lernzielerreichung zuunterstützen.

Modeling the Player: Predictability of the Models of Bartle and Kolb Based on NEO-FFI (Big5) and the Implications for Game Based Learning

For adaptation and personalization of game play sophisticated player models and learner models are used in game-based learning environments. Thus, the game flow can be optimized to increase efficiency and effectiveness of gaming and learning in parallel. In the field of gaming still the Bartle model is commonly used due to its simplicity and good mapping to game scenarios, for learning the Learning Style Inventory from Kolb or Index of Learning Styles by Felder and Silverman are well known. For personality traits the NEO-FFI (Big5) model is widely accepted. When designing games, it is always a challenge to assess one players profile characteristics properly in all three models (player/learner/personality). To reduce the effort and amount of dimensions and questionnaires a player might have to fill out, we proved the hypothesis that both, Learning Style Inventory and Bartle Player Types could be predicted by knowing the personality traits based on NEO-FFI. Thus we investigated the statistical correlations among the models by collecting answers to the questionnaires of Bartle Test, Kolb LSI 3.1 and BFI-K (short version of NEO-FFI). A study was conducted in spring 2012 with six school classes of grade 9 (12-14 year old students) in two different secondary schools in Germany. 74 students participated in the study which was offered optionally after the use of a game-based learning tool for peer learning. We present the results statistics and correlations among the models as well as the interdependencies with the students level of proficiency and their social connectedness. In conclusion, the evaluation (correlation and regression analyses) proved the independency of the models and the validity of the dimensions. Still, especially for all of the playing style preferences of Bartles model significant correlations with some of the analyzed other questionnaire items could be found. As no predictions of learning style preferences is possible on the basis of this studies data, the final recommendation for the development of game-based learning application concludes that separate modeling for the adaptation game flow (playing) and learn flow (learning) is still necessary.

Erstellung, Steuerung und Evaluation von Serious Games

ZusammenfassungSerious Games sind hochkomplex. Sie verbinden Game-Technologien und spielerische Konzepte mit weiteren Technologien und relevanten Konzepten für die verschiedenen Einsatzgebiete von Serious Games. Im Beitrag werden wissenschaftlich-technische Methoden, Konzepte und Software-Lösungen zur Erstellung, Steuerung und Evaluation von Serious Games vorgestellt, die in der Gruppe Serious Games am Fachgebiet Multimedia Kommunikation der TU Darmstadt entwickelt wurden. Praxisbeispiele umfassen die Bereiche Bildung und Training sowie Sport und Gesundheit.