Creating Student Interaction Profiles for Adaptive Collaboration Gamification Design

Abstract

Benefits of collaborative learning are established and gamification methods have been used to motivate students towards achieving course goals in educational settings. However, different users prefer different game elements and rewarding approaches and static gamification approaches can be inefficient. The authors present an evidence-based method and a case study where interaction analysis and k-means clustering are used to create gamification preference profiles. These profiles can be used to create adaptive gamification approaches for online learning or collaborative learning environments, improving on static gamification designs. Furthermore, the authors discuss possibilities for using our approach in collaborative online learning environments.

Adaptive Gamification in Education: A Literature Review of Current Trends and Developments

“…In the second category, Denden et al present three user studies, two based on a feedback after using a non adapted gamified tool [9,11], and one based on a user survey [10] where participants rated statements based on game elements in order to determine their preference. Knutas et al [21] analysed videos and interviews with learners in a software engineering project to create clusters of learners based on their interactions. These clusters were then linked to Bartle player types and relevant game elements.…”

“…Four papers from the same authors [25,28,29,30] use the term "game features" to present the same level of implementation. Knutas et al [21,22] use the terms "game like elements". Mora et al [31] present different gamification "situations" (that combine different game elements).…”

“…This is a straight forward way of implementing dynamic adaptive gamification using static adaptation rules. The systems proposed by Knutas et al [21,22] use an algorithm that also uses learners' profile and interactions. In both systems, they use the Hexad player profile, and in the more recent one [22] they also use learner skills.…”

“…In both systems, they use the Hexad player profile, and in the more recent one [22] they also use learner skills. In [21] they analysed videos of students during project meetings and classified their interactions and propose different game elements based on a combination of profile and interaction types. They lay the ground rules for a dynamic adaptation based on learner activity, but do not offer a method to detect these actions in real time.…”

A process for designing algorithm-based personalized gamification

“…After ideation we used the CN2 rule induction algorithm [8] to create a classifier to identify different conditions that occur in a CSCL environment [33] and to recommend gamification tasks for the main CSCL system. The algorithm instantiation process and the resulting artifact is detailed further in Section 5.4.…”