Serious Game Adaptive Learning Systems.

Saved in:

Bibliographic Details
Title:	Serious Game Adaptive Learning Systems.
Authors:	Obikwelu, Chinedu¹ coobikwelu@uclan.ac.uk, Read, Janet¹ JCRead@uclan.ac.uk
Source:	Proceedings of the European Conference on Games Based Learning. 2021, p442-449. 8p.
Subject Terms:	Instructional systems, Educational games, Active learning, Virtual classrooms, Virtual reality
Abstract:	Serious games have evolved from the traditional one‐size‐fits‐all mode to a Dynamic Difficulty Adjustment (DDA) mode. DDA is an individualized approach that is based on the principle of adaptivity. For serious games, there is an emphasis on adapting hints and feedbacks according to the changing learner’s competence by using set rules. Adaptive Learning Systems (ALS) are sometimes referred to as Personalised Learning Systems (PLS).According to Karagiannidis and Sampson every PLS should answer the following questions ‐ What is the learning content being adapted? Which aspects of the learning experience ‘drive’ adaptations? What is the basis for adaptation? These questions make up the adaptation logic which differs from game to game and from model to model. This paper reviews the current literature by investigating different adaptation logics embodied in existing serious game Adaptive Learning Systems. These different Adaptive Learning Systems have been adopted by and proposed for serious games. The Adaptive Learning Systems investigated in this paper include the ‘NUCLEO’ framework which emphasizes collaboration in a Multi‐User Virtual Environment (MUVE) with role assignment and team formation adapted to learners; S.M.I.L.E which is more of an accessibility model accommodating player‐learner’s with handicaps by adapting quests with the uniqueness of allowing teachers to define educational games with stored educational materials; ‘Framework for Adaptive Game Presenters with Emotions and Social Comments’ which adapts emotions and social feedback; the Fine‐Tuning System (FTS) which is based on adaptive fading ‐ adapting scaffolds including feedbacks and hints based on the fading principle; and ALIGN which separates the game logic from the adaptation logic by creating reusable adaptation abstractions. The trend in assessment generation which is drifting from the traditional After Action Review (AAR) to assessment generation for adaptive interventions is also highlighted in this paper. [ABSTRACT FROM AUTHOR]
	Copyright of Proceedings of the European Conference on Games Based Learning is the property of Academic Conferences & Publishing International Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Education Research Complete

Description
Abstract:	Serious games have evolved from the traditional one‐size‐fits‐all mode to a Dynamic Difficulty Adjustment (DDA) mode. DDA is an individualized approach that is based on the principle of adaptivity. For serious games, there is an emphasis on adapting hints and feedbacks according to the changing learner’s competence by using set rules. Adaptive Learning Systems (ALS) are sometimes referred to as Personalised Learning Systems (PLS).According to Karagiannidis and Sampson every PLS should answer the following questions ‐ What is the learning content being adapted? Which aspects of the learning experience ‘drive’ adaptations? What is the basis for adaptation? These questions make up the adaptation logic which differs from game to game and from model to model. This paper reviews the current literature by investigating different adaptation logics embodied in existing serious game Adaptive Learning Systems. These different Adaptive Learning Systems have been adopted by and proposed for serious games. The Adaptive Learning Systems investigated in this paper include the ‘NUCLEO’ framework which emphasizes collaboration in a Multi‐User Virtual Environment (MUVE) with role assignment and team formation adapted to learners; S.M.I.L.E which is more of an accessibility model accommodating player‐learner’s with handicaps by adapting quests with the uniqueness of allowing teachers to define educational games with stored educational materials; ‘Framework for Adaptive Game Presenters with Emotions and Social Comments’ which adapts emotions and social feedback; the Fine‐Tuning System (FTS) which is based on adaptive fading ‐ adapting scaffolds including feedbacks and hints based on the fading principle; and ALIGN which separates the game logic from the adaptation logic by creating reusable adaptation abstractions. The trend in assessment generation which is drifting from the traditional After Action Review (AAR) to assessment generation for adaptive interventions is also highlighted in this paper. [ABSTRACT FROM AUTHOR]
ISSN:	20490992