Game-Driven Intelligent Tutoring Systems

Gómez-Martín, Marco Antonio; Gómez-Martín, Pedro Pablo; González-Calero, Pedro A.

doi:10.1007/978-3-540-28643-1_14

Cited by 21 publications

(7 citation statements)

References 2 publications

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“…The TARDIS framework uses intelligent agents in a serious game for social coaching for job interviews [4]. Other educational games have utilized intelligent agents to teach the player number factorization [22,23], the Java compilation process [35], and computational algorithms [31].…”

Section: Intelligent Agent Help Facilitiesmentioning

confidence: 99%

Exploring Help Facilities in Game-Making Software

Kao

2020

International Conference on the Foundations of Digital Games

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Help facilities have been crucial in helping users learn about software for decades. But despite widespread prevalence of game engines and game editors that ship with many of today's most popular games, there is a lack of empirical evidence on how help facilities impact game-making. For instance, certain types of help facilities may help users more than others. To better understand help facilities, we created game-making software that allowed us to systematically vary the type of help available. We then ran a study of 1646 participants that compared six help facility conditions: 1) Text Help, 2) Interactive Help, 3) Intelligent Agent Help, 4) Video Help, 5) All Help, and 6) No Help. Each participant created their own first-person shooter game level using our game-making software with a randomly assigned help facility condition. Results indicate that Interactive Help has a greater positive impact on time spent, controls learnability, learning motivation, total editor activity, and game level quality. Video Help is a close second across these same measures. CCS CONCEPTS • Applied computing → Computer games; • Human-centered computing → Empirical studies in HCI.

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Section: Intelligent Agent Help Facilitiesmentioning

confidence: 99%

Exploring Help Facilities in Game-Making Software

Kao

2020

International Conference on the Foundations of Digital Games

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“…It combines CBR with system meta-learning that demonstrating that a detailed analysis and adaptation of the learning process can be used to improve students' results. An approach that comes closer to serious games is presented by Gomez-Martin et al [18,19]. They present a metaphorical simulation of the Java Virtual Machine to help students learn Java language compilation and reinforce their understanding of object-oriented programming concepts.…”

Section: Case-based Reasoning and Human Learningmentioning

confidence: 99%

A Framework for Personalised Learning-Plan Recommendations in Game-Based Learning

Hulpuş¹,

Hayes²,

Fradinho

2014

Recommender Systems for Technology Enhanced Learning

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Personalised recommender systems receive growing attention from researchers of technology enhanced learning. The learning domain has a great need for personalisation as there is a general consensus that instructional material should be adapted to the knowledge, needs and abilities of learners. At the same time, the increase in the interest in game-based learning opens great opportunities for learning material personalisation, due to the complexity of life situations that can be simulated in serious games environments. In this paper, we present a model for competency development using serious games, which is supported by case-based learning-plan recommendation. While case-based reasoning has been used before for recommending learning objects, this work goes beyond current state-of-the-art, by recommending learning plans in a two-step hierarchical case-based planning strategy. First of all, several abstract plans are retrieved, personalised and recommended to the learner. In the second stage, the chosen plan is incrementally instantiated as the learner engages with the learning material. We also suggest how several learning strategies that resonate with a game-based learning environment, can drive the adaptation of learning material.

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“…For example, ILMDA (Intelligent Learning Material Delivery Agent) [28] that combines CBR with system meta-learning in the domain of computer science for undergraduates. An approach that comes closer to serious games is presented in [8,9]. They present a metaphorical simulation of the Java Virtual Machine to help students learn the Java language and reinforce their understanding of object-oriented programming.…”

Section: Case-based Reasoning and Human Learningmentioning

confidence: 99%

On-the-Fly Adaptive Planning for Game-Based Learning

Hulpuş

Fradinho²,

Hayes

2010

Case-Based Reasoning. Research and Development

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In this paper, we present a model for competency development using serious games, which is underpinned by a hierarchical case-based planning strategy. In our model, a learner's objectives are addressed by retrieving a suitable learning plan in a two-stage retrieval process. First of all, a suitable abstract plan is retrieved and personalised to the learner's specific requirements. In the second stage, the plan is incrementally instantiated as the learner engages with the learning material. Each instantiated plan is composed of a series of stories-interactive narratives designed to improve the learner's competence within a particular learning domain. The sequence of stories in an instantiated plan is guided by the planner, which monitors the learner performance and suggests the next learning step. To create each story, the learner's competency proficiency and performance assessment history are considered. A new story is created to further progress the plan instantiation. The plan succeeds when the user consistently reaches a required level of proficiency. The successful instantiated plan trace is stored in an experience repository and forms a knowledge base on which introspective learning techniques are applied to justify and/or refine abstract plan composition.

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Game-Driven Intelligent Tutoring Systems

Cited by 21 publications

References 2 publications

Exploring Help Facilities in Game-Making Software

Exploring Help Facilities in Game-Making Software

A Framework for Personalised Learning-Plan Recommendations in Game-Based Learning

On-the-Fly Adaptive Planning for Game-Based Learning

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