Story-Based Learning: The Impact of Narrative on Learning Experiences and Outcomes

McQuiggan, Scott; Rowe, Jonathan P.; Lee, Sun Young; Lester, James C.

doi:10.1007/978-3-540-69132-7_56

Cited by 106 publications

(45 citation statements)

References 13 publications

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“…The chapter by Ha et al, "Recognizing Player Goals in Open-Ended Digital Games with Markov Logic Networks," describes research done on one of the most famous testbeds, Crystal Island, a gamebased learning environment in which the students solve a science mystery [42]. Crystal Island has been used as a testbed for both pedagogical research and earlier studies on performing goal recognition using Bayes nets and scalable n-gram models [45].…”

Section: Applicationsmentioning

confidence: 99%

Introduction

2014

Plan, Activity, and Intent Recognition

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The ability to recognize the plans and goals of other agents enables humans to reason about what other people are doing, why they are doing it, and what they will do next. This fundamental cognitive capability is also critical to interpersonal interactions because human communications presuppose an ability to understand the motivations of the participants and subjects of the discussion. As the complexity of human-machine interactions increases and automated systems become more intelligent, we strive to provide computers with comparable intent-recognition capabilities.Research addressing this area is variously referred to as plan recognition, activity recognition, goal recognition, and intent recognition. This synergistic research area combines techniques from user modeling, computer vision, natural language understanding, probabilistic reasoning, and machine learning. Plan-recognition algorithms play a crucial role in a wide variety of applications including smart homes, intelligent user interfaces, personal agent assistants, human-robot interaction, and video surveillance.Plan-recognition research in computer science dates back at least 35 years; it was initially defined in a paper by Schmidt, Sridharan, and Goodson [64]. In the last ten years, significant advances have been made on this subject by researchers in artificial intelligence (AI) and related areas. These advances have been driven by three primary factors: (1) the pressing need for sophisticated and efficient planrecognition systems for a wide variety of applications; (2) the development of new algorithmic techniques in probabilistic modeling, machine learning, and optimization (combined with more powerful computers to use these techniques); and (3) our increased ability to gather data about human activities.Recent research in the field is often divided into two subareas. Activity recognition focuses on the problem of dealing directly with noisy low-level data gathered by physical sensors such as cameras, wearable sensors, and instrumented user interfaces. The primary task in this space is to discover and extract interesting patterns in noisy sensory data that can be interpreted as meaningful activities. For example, an activity-recognition system processing a sequence of video frames might start by extracting a series of motions and then will attempt to verify that they are all part of the activity of filling a tea kettle. Plan and intent recognition concentrates on identifying high-level complex goals and intents by exploiting relationships between primitive action steps that are elements of the plan. Relationships that have been investigated include causality, temporal ordering, coordination among multiple subplans (possibly involving multiple actors), and social convention. xix a form of probabilistic reasoning [12]. Charniak and Goldman [9] argued that if plan recognition is a problem of abduction, it can best be done as Bayesian (probabilistic) inference. Bayesian inference supports the preference for minimal explanations in the case of equally ...

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Section: Applicationsmentioning

confidence: 99%

Introduction

2014

Plan, Activity, and Intent Recognition

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“…NLEs are being developed in many domains including language learning (Johnson, Vihjalmsson, & Marsella, 2005), antibullying education (Aylett, Louchart, Dias, Paiva, & Vala, 2005), and middle school science McQuiggan, Rowe, Lee, & Lester, 2008). NLEs are being developed in many domains including language learning (Johnson, Vihjalmsson, & Marsella, 2005), antibullying education (Aylett, Louchart, Dias, Paiva, & Vala, 2005), and middle school science McQuiggan, Rowe, Lee, & Lester, 2008).…”

Section: Research On Srl Within Gblesmentioning

confidence: 99%

Serious Educational Game Assessment

Annetta¹,

Bronack²

2011

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“…The results, however, failed to provide results on actual learning gains [6]. McQuiggan et al [7] evaluate the impact of rich narrative in a narrative-based adventure game for teaching microbiology, but there is no adaptive component in this system.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Adaptive Feedback in an Educational Computer Game

Conati

Manske

2009

Lecture Notes in Computer Science

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Abstract. In this paper, we present a study to evaluate the impact of adaptive feedback on the effectiveness of a pedagogical agent for an educational computer game. We compare a version of the game with no agent, and two versions with agents that differ only in the accuracy of the student model used to guide the agent's interventions. We found no difference in student learning across the three conditions, and we report an analysis to understand the reasons of these results.

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Story-Based Learning: The Impact of Narrative on Learning Experiences and Outcomes

Cited by 106 publications

References 13 publications

Introduction

Introduction

Serious Educational Game Assessment

Evaluating Adaptive Feedback in an Educational Computer Game

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