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Article
Publication date: 20 August 2009

Diego Zapata‐Rivera, Waverely VanWinkle, Bryan Doyle, Alyssa Buteux and Malcolm Bauer

The purpose of this paper is to propose and demonstrate an evidence‐based scenario design framework for assessment‐based computer games.

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Abstract

Purpose

The purpose of this paper is to propose and demonstrate an evidence‐based scenario design framework for assessment‐based computer games.

Design/methodology/approach

The evidence‐based scenario design framework is presented and demonstrated by using BELLA, a new assessment‐based gaming environment aimed at supporting student learning of vocabulary and math. BELLA integrates assessment and learning into an interactive gaming system that includes written conversations, math activities, oral and written feedback in both English and Spanish, and a visible psychometric model that is used to adaptively select activities as well as feedback levels. This paper also reports on a usability study carried out in a public middle school in New York City.

Findings

The evidence‐based, scenario design framework proves to be instrumental in helping combine game and assessment requirements. BELLA demonstrates how advances in artificial intelligence in education, cognitive science, educational measurement, and video games can be harnessed and integrated into valid instructional tools for the classroom.

Research limitations/implications

This paper provides initial evidence of the potential of these kinds of assessment‐based gaming tools to enhance teaching and learning. Future work involves exploring student learning effects in randomized controlled studies and comparing the internal assessment models to more traditional assessment instruments.

Originality/value

BELLA is the first step toward achieving engaging, assessment‐based, gaming environments for a variety of Science, Technology, Engineering and Math (STEM)‐related areas with explicit support for English language learners.

Details

Interactive Technology and Smart Education, vol. 6 no. 3
Type: Research Article
ISSN: 1741-5659

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Book part
Publication date: 20 September 2018

Stephen B. Gilbert, Michael C. Dorneich, Jamiahus Walton and Eliot Winer

This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing…

Abstract

This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs.

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
ISBN: 978-1-78754-474-1

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Article
Publication date: 1 February 2021

Daniel Bailey, Ashleigh Southam and Jamie Costley

This study aims to increase language learning (L2) output by incorporating a digital storytelling chatbot system (known as a “storybot”) that focused interactions on a narrative…

994

Abstract

Purpose

This study aims to increase language learning (L2) output by incorporating a digital storytelling chatbot system (known as a “storybot”) that focused interactions on a narrative. This study also sought to investigate student perceptions of these storybot interactions and improve on poor perception rates from previous studies.

Design/methodology/approach

This one-sample exploratory study was of student-storybot participation rates and student perceptions towards a storybot activity designed to increase L2 output. A combination of storybot participation analytics and survey analysis of student perception was carried out.

Findings

The use of storybots in the L2 class resulted in mixed participation rates. Students read nine times more than they wrote, indicating a high degree of reading comprehension necessary for storybot interaction. Survey results revealed that students believed storybots helped them meet their L2 goals, were relevant to their L2 and were easy to navigate.

Research limitations/implications

Interactions were through text messaging so no impact on speech or pronunciation could be observed. Further, the context was within a single university class in South Korea, restricting the generalization of findings to outside regions or with younger learners. Finally, while storybots proved to be valuable reading comprehension activities, the next step in this line of chatbot research should incorporate more writing prompts.

Practical implications

Storybots revealed explicit benefits to reading comprehension, as measured by cohesion between storybot delivered comprehension questions and student responses. Moreover, storybots can be used as examples for students in their own story creation, classroom forms to collect relevant student information regarding learning objectives and platforms for class quizzes.

Social implications

Storybots scaffold students through conversations, which abide by socio-pragmatic norms, providing models for L2 learners to incorporate in real-world text-based communication. Additionally, a wide range of idiomatic expressions is contextualized in comprehensible interactions that students can learn from the storybot then practice with friends.

Originality/value

This study contributes to the growing research on the use of chatbots for second L2 and offers specific insight into the use of narrative storybots as a means to increase L2 output and potentially benefit L2 reading comprehension.

Details

Interactive Technology and Smart Education, vol. 18 no. 1
Type: Research Article
ISSN: 1741-5659

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Article
Publication date: 31 May 2013

Nader Azizi, Ming Liang and Saeed Zolfaghari

Boredom is believed to be the common cause of workers' absenteeism, accidents, job dissatisfaction, and performance variations in manufacturing environments with repetitive jobs…

980

Abstract

Purpose

Boredom is believed to be the common cause of workers' absenteeism, accidents, job dissatisfaction, and performance variations in manufacturing environments with repetitive jobs. Effectively measuring and possibly predicting job boredom is the key to the design and implementation of appropriate strategies to deal with such undesirable emotional state. The purpose of this paper is to present new methodologies to measure and predict human boredom at work.

Design/methodology/approach

Two series of mathematical formulations, linear and nonlinear, to describe the variation of human boredom at work are first presented. Given the complexity of human emotions, the authors also present a probabilistic framework based on state‐of‐the‐art Bayesian networks to model employees' boredom at work.

Findings

The proposed methods centre on the prediction and measurement of human boredom at work. They enable managers to take proactive actions to deal with human boredom at work. Examples of such actions are task rotation and job redesign.

Research limitations/implications

The proposed methods are verified using a number of cases describing a set of phenomena that may occur in the real world. However, further research is required to demonstrate the validity of the models using real world data.

Originality/value

According to accessible literature, human boredom is being measured by self reporting scales thus far. This study describes and demonstrates analytical approaches to model human boredom at work.

Details

Journal of Manufacturing Technology Management, vol. 24 no. 5
Type: Research Article
ISSN: 1741-038X

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