Five Lenses on Team Tutor Challenges: A Multidisciplinary Approach

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

Designing Adaptive Instruction for Teams: a Meta-Analysis

The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams

Integrating Technology Acceptance Model and Health Belief Model Factors to Better Estimate Intelligent Tutoring System Use for Surge Capacity Public Health Events and Training

The U.S. public health system is continually challenged by unexpected epidemiological events that pose significant risks to the health of the community and require a commensurate surge in the public health system capacity to stem the spread of the disease. The complexity and even changing nature of funding and surge events drives agencies to innovate in order to maintain and support a competent workforce as well as update, or evolve the knowledge, skills and abilities (KSA) necessary to prevent, mitigate, or even eliminate the health crisis arising from a disease. This research investigates the capability of an agent-based, online personalized (AOP) intelligent tutoring system (ITS) that adaptively uses aptitude treatment interaction (ATI) to deliver public health training and assure competency. Also, presented is a conceptual model that combines Davis\u27 Technology Acceptance Model (TAM) and the Public Health Service\u27s Health Behavior Model (HBM) concepts to understand actual use of new technology in the public health sector. TAM is used to evaluate the effectiveness and the behavioral intent to use the system. HBM is used to explain and predict the preventative health behavior of actual use of the ITS. Our findings indicate the use of the ITS increases participant performance while providing a high level of acceptance, ease of use, and competency assurance. Without the determination of casual sequence, the TAM/HBM conceptual model demonstrated the best fit for predicting actual use of an ITS with the constructs of attitude, cues to action, and perceived ease of use showing the most influence. However, discussion of our findings indicates limited potential for an ITS to make a major contribution to adding workforce surge capacity unless members are directed to utilize it and technology barriers in the current public health IT infrastructure overcome

Authoring gamified intelligent tutoring systems.

Sistemas Tutores Inteligentes (STIs) têm recibo a atenção de acadêmicos e profissionais desde da década de 70. Tem havido um grande número de estudos recentes em apoio da efetividade de STIs. Entretanto, é muito comum que estudantes fiquem desengajados ou entediados durante o processo de aprendizagem usando STIs. Para considerar explicitamente os aspectos motivacionais de estudantes, pesquisadores estão cada vez mais interessados em usar gamificação em conjunto com STIs. Contudo, apesar de prover tutoria individualizada para estudantes e algum tipo de suporte para professores, estes usuários não têm recebido alta prioridade no desenvolvimento destes tipos de sistemas. De forma a contribuir para o uso ativo e personalizado de STIs gamificados por professores, três problemas técnicos devem ser considerados. Primeiro, projetar STI é muito complexo (deve-se considerar diferentes teorias, componentes e partes interessadas) e incluir gamificação pode aumentar significativamente tal complexidade e variabilidade. Segundo, as funcionalidades de STIs gamificados podem ser usadas de acordo com vários elementos (ex.: nível educacional, domínio de conhecimento, teorias de gamificaçãoe STI, etc). Desta forma, é imprescindível tirar proveito das teorias e práticas de ambos os tópicos para reduzir o espaço de design destes sistemas. Terceiro, para efetivamente auxiliar professores a usarem ativamente estes sistemas, faz-se necessário prover uma solução simples e usável para eles. Para lidar com estes problemas, o principal objetivo desta tese é projetar uma solução computacional de autoria para fornecer aos professores uma forma de personalizar as funcionalidades de STIs gamificados gerenciando a alta variabilidade destes sistemas e considerando as teorias/práticas de gamificação e STI. Visando alcançar este objetivo, nós identificamos o espaço de variabilidade e o representamos por meio do uso de uma abordagem de modelagem de features baseada em ontologias (OntoSPL). Desenvolvemos um modelo ontológico integrado (Ontologia de tutoria gamificada ou Gamified tutoring ontology) que conecta elementos de design de jogos apoiados por evidências no domínio de e-learning, além de teorias e frameworks de gamificação aos conceitos de STI. Finalmente, desenvolvemos uma solução de autoria (chamada AGITS) que leva em consideração tais ontologias para auxiliar professores na personalização de funcionalidades de STIs gamificados. As contribuições deste trabalho são avaliadas por meio da condução de quatro estudos empíricos: (1) conduzimos um experimento controlado para comparar a OntoSPL com uma abordagem de modelagem de features bem conhecida na literatura. Os resultados sugerem que esta abordagem é mais flexível e requer menos tempo para mudar; (2) avaliamos o modelo ontológico integrado usando um método de avaliação de ontologias (FOCA) com especialistas tanto de contexto acadêmico quanto industrial. Os resultados sugerem que as ontologias estão atendendo adequadamente os papeis de representação do conhecimento; (3) avaliamos versões não-interativas da solução de autoria desenvolvida com 59 participantes. Os resultados indicam uma atitude favorável ao uso da solução de autoria projetada,nos quais os participantes concordaram que a solução é fácil de usar, usável, simples, esteticamente atraente,tem um suporte bem percebido e alta credibilidade; e (4) avaliamos, por fim,versões interativas (do zero e usando um modelo) da solução de autoria com 41 professores. Os resultados sugerem que professores podem usar e reusar, com um alto nível de aceitação, uma solução de autoria que inclui toda a complexidade de projetar STI gamificado.Intelligent Tutoring Systems (ITSs) have been drawing the attention of academics and practitioners since early 70’s. There have been a number of recent studies in support of the effectiveness of ITSs. However, it is very common that students become disengaged or bored during the learning process by using ITSs. To explicitly consider students’ motivational aspects, researchers are increasingly interested in using gamification along with ITS.However, despite providing individualized tutoring to students and some kind of support for teachers, teachers have been not considered as first-class citizens in the development of these kinds of systems. In order to contribute to the active and customized use of gamified ITS by teachers, three technical problems should be considered. First, designing ITS is very complex (i.e., take into account different theories, components, and stahekolders) and including gamification may significantly increase such complexity and variability. Second, gamified ITS features can be used depending on several elements (e.g., educational level, knowledge domain, gamification and ITS theories, etc). Thus, it is imperative to take advantage of theories and practices from both topics to reduce the design space of these systems. Third, in order to effectively aid teachers to actively use such systems, it is needed to provide a simple and usable solution for them. To deal with these problems, the main objective of this thesis is to design an authoring computational solution to provide for teachers a way to customize gamified ITS features managing the high variability of these systems and considering gamification and ITS theories/practices. To achieve this objective, we identify the variability space and represent it using an ontology-based feature modeling approach (OntoSPL). We develop an integrated ontological model (Gamified tutoring ontology) that connects evidence-supported game design elements in the e-learning domain as well as gamification theories and frameworks to existing ITS concepts. Finally, we develop an authoring solution (named AGITS) that takes into account these ontologies to aid teachers in the customization of gamified ITS features. We evaluate our contributions by conducting four empirical studies: (1) we perform a controlled experiment to compare OntoSPL against a well-known ontology-based feature modeling approach. The results suggest that our approach is more flexible and requires less time to change; (2) we evaluate the ontological integrated model by using an ontology evaluation method (FOCA) with experts from academic and industrial settings. The results suggest that our ontologies are properly targeting the knowledge representation roles; (3) we evaluate non-interactive versions of the designed authoring solution with 59 participants. The results indicate a positive attitude towards the use of the designed authoring solutions, in which participants agreed that they are ease to use, usable, simple, aesthetically appealing, have a well-perceived system support and high credibility; and (4) we also evaluate interactive versions (scratch and template) of our authoring solution with 41 teachers. The results suggest that teachers can use and reuse, with a high acceptance level, an authoring solution that includes all the complexity to design gamified ITS

Digital Personalization in Early Childhood

This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. Digital personalization is an emerging interdisciplinary research field, with application to a variety of areas including design, education and publication industry. This book focuses on children’s education and literacy resources, which have undergone important changes with the ‘personalization revolution’ in the early 21st century. The author develops original insights from educational research and her own studies concerned with digital and non-digital personalization, to discuss in a clear and critical way the thinking, research issues and practical implications of this new field. She scrutinises the character of technology-based personalized education to substantiate the claim that the current models of personalized education tend to be technology- and business-driven, with little pedagogical understanding of the social value of personalization. Research involving touchscreens, personalized books and 2-8-year olds is interrogated for its impact on children’s development of language, creativity, identity, as well as family dynamics and classroom dialogue. The literature available on digital and non-digital personalization is discussed in relation to five key themes of personalized education, the so-called 5As: autonomy, authorship, aesthetics, attachment and authenticity. It is argued that the 5As need to be anchored in humanist principles for a sustainable pedagogy and practice. Based on the insights from research with typically and atypically developing children, Kucirkova proposes personalised pluralisation, as a pedagogical framework of personalized education for the future. The book aims to help scholars and professionals understand the connections between personalization and literacy, personalization and education, and personalization and wider socio-moral issues

Multimedia Development of English Vocabulary Learning in Primary School

In this paper, we describe a prototype of web-based intelligent handwriting education system for autonomous learning of Bengali characters. Bengali language is used by more than 211 million people of India and Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. This research project was aimed to develop an intelligent Bengali handwriting education system. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide a feedback to the students to correct themselves. Our proposed system can be accessed from smartphone or iPhone that allows students to do practice their Bengali handwriting at anytime and anywhere. Bengali is a multi-stroke input characters with extremely long cursive shaped where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm for Bengali language learning. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older national. The experimental results showed that our proposed hierarchical recognition algorithm can provide higher accuracy than traditional multi-stroke recognition algorithm with more writing variability

Cognitive tutoring and assessment systems and mathematics achievement: a quantitative study of the Summit Learning Platform

Title from PDF of title page viewed June 16, 2021Dissertation advisor: Loyce CaruthersVitaIncludes bibliographical references (pages 151-191)Thesis (Ed.D.)--School of Education. University of Missouri--Kansas City, 2021The purpose of this study was to determine if the Summit Learning Platform, a type of Intelligent Tutoring System, has a positive association with mathematics achievement of high school students in grades nine through eleven. The study was conducted in a Midwest suburban school district among three high schools within the same district. Further, a quasi-experimental research design was used with a sample size of 2000 students in the control group and 450 students in the treatment group. Data were compiled from the 2018-2019 school year and applied a combination of t-tests and analysis of variance (ANOVA) to compare the mean scores of the two groups. As comparison points, the Northwest Evaluation Association (NWEA), pre-ACT, and ACT were used in this Midwest district as measures among all students. The results demonstrated that students using the Summit Learning Platform showed significant gains when using their pre-test and post-test scores, but there was not statistical significance when analyzing the measures between the control and treatment groups. As more school districts utilize technological tools in far-reaching efforts to raise achievement levels in math, the intent of the study was to demonstrate potential benefits of the Summit Learning Platform for districts across the nation.Introduction -- Review of literature -- Methodology -- Results of analyses and conclusions -- Discussio

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks