Solutions to cognitive overload in game-based learning using learning experience design for K-12 education : a review of the literature

Learners especially in K-12 education often encounter learning difficulties in the context of game-based learning. One explanation accounting for such learning difficulties in game-based environments is that cognitive load is not properly managed and overload is imposed on learners’ working memory. Learning Experience Design (LX Design), as an ideal substitute compared to instructional design, has been growing to meet the requirements of game-based learning in the 21st century. Based on the development of game-based learning, various factors (e.g., content and functionality) should be weighed to facilitate an optimal learning experience. This report reviews the literature on the impact of cognitive (over)load on game-based learning based on Cognitive Load Theory (CLT). The report applies one specific User Experience Design (UX Design) model, Garrett’s Elements (Garrett, 2011) to explain how LX Design can provide solutions to learning difficulties caused by cognitive (over)load. The purpose of this report is to conduct a review of literature including empirical studies and theoretical articles from 2007 to present. The findings showed that meaningful learning experience designs for game-based learning, in which three types of cognitive load (i.e., intrinsic, extraneous and germane cognitive load) play an important role, should integrate game design with instructional design principles.Curriculum and Instructio

A Holistic Usability Framework For Distributed Simulation Systems

This dissertation develops a holistic usability framework for distributed simulation systems (DSSs). The framework is developed considering relevant research in human-computer interaction, computer science, technical writing, engineering, management, and psychology. The methodology used consists of three steps: (1) framework development, (2) surveys of users to validate and refine the framework, and to determine attribute weights, and (3) application of the framework to two real-world systems. The concept of a holistic usability framework for DSSs arose during a project to improve the usability of the Virtual Test Bed, a prototypical DSS, and the framework is partly a result of that project. In addition, DSSs at Ames Research Center were studied for additional insights. The framework has six dimensions: end user needs, end user interface(s), programming, installation, training, and documentation. The categories of participants in this study include managers, researchers, programmers, end users, trainers, and trainees. The first survey was used to obtain qualitative and quantitative data to validate and refine the framework. Attributes that failed the validation test were dropped from the framework. A second survey was used to obtain attribute weights. The refined framework was used to evaluate two existing DSSs, measuring their holistic usabilities. Ensuring that the needs of the variety of types of users who interact with the system during design, development, and use are met is important to launch a successful system. Adequate consideration of system usability along the several dimensions in the framework will not only ensure system success but also increase productivity, lower life cycle costs, and result in a more pleasurable working experience for people who work with the system

A Guided Chatbot Learning Experience in the Science Classroom

This dissertation describes a practitioner’s design-based development of a prototype chatbot to guide students in learning biological concepts of genetic mutations and protein synthesis. This chatbot’s architecture provides learning activities, feedback, and support throughout a series of short, connected lessons. The chatbot is designed to scaffold learners through a predict, observe, explain model of inquiry learning. It utilizes real-world phenomena to lead students through biology core ideas, science and engineering practices, and crosscutting concepts. Results of prototype testing include survey results in support of the proof of concept among both students and teachers, as well as accuracy measurements of chatbot intents. Descriptive statistics and suggestions were collected from both groups to evaluate the relevancy, consistency, practicality, and effectiveness of the project as well as speak to improvements for future projects. The designer finds that the construction of chatbots as guided learning experiences holds untapped potential in science educational technology. Advisor: Guy Traini

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Educational Technology

Educational technology is the study and ethical practice of facilitating learning and improving performance by creating, using, and managing appropriate technological processes and resources. From the perspective of technology used in education, educational technology could be understood as the use of emerging and existing technologies to improve learning experiences in a variety of instructional settings, such as formal learning, informal learning, non-formal learning, lifelong learning, learning on demand, and just-in-time learning. Educational technology approaches have evolved from early uses of audiovisual aids to individual and networked computers, and now have evolved to include various mobile and smart technologies, as well as virtual and augmented realities, avatar-based immersive environments, cloud computing, and wearable and location-aware devices. Various terms have been used along the way to refer to educational technologies, such as learning technologies/environments and instructional technologies/systems. We have embraced a broad interpretation in this book to cover instructional design approaches, learning strategies, and hardware and software. Our view is that anything that consistently can support learning and instruction can be considered an educational technology. Some educational technologies are simple and have existed for many years; others are complex, and new ones are finding their way into educational settings every day. Educational technology focuses on both the technical and pedagogical ways and means of supporting learning and instruction. It is the basis for the success of the e-learning revolution in recent years. Technology-based instruction can surpass traditional classroom-based instruction in quality by providing a wide variety of affordances and capabilities that can promote motivation and result in engaging, efficient, and effective learning. The demand for educational technologies has been rising steadily; e-learning is a huge and expanding worldwide industry. Commercial e-learning companies, training departments in large companies and organizations, computer software companies, and educational institutions over the world employ large numbers of specialists in various aspects of educational technology creation (programming, graphic design, instructional design, task analysis, usability engineering, subject matter analysis, editing, etc.). However, these organizations often find it hard to employ suitably qualified workers who have knowledge beyond their subfields and disciplines. There is a strong demand for technologists who understand learnin

Intelligent Mobile Learning Interaction System (IMLIS): A Personalized Learning System for People with Mental Disabilities

The domain of learning context for people with special needs is a big challenge for digi- tal media in education. This thesis describes the main ideas and the architecture of a system called Intelligent Mobile Learning Interaction System (IMLIS) that provides a mobile learning environment for people with mental disabilities. The design of IMLIS aims to enhance personalization aspects by using a decision engine, which makes deci- sions based on the user s abilities, learning history and reactions to processes. It allows for adaptation, adjustment and personalization of content, learning activities, and the user interface on different levels in a context where learners and teachers are targeting autonomous learning by personalized lessons and feedback. Due to IMLIS dynamic structure and flexible patterns, it is able to meet the specific needs of individuals and to engage them in learning activities with new learning motivations. In addition to support- ing learning material and educational aspects, mobile learning fosters learning across context and provides more social communication and collaboration for its users. The suggested methodology defines a comprehensive learning process for the mentally disabled to support them in formal and informal learning. We apply knowledge from the field of research and practice to people with mental disabilities, as well as discuss the pedagogical and didactical aspects of the design

Optimal user esperience in social commerce: the role of emotions, flow and user-generated information

This doctoral dissertation aims to understand how to optimize online customer experience in the highly interactive environment of social commerce. In an attempt to go beyond online commercial transactions and to support a consumer-centered and social-oriented perspective, social commerce offers users the necessary tools (e.g., recommendations, referrals, ratings and forums) for fostering social interactions during the online purchasing process. User-generated content, the fruit of these social interactions, can affect and help users in their decision-making process. Hence, the main objective of this dissertation aims to understand online consumer behavior to optimize the customer experience in social commerce. This doctoral dissertation is organized into four studies.Study 1 aims to investigate the customer engagement behavior literature in depth, analyzing the cognitive, affective and behavioral dimensions of the engagement generation process in social commerce and the role of emotions within that process. This study proposes a model of the cognitive, affective and behavioral dimensions of the engagement generation process. The model analyzes how interactivity, social presence and enjoyment affect sPassion and result in positive sWOM. The results confirm empirically that cognitive experience and emotional feelings derived from the process boost user participation. At the core of the process, sPassion positively affects the spread of sWOM. Study 2 has the objective of reaching a wider understanding of optimal user experience in social commerce and its mediating effect between emotions and behavior. Accordingly, the study is divided into two parts: first, to analyze the dimensionality, structure and measurement of the state of flow; and second, to test how websites can improve user experience to boost positive sWOM while avoiding negative sWOM. The empirical results confirm the three-dimensional nature of the concept and support its second-order reflective structure, thereby helping to establish the basis for measuring state of flow, its structure and factors; and it confirms that passionate users are likely to experience a state of flow and, as a consequence, to share positive sWOM. Study 3 investigates how user-generated versus company-generated information contributes to trust in the social commerce site, at the same time analyzing how user-generational cohorts behave (Generations X, Y and Z). Social commerce websites offer content created by the company itself and by its users, and this content is accessible without time and space constraints; therefore, everyone, regardless of age, can access social commerce information. The mission of social commerce is to boost tradeoffs while offering users the chance to share their own experiences and to obtain information from the experiences of others. Hence, trust transferred in this part of the purchasing decision process will be influenced by trust in the type of information available. Thus, Study 3 analyzes how user-generated and company-generated information contribute to trust in social commerce. The younger the generation, the more trust in social commerce is transferred from trust in user-generated information; the older the generation, the more trust in social commerce is transferred from trust in company-generated information. Study 3 confirms that users cannot be considered as a single group and must be segmented into generational cohorts.Study 4 investigates user experience across cultures, analyzing the effect of hedonic and utilitarian antecedents on optimal user experience and its consequences on user intention. Taking into account the salience of emotions within experiences of digital technologies, this study has a twofold purpose. First, it analyzes how emotions such as sPassion compared with flow state affected by usability, resulting in a positive impact on emotional and behavioral loyalty. Second, as the main focus of the study, cultural background is tested as a moderating effect.This dissertation allows us to draw a number of main conclusions regarding the study of online consumer experience in social commerce. First, on the basis of the importance of emotion in customer experience, this dissertation supports the primary role of emotions in shaping optimal user experience in social commerce. Second, once users are engaged and have reached an optimal experience (state of flow), this situation drives positive changes in their behavior, positively affecting their decision-making process. Third, it is necessary to take into account the fact that generational cohorts behave differently, since they trust information in different ways. Last, but not least, despite the fact that culture influences decision-making processes, the internationalization of markets and multiculturalism is making users more and more similar.<br /

Artificial Intelligence-based Smarter Accessibility Evaluations for Comprehensive and Personalized Assessment

The research focuses on utilizing artificial intelligence (AI) and machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) in three areas: public buildings, homes, and medical devices. The overarching goal is to improve the accuracy, reliability, and effectiveness of accessibility evaluation systems by leveraging smarter technologies. For public buildings, the challenge lies in developing an accurate and reliable accessibility evaluation system. AI can play a crucial role by analyzing data, identifying potential barriers, and assessing the accessibility of various features within buildings. By training ML algorithms on relevant data, the system can learn to make accurate predictions about the accessibility of different spaces and help policymakers and architects design more inclusive environments. For private places such as homes, it is essential to have a person-focused accessibility evaluation system. By utilizing machine learning-based intelligent systems, it becomes possible to assess the accessibility of individual homes based on specific needs and requirements. This personalized approach can help identify barriers and recommend modifications or assistive technologies that can enhance accessibility and independence for PwD within their own living spaces. The research also addresses the intelligent evaluation of healthcare devices in the home. Many PwD rely on medical devices for their daily living, and ensuring the accessibility and usability of these devices is crucial. AI can be employed to evaluate the accessibility features of medical devices, provide recommendations for improvement, and even measure their effectiveness in supporting the needs of PwD. Overall, this research aims to enhance the accuracy and reliability of accessibility evaluation systems by leveraging AI and ML technologies. By doing so, it seeks to improve the quality of life for individuals with disabilities by enabling increased independence, fostering social inclusion, and promoting better accessibility in public buildings, private homes, and medical devices