Robert K. Atkinson’s research while affiliated with Arizona State University and other places

This study aimed to establish an accurate baseline for Facebook usage and to investigate its learning potential via biometric perspective. This was explored by asking college students to browse their own Facebook page and peruse the content while wearing a high-fidelity EEG headset to record brainwave activity they experienced. Their actions were then coded using the empirically based Interactive-Constructive-Active-Passive (ICAP) engagement model to identify elements that could potentially be leveraged for learning. Based on these results, Facebook does not seem to make an ideal learning platform, due to higher frustration levels during higher cognitive tasks compared to passively consuming personally relevant non-instructional content. However, it may have more positive impacts on learning as long as initial frustration level is transformed smoothly to engagement and motivation for some learning opportunities and activities.

Drawing from the scholarly literature, it seems that across all levels of education, teachers frequently must assume that students come to them without knowledge of statistics; in essence, instructors start from scratch each time they set out to teach statistics. As such, augmented reality (AR) presents an opportunity to explore ways for students to learn basic statistical reasoning skills authentically and engagingly. This study investigated the impact of an AR learning environment, MantarayAR, on college students' learning outcomes and the perception of collaboration and engagement. The design of this study was a 3 × 2 factorial design that compared and analyzed the performance of students given three types of augmented reality conditions; a high AR experience (with movement throughout a physical space), a low AR experience (without movement throughout a physical space), and no AR experience (2-D images and text), as well as two levels of collaboration (pairs and no pairs). Using the pre-test to categorize students into low or high prior knowledge, students with low prior knowledge assigned to either high or low AR experience had statistically significant higher learning gains than those assigned to a no AR experience. The results suggest that an AR experience was particularly advantageous for students without prior knowledge of statistical reasoning. Also, students in the AR conditions reported a higher perception of engagement as measured than those who did not.

Traditional user experience assessments rely on self-report, human-system performance, and observational data that incompletely capture users' psychological demands, processing, or affect. Specifically, self-report measures require users to identify and articulate subjective responses to product features, yet users may not possess accurate awareness or may be unwilling or unable to express themselves. Similarly, human-system performance and observational measures require analysts to make inferences about hidden psychological states based on observed external patterns. This chapter discusses how biometric sensor-based affect detection technologies (e.g., eye tracking and EEG) may supplement traditional methods. By measuring biometric indicators of psychological states, researchers can gain potentially richer and more accurate insights into user experience. These technologies are gaining traction in educational technology development and functionality, and thus the extension of these tools for usability and user experience evaluation is highly feasible.

Affect is inextricably related to human cognitive processes and ex- presses a great deal about human necessities (Picard, 1997); affect signals what matters to us and what we care about. Furthermore, affect impacts our rational decision-making and action selection (Picard, 2010). Provid- ing computers with the capability to recognize, understand, and respond to human affective states would narrow the communication gap between the highly emotional human and the emotionally detached computer, en- hancing their interactions. Computer applications in learning, health care, and entertainment stand to bene t from such capabilities. Affect is a conceptual quantity with fuzzy boundaries and with sub- stantial individual difference variations in expression and experience (Picard, 1997). This makes measuring affect a challenging task. This chap- ter does not intend to present a comprehensive survey of the methods used to measure affect but instead provides a roadmap through a selection of the principal approaches and technologies. Section “Affect, Emotions, and Measurement” introduces the concepts and background behind affect measurement. Section “Gathering Data: Approaches and Technologies” presents approaches and technologies, explaining the type of data gath- ered from each, its characteristics, and its pros and cons. Section “Data Handling: Sampling, Filtering, and Integration” describes the stages of data handling, which include data sampling, data ltering, and data inte- gration from a variety of sources. Section “Data Analysis” describes tools and techniques for data analysis that correlate affect measurements with stimuli, and presents examples of the application of these tools in the anal- ysis of data samples collected in experimental studies.

Traditional user experience assessments rely on self-report, human-system performance, and observational data that incompletely capture users' psychological demands, processing, or affect. Specifically, self-report measures require users to identify and articulate subjective responses to product features, yet users may not possess accurate awareness or may be unwilling or unable to express themselves. Similarly, human-system performance and observational measures require analysts to make inferences about hidden psychological states based on observed external patterns. This chapter discusses how biometric sensor-based affect detection technologies (e.g., eye tracking and EEG) may supplement traditional methods. By measuring biometric indicators of psychological states, researchers can gain potentially richer and more accurate insights into user experience. These technologies are gaining traction in educational technology development and functionality, and thus the extension of these tools for usability and user experience evaluation is highly feasible.

The purpose of this study was to determine whether playing Quest for the Code™, a computer game designed to teach children about asthma, would help healthy children acquire knowledge about and attitudes towards asthma and whether the beneficial effects would be maintained over time. The sample consisted of 155 children from four middle schools who were randomly assigned to play Quest for the Code™ or a game about nutrition serving as the control condition. Data were collected on knowledge and attitude pre-intervention, post-intervention, and at follow-up four weeks later. The results revealed that children who played Quest for the Code™ were more knowledgeable and had more positive attitudes than their peers in the control condition. And these benefits were maintained on follow-up tests. Our findings indicate the effectiveness and potential of using Quest for the Code™ as a tool for asthma education in a classroom setting.

The book Educational Technologies: Challenges, Applications and Learning Outcomes contains fourteen chapters contributed by leading experts from the United States, China, Germany, Australia, Spain, Singapore and other countries across the globe. The chapters address the current challenges faced by researchers and professionals in the field of educational technology and how a variety of educational technologies can impact learning. In addition, these chapters provide educational technology applications, lessons learned from implementations, and recommendations for research and practices. Our contributors provide expertise on a wide range of topics that include educational values of social media, game-based curricula, transient effect in multimedia learning, using visuals to enhance learning, data visualization tools, self-monitoring, portfolio assessment systems, feedback via educational technology, embodied cognition via touch-screen technology, technological pedagogical content knowledge, classroom orchestration systems, adaptive learning systems, technology-supported language learning, and college algebra redesign with technology infusion. The book will be of interest to a diverse audience including: (a) researchers from education, psychology, computer science, and other interdisciplinary areas; (b) instructional designers, media specialists, assessment specialists, developers, and other professionals; (c) administrators and policy makers; and (d) students from educational technology, educational psychology, learning sciences, computer sciences, cognitive psychology, and other interdisciplinary areas.

The purpose of the study was to investigate the efficacy of using faded worked examples presented in matrices with problem structure variability to enhance learners’ ability to recognize the underlying structure of the problems. Specifically, this study compared the effects of matrix-format vs. linear-format faded worked examples combined with equivalent problem structure vs. contrast problem structure on learning. A total of 113 undergraduate students recruited from campus were randomly assigned to one of the four experimental conditions formed by a 2 X 2 factorial design. The results revealed three significant interactions on accuracy of anticipations, near transfer and medium transfer, suggesting that matrices foster learning when they contain contrast-structure problems but not with equivalent-structure problems.