Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Eye Tracking Support for Visual Analytics Systems

Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complex datasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches. Therefore, we discuss foundations for eye tracking support in VA systems. We first review and discuss the structure and range of typical VA systems. Based on a widely used VA model, we present five comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used to systematically explore how concrete VA systems could be extended with eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and application opportunities, and classify them into research themes. In a call for action, we map the road for future research to broaden the use of eye tracking and advance visual analytics

From Visualization to Visually Enabled Reasoning

Interactive Visualization has been used to study scientific phenomena, analyze data, visualize information, and to explore large amounts of multi-variate data. It enables the human mind to gain novel insights by empowering the human visual system, encompassing the brain and the eyes, to discover properties that were previously unknown. While it is believed that the process of creating interactive visualizations is reasonably well understood, the process of stimulating and enabling human reasoning with the aid of interactive visualization tools is still a highly unexplored field. We hypothesize that visualizations make an impact if they successfully influence a thought process or a decision. Interacting with visualizations is part of this process. We present exemplary cases where visualization was successful in enabling human reasoning, and instances where the interaction with data helped in understanding the data and making a better informed decision. We suggest metrics that help in understanding the evolution of a decision making process. Such a metric would measure the efficiency of the reasoning process, rather than the performance of the visualization system or the user. We claim that the methodology of interactive visualization, which has been studied to a great extent, is now sufficiently mature, and we would like to provide some guidance regarding the evaluation of knowledge gain through visually enabled reasoning. It is our ambition to encourage the reader to take on the next step and move from information visualization to visually enabled reasoning

The Effects of Mixed-Initiative Visualization Systems on Exploratory Data Analysis

The primary purpose of information visualization is to act as a window between a user and the data. Historically, this has been accomplished via a single-agent framework: the only decision-maker in the relationship between visualization system and analyst is the analyst herself. Yet this framework arose not from first principles, but a necessity. Before this decade, computers were limited in their decision-making capabilities, especially in the face of large, complex datasets and visualization systems. This paper aims to present the design and evaluation of a mixed-initiative system that aids the user in handling large, complex datasets and dense visualization systems. We demonstrate this system with a between-groups, two-by-two study measuring the effects of this mixed-initiative system on user interactions and system usability. We find little to no evidence that the adaptive system designed here has a statistically significant impact on user interactions or system usability. We discuss the implications of this lack of evidence and examine how the data suggests a promising avenue for further research

The Effects of Mixed-Initiative Visualization Systems on Exploratory Data Analysis

The main purpose of information visualization is to act as a window between a user and data. Historically, this has been accomplished via a single-agent framework: the only decisionmaker in the relationship between visualization system and analyst is the analyst herself. Yet this framework arose not from first principles, but from necessity: prior to this decade, computers were limited in their decision-making capabilities, especially in the face of large, complex datasets and visualization systems. This thesis aims to present the design and evaluation of a mixed-initiative system that aids the user in handling large, complex datasets and dense visualization systems. We demonstrate this system with a between-groups, two-by-two study measuring the effects of this mixed-initiative system on user interactions and system usability. We find little to no evidence that the adaptive system designed here has a statistically-significant effect on user interactions or system usability. We discuss the implications of this lack of evidence, and examine how the data suggests a promising avenue of further research

Interactive maps: What we know and what we need to know

This article provides a review of the current state of science regarding cartographic interaction a complement to the traditional focus within cartography on cartographic representation. Cartographic interaction is defined as the dialog between a human and map mediated through a computing device and is essential to the research into interactive cartography geovisualization and geovisual analytics. The review is structured around six fundamental questions facing a science of cartographic interaction: (1) what is cartographic interaction (e.g. digital versus analog interactions interaction versus interfaces stages of interaction interactive maps versus mapping systems versus map mash-ups); (2) why provide cartographic interaction (e.g. visual thinking geographic insight the stages of science the cartographic problematic); (3) when should cartographic interaction be provided (e.g. static versus interactive maps interface complexity the productivity paradox flexibility versus constraint work versus enabling interactions); (4) who should be provided with cartographic interaction (e.g. user-centered design user ability expertise and motivation adaptive cartography and geocollaboration); (5) where should cartographic interaction be provided (e.g. input capabilities bandwidth and processing power display capabilities mobile mapping and location-based services); and (6) how should cartographic interaction be provided (e.g. interaction primitives objective-based versus operator-based versus operand-based taxonomies interface styles interface design)? The article concludes with a summary of research questions facing cartographic interaction and offers an outlook for cartography as a field of study moving forward

Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions

Increasing evidence has shown that theory-based health behavior change interventions are more effective than non-theory-based ones. However, only a few segments of relevant studies were theory-based, especially the studies conducted by non-psychology researchers. On the other hand, many mobile health interventions, even those based on the behavioral theories, may still fail in the absence of a user-centered design process. The gap between behavioral theories and user-centered design increases the difficulty of designing and implementing mobile health interventions. To bridge this gap, we propose a holistic approach to designing theory-based mobile health interventions built on the existing theories and frameworks of three categories: (1) behavioral theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior, and the Health Action Process Approach), (2) the technological models and frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design and Behavior Change Support System, and the Just-in-Time Adaptive Interventions), and (3) the user-centered systematic approaches (e.g., the CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic approach provides researchers a lens to see the whole picture for developing mobile health interventions