People working collaboratively must establish and maintain awareness of one another's intentions, actions and results. Notification systems typically support awareness of the presence, tasks and actions of collaborators, but they do not adequately support awareness of persistent and complex activities. We analysed awareness breakdowns in use of our Virtual School system-stemming from problems related to the collaborative situation, group, task and tool support-to motivate the concept of activity awareness. Activity awareness builds on prior conceptions of social and action awareness, but emphasizes the importance of activity context factors like planning and coordination. This work suggests design strategies for notification systems to better support collaborative activity. r

Addressing the need to tailor usability evaluation methods (UEMs) and promote effective reuse of HCI knowledge for computing activities undertaken in divided-attention situations, we present the foundations of a unifying model that can guide evaluation efforts for notification systems. Often implemented as ubiquitous systems or within a small portion of the traditional desktop, notification systems typically deliver information of interest in a parallel, multitasking approach, extraneous or supplemental to a user's attention priority. Such systems represent a difficult challenge to evaluate meaningfully. We introduce a design model of user goals based on blends of three critical parameters-interruption, reaction, and comprehension. Categorization possibilities form a logical, descriptive design space for notification systems, rooted in human information processing theory. This model allows conceptualization of distinct action models for at least eight classes of notification systems, which we describe and analyze with a human information processing model. System classification regions immediately suggest useful empirical and analytical evaluation metrics from related literature. We present a case study that demonstrates how these techniques can assist an evaluator in adapting traditional UEMs for notification and other multitasking systems. We explain why using the design model categorization scheme enabled us to generate evaluation results that are more relevant for the system redesign than the results of the original exploration done by the system's designers.

In today's world, users want to be notified about multiple sources of information while engaged in other tasks. Notification systems are interfaces specifically designed to support user access to additional digital information from sources secondary to current activities. Many such interfaces, especially examples such as Web page advertisements and animated software agents, seem to be ineffective and distracting, and are abandoned or ignored after brief use. We believe dissatisfaction results from incorrect estimates of the user's task prioritization during design time. Consequently, information is introduced at inappropriate times and with unsuitable presentation choices. and attentional focus require changes in the way information should be delivered. By tracking priorities of user attention and inferring workload characteristics through eye gaze, physical or biomedical sensors, and input devices, attentive user interfaces (AUI) [8] and more specifically, attention-centric systems [1], can adapt information delivery to avoid overloading the user. This interface adaptivity suggests a key paradigm with enormous potential for notification systems. To best leverage the AUI paradigm for notification design, we explore how we might understand the associated costs and benefits of user notification in terms of its impact on user attention. We introduce a framework that allows these costs and benefits to be described and design options to be compared. Based on this, we show how user goal representations can be integrated with information design guidelines from usability studies. This demonstrates vast potential for AUIs in notifying users-compelling attentive notification systems. We also suggest some challenges for this emerging research community.The paramount challenge of notification is preventing unwanted distraction to the primary task, while still delivering information in an accurate and timely manner. In many cases, very little distraction can be tolerated. For example, a typical in-vehicle information system may notify the user about navigation instructions, incoming communications, and other information secondary from the main task of the user-driving the car. Such systems should be designed to ensure notification is provided without diverting attention from driving-related tasks. In other cases, a user is willing to accept some distraction in exchange for valued information. Desktop computer users may perform daily word-processing tasks while casually

Designing and evaluating notification systems represents an emerging challenge in the study of human-computer interaction. Users rely on notification systems to present potentially interruptive information in an efficient and effective manner to enable appropriate reaction and comprehension. Little is known about the effects of these systems on ongoing computer tasks. As the research community strives to understand information design suitable for opposing usage goals, few existing efforts lend themselves to extensibility.However, three often conflicting design objectives are interruption to primary tasks, reaction to specific notifications, and comprehension of information over time. Based on these competing parameters, we propose a unifying research theme for the field that defines success in notification systems design as achieving the desirable balance between attention and utility. This paradigm distinguishes notification systems research from traditional HCI by centering on the limitations of the human attention system.In a series of experiments that demonstrate this research approach and investigate use of animated text in secondary displays, we describe two empirical investigations focused on the three critical parameters during a browsing task. The first experiment compares tickering, blasting, and fading text, finding that tickering text is best for supporting deeper comprehension, fading best facilitates reaction, and, compared to the control condition, none of the animated displays are interruptive to the browsing task. The second experiment investigates fading and tickering animation in greater detail with similar tasks-at two different speeds and sizes. Here, we found smaller displays allowed better reaction but were more interruptive, while slower displays provides increased comprehension. Overall, the slow fade appears to be the best secondary display animation type tested. Focusing research and user studies within this field on critical parameters such as interruption, reaction, and comprehension will increase cohesion among design and evaluation efforts for notification systems. r

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Smartphone games can be feasible for adolescents to use for PA. Lessons learned will be used to provide improvements for future game development and evaluation.

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(Abstract)Current research suggests that by actively involving students, you can increase pedagogical value of algorithm visualizations. We believe that a pedagogically successful visualization, besides actively engaging participants, also requires certain other key features. We compared several existing algorithm visualizations for the purpose of identifying features that we believe increase the pedagogical value of an algorithm visualization. To identify the most important features from this list, we conducted two experiments using a variety of the heapsort algorithm visualizations.The results of these experiments indicate that the single most important feature is the ability to control the pace of the visualization. Providing a good data set that covers all the special cases is important to help students comprehend an unfamiliar algorithm. An algorithm visualization having minimum features that focuses on the logical steps of an algorithm is sufficient for procedural understanding of the algorithm. To have better conceptual understanding, additional features (like an activity guide that makes students cover the algorithm in detail and analyze what they are doing, and pseudocode display of an algorithm) may prove to be helpful, but that is a much harder effect to detect.iii