No abstract

No abstract

No abstract

Chronic illness and sickness includes more than just the physical treatment of a disease process. Health care relational models emphasize that adequate care for an illness involves ensuring that a patient’s emotional health and well-being is addressed along with one’s physical well-being. During a health care assessment, a doctorally prepared advanced practice nurse (APN) should take into consideration the patient’s physiological, social, neurological, and spiritual health. Today’s health care arena does not allow or reimburse for lengthy assessments or extensive health histories, practices, and support systems. The time allotted instead is spent listening to each patient’s current issues, making an assessment and diagnosis, and formulating a treatment plan and educating the patient accordingly. Because of the drive for efficiency, mainly because of reimbursement reductions, providers may doubt the necessity to discuss spirituality in the management of chronic illness. Patients that lack a social support system especially may benefit from a doctorally prepared APN’s nurturing of their spirituality for emotional comfort. Spirituality influences the ability of the patient to cope with chronic pain, either negatively or positively, and is acknowledged by doctorally prepared APNs as an important coping mechanism. For these reasons, doctorally prepared APNs should be aware of community resources to support patients with their spiritual growth and well-being.

We present Newton's Pen, a statics tutor implemented on a "pentop computer," a writing instrument with an integrated digitizer and embedded processor. The tutor, intended for undergraduate education, scaffolds students in the construction of free body diagrams and equilibrium equations. This project entailed the development of sketch understanding techniques and user interface principles for creating pedagogically-sound instructional tools for pentop computers. Development on the pentop platform presented novel challenges because of limited computational resources and a visually static, ink-on-paper display (the only dynamic output device is an audio speaker).We show that a system architecture based on a finite state machine reduces the computational complexity, and serves as a convenient means for providing context-sensitive tutorial help. Our pilot study suggests that Newton's Pen has potential as an effective teaching tool.

Kirchhoff's Pen is a pen-based tutoring system that teaches students to apply Kirchhoff's voltage law (KVL) and current law (KCL). To use the system, the student sketches a circuit schematic and annotates it to indicate component labels, mesh currents, and nodal voltages. The student then selects either mesh (KVL) or nodal (KCL) analysis, and writes the appropriate equations. The system interprets the equations, compares them to the correct equations (which are automatically derived from the circuit), and provides tutorial feedback about errors. Unlike traditional tutoring systems that work from input provided with a keyboard and mouse, our system works from ambiguous, hand-drawn input. The goal of our work is to create computational techniques to enable natural, pen-based tutoring systems that scaffold students in solving problems in the same way they would ordinarily solve them with paper and pencil. Kirchhoff's Pen is an important first step toward this goal.

Generating, grouping, and labeling free-sketch data is a difficult and time-consuming task for both user study participants and researchers. To simplify this process for both parties, we would like to have users draw isolated shapes instead of complete sketches that must be hand-labeled and grouped, and then use this data to train our free-sketch symbol recognizer. However, it is an open question whether shapes draw in isolation accurately reflect the way users draw shapes in a complete diagram. Furthermore, many of the simplest shape recognition algorithms were designed to recognize gestures, and it is not clear that they will generalize to freely-drawn shapes. To answer these questions, we perform experiments using three different recognizers to measure the effect of the data collection task on recognition accuracy. We find that recognizers trained only on isolated shapes can classify freely-sketched shapes as well as the same recognizers trained on free-sketches. We also show that user-specific training examples significantly improve recognition rates. Finally, we introduce a variant of a popular and simple gesture recognition algorithm that recognizes freely-drawn shapes as well as a highly-accurate but more complex recognizer designed explicitly for free-sketch recognition.

No abstract