Computer-assisted techniques to enhance transformative learning in first-year literature courses

When students use computers as learning tools, the whole process of learning, and, indeed, the learners themselves, are transformed. This article illustrates some techniques that foster transformative learning in computer-assisted first-year literature classes: first, a lesson plan on A Valediction: Forbidding Mourning that uses Microsoft Word functions, including format painter, tables, and annotation to explore meaning in context; second, a plan for learners to use subconference options in the Daedalus Interactive Writing Environment to analyze Oedipus Rex; finally, a demonstration of how students engage in a meta-reflection process as they explore Barn Burning with Freelance Graphics.Marguerite Jamieson is an English instructor at Anne Arundel Community College in Arnold, Maryland, and a doctoral student at George Mason University. Her research interests include forming bridges between adult learning theory and contemporary literary theory — especially drawing on transformational learning theory and the work of Mikhail Bakhtin and Lev Vygotsky.Rebecca Kajs holds a doctorate in English from Texas Woman's University with a concentration in rhetoric. For ten years, she taught the use of heuristic tools for reading analysis at the University of Texas at Arlington. She is currently an associate professor of English and Philosophy at Anne Arundel Community College.Anne Agee holds a doctorate in rhetoric from The Catholic University of America. A professor of English and formerly director of the Humanities Computer Center at Anne Arundel Community College, she is currently the college's Coordinator of Instructional Technology. Dr. Agee and Professor Jamieson have collaborated in a study of the learning environment in a computer classroom, the results of which were published in the Fall 1995 issue of Teaching/Learning Conversations. Dr. Agee has also published Using [Daedalus] InterChange as a Teachers' Journal in the Fall 1995 issue of Wings.     

Inactivation of nitric oxide synthase isoforms by diaminoguanidine and NG-amino-L-arginine

We studied the value of long video-split electroencephalographic monitoring (VSEEG) in detecting myoclonus in nearly SSPE and evaluated the natural history and outcome-affecting factors. The 32 newly diagnosed patients had VSEEG to detect myoclonus and its correlations with EEG periodic complexes. Disease progression was monitored by a special "outcome score'; the chi-square test and multi-variable statistics analysed the outcome score in relation to different variables, such as age at onset, sex, duration of symptoms at presentation, CSF measles antibody titre, type and interval between periodic complexes (EEG discharges). Myoclonus or atonia occurred in all patients and was time-related to the EEG periodic complexes; in 32% of patients, myoclonus or atonia were not clinically evident. The EEG periodic complexes were of 3 types: Type I (16 patients) periodic giant delta waves; Type II (10 patients) periodic giant delta waves intermixed with rapid spikes or fast activity; and Type III (6 patients), long spike-wave discharges interrupted by giant delta waves. Outcome score was associated with symptoms duration (P < 0.01) and EEG periodic complexes (P < 0.05). Symptom duration was inversely related to final outcome (multi-variable analysis). Long VSEEG monitoring greatly improves early diagnosis and detection of subtle atonia or segmental myoclonus. Prognostic factors were the type of EEG periodic complexes and duration of symptoms at presentation.     

Two nonlinear optimization methods for black box identification compared

In this paper, two nonlinear optimization methods for the identification of nonlinear systems are compared. Both methods estimate the parameters of e.g. a polynomial nonlinear state-space model by means of a nonlinear least-squares optimization of the same cost function. While the first method does not estimate the states explicitly, the second method estimates both states and parameters adding an extra constraint equation. Both methods are introduced and their similarities and differences are discussed utilizing simulation data. The unconstrained method appears to be faster and more memory efficient, but the constrained method has a significant advantage as well: it is robust for unstable systems of which bounded input-output data can be measured (e.g. a system captured in a stabilizing feedback loop). Both methods have successfully been applied on real-life measurement data.     

Locating computer clubs in multicultural neighborhoods: How collaborative project work fosters integration processes

Located in socially and culturally diverse neighborhoods, we have built a network of intercultural computer clubs, called come_IN. These clubs offer a place to share practices among children and adults of diverse ethnical backgrounds. We show how this initiative ties into the striving for the integration of migrant communities and host society in Germany. In this paper, we analyze how collaborative project work and the use of mobile media and technologies contribute to integration processes in multicultural neighborhoods. Qualitative data gathered from interviews with club participants, participative observation in the computer clubs, as well as the analysis of artifacts created during project work provides the background needed to match local needs and peculiarities with (mobile) technologies. Based on these findings we present two approaches to add to the technological infrastructure: (1) a mesh-network extending the clubs into the neighborhood and (2) a project management tool, which supports projects and stimulates the sharing of ideas among projects.     

From monolithic to component-based performance evaluation of software architectures

Model-based performance evaluation methods for software architectures can help architects to assess design alternatives and save costs for late life-cycle performance fixes. A recent trend is component-based performance modelling, which aims at creating reusable performance models; a number of such methods have been proposed during the last decade. Their accuracy and the needed effort for modelling are heavily influenced by human factors, which are so far hardly understood empirically. Do component-based methods allow to make performance predictions with a comparable accuracy while saving effort in a reuse scenario? We examined three monolithic methods (SPE, umlPSI, Capacity Planning (CP)) and one component-based performance evaluation method (PCM) with regard to their accuracy and effort from the viewpoint of method users. We conducted a series of three experiments (with different levels of control) involving 47 computer science students. In the first experiment, we compared the applicability of the monolithic methods in order to choose one of them for comparison. In the second experiment, we compared the accuracy and effort of this monolithic and the component-based method for the model creation case. In the third, we studied the effort reduction from reusing component-based models. Data were collected based on the resulting artefacts, questionnaires and screen recording. They were analysed using hypothesis testing, linear models, and analysis of variance. For the monolithic methods, we found that using SPE and CP resulted in accurate predictions, while umlPSI produced over-estimates. Comparing the component-based method PCM with SPE, we found that creating reusable models using PCM takes more (but not drastically more) time than using SPE and that participants can create accurate models with both techniques. Finally, we found that reusing PCM models can save time, because effort to reuse can be explained by a model that is independent of the inner complexity of a component. The tasks performed in our experiments reflect only a subset of the actual activities when applying model-based performance evaluation methods in a software development process. Our results indicate that sufficient prediction accuracy can be achieved with both monolithic and component-based methods, and that the higher effort for component-based performance modelling will indeed pay off when the component models incorporate and hide a sufficient amount of complexity.     

Improving literacy and metacognition with electronic portfolios: Teaching and learning with ePEARL

Can an electronic portfolio that is both a multimedia container for student work and a tool to support key learning processes have a positive impact on the literacy practices and self-regulated learning skills of students? This article presents the findings of a yearlong study conducted in three Canadian provinces during the 2007–2008 school year initially involving 32 teachers and 388 students. Due to varying levels of implementation our final data set included 14 teachers and 296 students. Using a non-equivalent pre-test/post-test design, we found that grade 4–6 students who were in classrooms where the teacher provided regular and appropriate use of the electronic portfolio tool ePEARL (i.e., medium–high implementation condition, n = 7 classrooms and 121 students), compared to control students (n = 7 classrooms and 175 students) who did not use ePEARL, showed significant improvements (p < .05) in their writing skills on a standardized literacy measure (i.e., the constructed response subtest of the Canadian Achievement Test-4th ed.) and certain metacognitive skills measured via student self-report. The results of this study indicate that teaching with ePEARL has positive impacts on students’ literacy and self-regulated learning skills when the tool is used regularly and integrated into classroom instruction.     

IT professional identity: needs,perceptions, and belonging

A theoretical model of professional identification is developed and empirically examined as a means to understanding information technology (IT) workers’ attachment to the IT profession. Professional identification represents oneness with or belonging to a profession and provides a unique means of investigating and evaluating the IT profession. Results from a survey of 305 IT workers indicate that professional identification is directly impacted by three factors: (1) the individual's need for professional identification; (2) the individual's perceived similarity to others in the IT profession; and (3) the individual's perceptions of the IT profession, signifying the importance of internalization to identification. Professional identification is also indirectly impacted by the public's perception of the IT profession.     

Subspace sums for extracting non-random data from massive noise

An algorithm is introduced that distinguishes relevant data points from randomly distributed noise. The algorithm is related to subspace clustering based on axis-parallel projections, but considers membership in any projected cluster of a given side length, as opposed to a particular cluster. An aggregate measure is introduced that is based on the total number of points that are close to the given point in all possible 2 ^d projections of a d-dimensional hypercube. No explicit summation over subspaces is required for evaluating this measure. Attribute values are normalized based on rank order to avoid making assumptions on the distribution of random data. Effectiveness of the algorithm is demonstrated through comparison with conventional outlier detection on a real microarray data set as well as on time series subsequence data.

Effects of auditory,haptic and visual feedback on performing gestures by gaze or by hand

Modern interaction techniques like non-intrusive gestures provide means for interacting with distant displays and smart objects without touching them. We were interested in the effects of feedback modality (auditory, haptic or visual) and its combined effect with input modality on user performance and experience in such interactions. Therefore, we conducted two exploratory experiments where numbers were entered, either by gaze or hand, using gestures composed of four stroke elements (up, down, left and right). In Experiment 1, a simple feedback was given on each stroke during the motor action of gesturing: an audible click, a haptic tap or a visual flash. In Experiment 2, a semantic feedback was given on the final gesture: the executed number was spoken, coded by haptic taps or shown as text. With simultaneous simple feedback in Experiment 1, performance with hand input was slower but more accurate than with gaze input. With semantic feedback in Experiment 2, however, hand input was only slower. Effects of feedback modality were of minor importance; nevertheless, semantic haptic feedback in Experiment 2 showed to be useless at least without extensive training. Error patterns differed between both input modes, but again not dependent on feedback modality. Taken together, the results show that in designing gestural systems, choosing a feedback modality can be given a low priority; it can be chosen according to the task, context and user preferences.     

Static worksharing strategies for heterogeneous computers with unrecoverable interruptions

One has a large computational workload that is “divisible” (its constituent tasks’ granularity can be adjusted arbitrarily) and one has access to p remote computers that can assist in computing the workload. How can one best utilize the computers? Two features complicate this question. First, the remote computers may differ from one another in speed. Second, each remote computer is subject to interruptions of known likelihood that kill all work in progress on it. One wishes to orchestrate sharing the workload with the remote computers in a way that maximizes the expected amount of work completed. We deal with three versions of this problem. The simplest version ignores communication costs but allows computers to differ in speed (a heterogeneous set of computers). The other two versions account for communication costs, first with identical remote computers (a homogeneous set of computers), and then with computers that may differ in speed. We provide exact expressions for the optimal work expectation for all three versions of the problem - via explicit closed-form expressions for the first two versions, and via a recurrence that computes this optimal value for the last, most general version.