Accommodating Asperger's: an autoethnography on the learning experience in an e-learning music education program

Thesis (D.M.A.)--Boston UniversityA student with Asperger's Syndrome faces a complex myriad of learning disabilities and social difficulties. The co-morbid conditions of dyslexia, Obsessive Compulsive Disorder, Attention Deficit Disorder, Attention Deficit Hyperactive Disorder and anxiety further complicate Asperger's Syndrome. Asperger's Syndrome and these conditions, singularly and in combination, have the potential to significantly hamper a student's achievement and success in learning environments. I am a person with Asperger's Syndrome, formerly diagnosed as Autism Spectrum Disorder-High Functioning, engaged in Boston University's Doctorate in Music Education Program delivered via E-learning modalities. The research question, "How does the E-learning modality serve the needs of a student with Asperger's Syndrome in the field of music education?" was a direct product of my personal experience with the convergence of E-learning, music education and Asperger's Syndrome. Autoethnography was employed as the research strategy to explore this convergence. The primary data source was a journal spanning almost three decades in conjunction with artifacts and other data sources. The data analysis and interpretation was completed through self-reflective and selfnarrative writing. The findings of this study, suggest that while E-learning modalities present both positives and negatives for students with Asperger's Syndrome; the potential to alleviate many of the challenges they face makes this is an excellent alternative to the traditional classroom educational delivery method in the field of music education. Further this research highlights the importance for educators to reflect on their own teaching methods and the profession to continually evaluate the methods utilized in delivering content and assessing achievement

Diffusion Adaptation over Networks under Imperfect Information Exchange and Non-stationary Data

Adaptive networks rely on in-network and collaborative processing among distributed agents to deliver enhanced performance in estimation and inference tasks. Information is exchanged among the nodes, usually over noisy links. The combination weights that are used by the nodes to fuse information from their neighbors play a critical role in influencing the adaptation and tracking abilities of the network. This paper first investigates the mean-square performance of general adaptive diffusion algorithms in the presence of various sources of imperfect information exchanges, quantization errors, and model non-stationarities. Among other results, the analysis reveals that link noise over the regression data modifies the dynamics of the network evolution in a distinct way, and leads to biased estimates in steady-state. The analysis also reveals how the network mean-square performance is dependent on the combination weights. We use these observations to show how the combination weights can be optimized and adapted. Simulation results illustrate the theoretical findings and match well with theory.Comment: 36 pages, 7 figures, to appear in IEEE Transactions on Signal Processing, June 201

Discrete MDL Predicts in Total Variation

The Minimum Description Length (MDL) principle selects the model that has the shortest code for data plus model. We show that for a countable class of models, MDL predictions are close to the true distribution in a strong sense. The result is completely general. No independence, ergodicity, stationarity, identifiability, or other assumption on the model class need to be made. More formally, we show that for any countable class of models, the distributions selected by MDL (or MAP) asymptotically predict (merge with) the true measure in the class in total variation distance. Implications for non-i.i.d. domains like time-series forecasting, discriminative learning, and reinforcement learning are discussed.Comment: 15 LaTeX page

Effects of individual prior knowledge on collaborative knowledge construction and individual learning outcome in videoconferencing

This paper deals with collaborative knowledge construction in videoconferencing. The main issue is about how to predict individual learning outcome, in particular how far individual prior knowledge and the collaborative knowledge construction can influence individual learning outcomes. In this context, the influence of prior knowledge and two measures of instructional support, a collaboration script and a content scheme were analyzed concerning the collaborative knowledge construction. An empirical study was conducted with 159 university students as sample. Students learned collaboratively in groups of three in a case based learning environment in videoconferencing and were supported by the instructional support measures. Results show that collaborative knowledge construction had more impact on individual learning outcome than individual prior knowledge.Diese Studie beschäftigt sich mit der gemeinsamen Wissenskonstruktion in Videokonferenzen. Die Hauptfragestellung befasst sich mit Prädiktoren für den individuellen Lernerfolg, insbesondere inwieweit dieser vom individuellen Vorwissen der Lernenden und der gemeinsamen Wissenskonstruktion beeinflusst wird. In diesem Kontext wird analysiert, inwiefern das individuelle Vorwissen und zwei Unterstützungsmaßnahmen - Wissensschema und Kooperationsskript - Einfluss auf die gemeinsame Wissenskonstruktion nehmen. An der empirischen Studie nahmen 159 Universitätsstudierende teil. Diese lernten kooperativ in Dreiergruppen in einer fallbasierten Lernumgebung in Videokonferenzen und erhielten dabei instruktionale Unterstützung. Die Ergebnisse zeigen, dass die gemeinsame Wissenskonstruktion einen größeren Einfluss auf die individuellen Lernerfolge hatte, als individuelles Vorwissen

Evaluating a virtual learning environment in the context of its community of practice

The evaluation of virtual learning environments (VLEs) and similar applications has, to date, largely consisted of checklists of system features, phenomenological studies or measures of specific forms of educational efficacy. Although these approaches offer some value, they are unable to capture the complex and holistic nature of a group of individuals using a common system to support the wide range of activities that make up a course or programme of study over time. This paper employs Wenger's theories of 'communities of practice' to provide a formal structure for looking at how a VLE supports a pre-existing course community. Wenger proposes a Learning Architecture Framework for a learning community of practice, which the authors have taken to provide an evaluation framework. This approach is complementary to both the holistic and complex natures of course environments, in that particular VLE affordances are less important than the activities of the course community in respect of the system. Thus, the VLE's efficacy in its context of use is the prime area of investigation rather than a reductionist analysis of its tools and components. An example of this approach in use is presented, evaluating the VLE that supports the undergraduate medical course at the University of Edinburgh. The paper provides a theoretical grounding, derives an evaluation instrument, analyses the efficacy and validity of the instrument in practice and draws conclusions as to how and where it may best be used