25,815 research outputs found

    Communication and leadership skills in the Computer Science and Information Systems curricula: A case study comparison of US and Bulgarian programs

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    In this paper we present results from our curriculum research on the behavioral educational topics being in the computer science (CS) and information systems (IS) academic programs in two countries USA and Bulgaria. Specifically, we address learning outcomes as they pertain to IT Project Management. Our research reveals that the two countries approach undergraduate education from different vantage points. The US universities provide a flexible general education curriculum in many academic areas and students have the opportunity to strengthen their soft skills before they enter the workforce. Bulgarian universities provide specialized education in main CS subject areas and the students are technically strong upon graduation. Is there a way to balance out this divergent educational experience so that students get the best of both worlds? Our paper explores this aspect and provides possible solutions

    The Master's Degree: Basic Preparation for Professional Practice

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    Business Process Management Education in Academia: Status, challenges, and Recommendations

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    In response to the growing proliferation of Business Process Management (BPM) in industry and the demand this creates for BPM expertise, universities across the globe are at various stages of incorporating knowledge and skills in their teaching offerings. However, there are still only a handful of institutions that offer specialized education in BPM in a systematic and in-depth manner. This article is based on a global educators’ panel discussion held at the 2009 European Conference on Information Systems in Verona, Italy. The article presents the BPM programs of five universities from Australia, Europe, Africa, and North America, describing the BPM content covered, program and course structures, and challenges and lessons learned. The article also provides a comparative content analysis of BPM education programs illustrating a heterogeneous view of BPM. The examples presented demonstrate how different courses and programs can be developed to meet the educational goals of a university department, program, or school. This article contributes insights on how best to continuously sustain and reshape BPM education to ensure it remains dynamic, responsive, and sustainable in light of the evolving and ever-changing marketplace demands for BPM expertise

    Data Mining Applications in Higher Education and Academic Intelligence Management

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    Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management

    Graduate Catalog, 2002-2003

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    https://scholar.valpo.edu/gradcatalogs/1029/thumbnail.jp

    Graduate Catalog, 2004-2005

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    https://scholar.valpo.edu/gradcatalogs/1031/thumbnail.jp

    Graduate Catalog, 2005-2006

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    https://scholar.valpo.edu/gradcatalogs/1032/thumbnail.jp

    Graduate Catalog, 2003-2004

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    https://scholar.valpo.edu/gradcatalogs/1030/thumbnail.jp

    Boston University Bulletin. School of Management; Graduate Programs, 1980-1981

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    Each year Boston University publishes a bulletin for all undergraduate programs and separate bulletins for each School and College, Summer Term, and Overseas Programs. Requests for the undergraduat e bulle tin should be addressed to the Admissions Office and those for other bulletins to the individual School or College. This bulletin contains current information regarding the calendar, admissions, degree requirements, fees, regulations, and course offerings. The policy of the University is to give advance notice of change, when ever possible, to permit adjustment. The University reserves the right in its sole judgment to make changes of any nature in its program, calendar, or academic schedule whenever it is deemed necessary or desirable, including changes in course content, the rescheduling of classes with or without extending the academic term, canceling of scheduled classes and other academic activities, and requiring or affording alternatives for schedul ed classes or other academic activities, in any such case giving such notice thereof as is reasonably practicable under the circumstances. Boston University Bulletins (USPS 061-540) are published twenty times a year: one in January, one in March, four in May, four in June, six in July, one in August, and three in September
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