A “Novel” Approach to the Design of an IS Management Course

We report on the design and implementation of an unusual course in Information Systems (IS) management built around an extended case series: a fictitious but reality-based story about the trials and tribulations of a newly appointed but not-technically-trained Chief Information Officer (CIO) in his first year on the job. Together the cases constitute a true-to-life “novel” about IS management (published, in fact, as a novel, as well as individual cases). Four principles guided development of the series and its associated pedagogy: 1) Emphasis on integrative, soft-skill, and business-oriented aspects of IS, independent of underlying technologies; 2) Student derivation and ongoing refinement of cumulative theoretical frameworks arrived at via in-class discussion; 3) Identification of a set of core issues vital to practice that collectively approximate IS management as a business discipline; and 4) Design for student engagement, in particular by basing the case “story” on the monomyth, a literary pattern common to important narratives around the world. A supporting website facilitates sharing of teaching materials and experiences by faculty using the case series. We report results from using this curriculum with undergraduate and graduate students in two universities in different countries, and with executives at a multinational corporation and in an executive program at Harvard Business School. Our results suggest that a “novel-based” approach holds considerable promise for use at undergraduate, graduate, and executive levels, and that it might have advantages in addressing the so-called “enrollment crisis” in IS education, especially with the generation of “digital natives” who have come of age in an environment crowded with engaging approaches to communication and entertainment that compete for their attention

What\u27s News @ Rhode Island College

Profile Indicates: Good Academic Reputation Reason For Choosing RIC -- To Cut Auto Thefts -- Fellowships, Research, Program Support Deadlines -- Wins Fulbright-Hays Award -- Program On Calypso -- RUC Surveys Gives Insight To: Graduates\u27 Job Fate -- RIC Symposium to Address: Italy and Legacy of Fascism -- UEC Conference: To Include Prominent Educators -- Graduate Counseling Workshop -- Focus on the Faculty and Staff -- Career Week Activities Set -- History Dept. Offers \u27Outreach\u27 Series -- RIC Chamber Singers: To Perform In Boston -- Gay Enrichment Weekend -- Performing Arts Series Hosts: Compagnie Philippe Genty at RIC -- To Show Works -- Calendar of Eventshttps://digitalcommons.ric.edu/whats_news/1158/thumbnail.jp

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

Design & Learner-Centric Analytics. Proceedings of the 6th International Conference on Designs for Learning. 23-25 May, 2018, Bergen Norway

publishedVersio

Lawrence Today, Volume 70, Number 4, Fall 1990

https://lux.lawrence.edu/alumni_magazines/1077/thumbnail.jp

Automated Algorithmic Machine-to-Machine Negotiation for Lane Changes Performed by Driverless Vehicles at the Edge of the Internet of Things

This dissertation creates and examines algorithmic models for automated machine-to-machine negotiation in localized multi-agent systems at the edge of the Internet of Things. It provides an implementation of two such models for unsupervised resource allocation for the application domain of autonomous vehicle traffic as it pertains to lane changing and speed setting selection. The first part concerns negotiation via abstract argumentation. A general model for the arbitration of conflict based on abstract argumentation is outlined and then applied to a scenario where autonomous vehicles on a multi-lane highway use expert systems in consultation with private objectives to form arguments and use them to compete for lane positions. The conflict resolution component of the resulting argumentation framework is augmented with social voting to achieve a community supported conflict-free outcome. The presented model heralds a step toward independent negotiation through automated argumentation in distributed multi-agent systems. Many other cyber-physical environments embody stages for opposing positions that may benefit from this type of tool for collaboration. The second part deals with game-theoretic negotiation through mechanism design. It outlines a mechanism providing resource allocation for a fee and applies it to autonomous vehicle traffic. Vehicular agents apply for speed and lane assignments with sealed bids containing their private feasible action valuations determined within the context of their governing objective. A truth-inducing mechanism implementing an incentive-compatible strategyproof social choice functions achieves a socially optimal outcome. The model can be adapted to many application fields through the definition of a domain-appropriate operation to be used by the allocation function of the mechanism. Both presented prototypes conduct operations at the edge of the Internet of Things. They can be applied to agent networks in just about any domain where the sharing of resources is required. The social voting argumentation approach is a minimal but powerful tool facilitating the democratic process when a community makes decisions on the sharing or rationing of common-pool assets. The mechanism design model can create social welfare maximizing allocations for multiple or multidimensional resources

Innovation origins

The book establishes digital fiction in a foundation of innovation, tracing its emergence in various guises around the world

Computational models for semantic textual similarity

164 p.The overarching goal of this thesis is to advance on computational models of meaning and their evaluation. To achieve this goal we define two tasks and develop state-of-the-art systems that tackle both task: Semantic Textual Similarity (STS) and Typed Similarity.STS aims to measure the degree of semantic equivalence between two sentences by assigning graded similarity values that capture the intermediate shades of similarity. We have collected pairs of sentences to construct datasets for STS, a total of 15,436 pairs of sentences, being by far the largest collection of data for STS.We have designed, constructed and evaluated a new approach to combine knowledge-based and corpus-based methods using a cube. This new system for STS is on par with state-of-the-art approaches that make use of Machine Learning (ML) without using any of it, but ML can be used on this system, improving the results.Typed Similarity tries to identify the type of relation that holds between a pair of similar items in a digital library. Providing a reason why items are similar has applications in recommendation, personalization, and search. A range of types of similarity in this collection were identified and a set of 1,500 pairs of items from the collection were annotated using crowdsourcing.Finally, we present systems capable of resolving the Typed Similarity task. The best system resulted in a real-world application to recommend similar items to users in an online digital library