Holistic analysis of the effectiveness of a software engineering teaching approach

To provide the best training in software engineering, several approaches and strategies are carried out. Some of them are more theoretical, learned through books and manuals, while others have a practical focus and often done in collaboration with companies. In this paper, we share an approach based on a balanced mix to foster the assimilation of knowledge, the approximation with what is done in software companies and student motivation. Two questionnaires were also carried out, one involving students, who had successfully completed the subject in past academic years (some had already graduated, and others are still students), and other questionnaire involving companies, in the field of software development, which employ students from our school. The analysis of the perspectives of the different stakeholders allows an overall and holistic) view, and a general understanding, of the effectiveness of the software engineering teaching approach. We analyse the results of the questionnaires and share some of the experiences and lessons learned.info:eu-repo/semantics/publishedVersio

International Business Research: Understanding Past Paths to Design Future Research Directions

In this paper we examine the extant research in IB by conducting a bibliometric study of the articles published in three leading international business journals – International Business Review, Journal of International Business Studies and Management International Review, over their entire track record of publication available in the ISI – Institute for Scientific Information. In longitudinal analyses of citation data we ascertain the most relevant works to the international business field. We also identify intellectual interconnectedness in co-citation networks of the research published in each journal. A second-tier analysis delves into publication patterns of those articles that are not at the top citation listings. Our results permit us better understand and depict the extant international business research and, to some extent, its evolution thus far.info:eu-repo/semantics/publishedVersio

Open and closed industry clusters: The social structure of innovation

In this paper we discuss knowledge and innovation in clusters and the benefits of clustering from a knowledge-based perspective. Knowledge-based resources and innovations are important sources of competitive advantage for firms. Aware of the importance of continuously seeking new knowledge firms increasingly seek knowledge-rich locations such as specific industry clusters across the world. These are locations characterized by the concentration of firms operating in related and supporting activities, a specialized work force and a specialized institutional environment that nurtures the industry. However, it is not likely that these clusters are always locations from which the firms will be able to draw the intended knowledge benefits. The social structure of the relationships between individuals and firms determines the extent to which knowledge will be created, will flow between co-located firms and bounds the knowledge benefits the firms may capture. We finish with a discussion of the need of further examination of the network dynamics involved in an industry cluster to obtain a clearer identification of the actual positive externalities that may accrue to co-locating firms.Strategy; Industry clusters; Innovation

Assessing User Expertise in Spoken Dialog System Interactions

Identifying the level of expertise of its users is important for a system since it can lead to a better interaction through adaptation techniques. Furthermore, this information can be used in offline processes of root cause analysis. However, not much effort has been put into automatically identifying the level of expertise of an user, especially in dialog-based interactions. In this paper we present an approach based on a specific set of task related features. Based on the distribution of the features among the two classes - Novice and Expert - we used Random Forests as a classification approach. Furthermore, we used a Support Vector Machine classifier, in order to perform a result comparison. By applying these approaches on data from a real system, Let's Go, we obtained preliminary results that we consider positive, given the difficulty of the task and the lack of competing approaches for comparison.Comment: 10 page