Organization and constraints of a recommender system for MOOCS

Recommender Systems (RSs) are software tools providing suggestions for users about items likely to be of interest for them. MOOCs (Massive Open Online Courses) offer a vast amount of learning items to millions of users. Users face a challenging task of finding the suitable learning content, learning path and peer-learners for collaborative activities. Poor selections lead to very low completion rates, typically under 10%. The paper assumes that a better decision-making process is likely to lead to a higher completion rate and discusses the organization and constraints of a Recommender Systems in a MOOCs context. As the organization of a Learning Recommender System and the choice of algorithms represent the first two architecture design criteria for meaningful results, attention is given to this factors. The paper argues that an effective Recommender System must focus on MOOCs specific type of items (learning items) and user behavior in MOOCs context and consequently, a number of particularities and constraints are identified and discussed. In this paper, the term organization is used to describe how different components work together to form a system and the term architecture is used to describe a blue-print of how to actually build such a system

Unstructured Social Networks Data for Business Context Analysis

Communications technology has enabled new approaches to business context understanding. The paper proposes and explores a new mechanism through which unstructured social networks data about companies is gathered, aggregated and presented. The authors assume that collected data, interpreted through an adequate metrics, may be used as a tool for better understanding the business model of a company, its health and/or its sustainability. The paper does not address the issue of adequacy of the tool to the problem but the technical details to collect, aggregate and present unstructured social networking data for business context analysis. The proposed solution is a preliminary wor

Step-by-Step Model for the Study of the Apriori Algorithm for Predictive Analysis

The goal of this paper was to develop an educational oriented application based on the Data Mining Apriori Algorithm which facilitates both the research and the study of data mining by graduate students. The application could be used to discover interesting patterns in the corpus of data and to measure the impact on the speed of execution as a function of problem constraints (value of support and confidence variables) or size of the transactional data-base. The paper presents a brief overview of the Apriori Algorithm, aspects about the implementation of the algorithm using a step-by-step process, a discussion of the education-oriented user interface and the process of data mining of a test transactional data base. The impact of some constraints on the speed of the algorithm is also experimentally measured without a systematic review of different approaches to increase execution speed. Possible applications of the implementation, as well as its limits, are briefly reviewed