Data mining technology for the evaluation of learning content interaction

Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

The Federal Rules of Civil Settlement

The Federal Rules of Civil Procedure were originally based upon a straightforward model of adjudication: Resolve the merits of cases at trial and use pretrial procedures to facilitate accurate trial outcomes. Though appealing in principle, this model has little relevance today. As is now well known, the endpoint around which the Federal Rules were structured — trial — virtually never occurs. Today, the vast majority of civil cases terminate in settlement. This Article is the first to argue that the current litigation process needs a new regime of civil procedure for the world of settlement This Article begins by providing a systemic analysis of why the Federal Rules inadequately prevent settlement outcomes from being distorted relative to the underlying merits — as defined by reference to substantive law — of a given dispute. It then explains how the Federal Rules can actually amplify these distortions. Indeed, notwithstanding the well-worn adage that settlement occurs in the “shadow of the law,” scholars have shown that non-merits factors exert significant influence on settlement outcomes. However, these insights have not been considered together and combined with a systemic focus on the ways in which the influence of these factors on settlement outcomes is actually a product of the basic structural features of the Federal Rules. This Article takes these next steps to explain that the “shadow of the law” that is cast on settlements is fading. Further, this Article discusses a new phenomenon in the current litigation environment — namely, that litigants’ increased reliance on prior settlements as “precedent” for future settlement decisions may move settlement even further out of the “shadow of the law” and into the “shadow of settlement” itself. This Article then traces these problems to three foundational assumptions underlying the Federal Rules of Civil Procedure, all of which have become outmoded in a world of settlement. In rethinking these assumptions, it provides a new conceptual account that contextualizes previously isolated procedural reform proposals as challenges to these foundational assumptions. It also explains how these reform efforts ought to be refined and extended with a specific view toward systematically redesigning the basic model and operation of the Federal Rules for a world of settlement. Lastly, it sets forth new proposals that seek to reorient current rules expressly toward the goal of aligning settlement outcomes with the merits of underlying claims. What emerges is a new vision of procedure — one in which the application of pretrial procedural rules do not merely facilitate trial but are designed to provide litigants with guidance regarding the merits of claims and are used to align settlement outcomes more meaningfully with the dictates of the substantive law. In describing this vision, this Article lays the groundwork for the design of a new Federal Rules of Civil Settlement

Rough sets theory for travel demand analysis in Malaysia

This study integrates the rough sets theory into tourism demand analysis. Originated from the area of Artificial Intelligence, the rough sets theory was introduced to disclose important structures and to classify objects. The Rough Sets methodology provides definitions and methods for finding which attributes separates one class or classification from another. Based on this theory can propose a formal framework for the automated transformation of data into knowledge. This makes the rough sets approach a useful classification and pattern recognition technique. This study introduces a new rough sets approach for deriving rules from information table of tourist in Malaysia. The induced rules were able to forecast change in demand with certain accuracy

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home