Digital Library logging using XML

This project explores the various ways in which relevant statistics can be extracted from digital library logs collected in XML. A set of potential statistics that can be used for performing clickstream analysis are listed. Clickstream analysis deals with the path taken by the user when he/she is using the digital library site. This project also involves visualization of the statistics collected. Visualizations are an intuitive way to represent raw data and they can help in gaining more insight into the statistics. The target digital library was CITIDEL and the XML logs collected from this digital library were used in the project. We also designed and developed a prototype for collection of statistics and visualizing them. Implementation of the tools was done using Java and PHP. JpGraph was used for building visualizations in PHP

Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions

In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into cognitively plausible higher level behaviors, and construct a quantitative information processing index, which can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illustrate how such a metric inspired by cognitive psychology can help answer critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video & course dropouts. Implications for research and practice are discusse

Determination of Attribute Weights for Recommender Systems Based on Product Popularity

In content- and knowledge-based recommender systems often a measure of (dis)similarity between products is used. Frequently, this measure is based on the attributes of the products. However, which attributes are important for the users of the system remains an important question to answer. In this paper, we present two approaches to determine attribute weights in a dissimilarity measure based on product popularity. We count how many times products are sold and based on this, we create two models to determine attribute weights: a Poisson regression model and a novel boosting model minimizing Poisson deviance. We evaluate these two models in two ways, namely using a clickstream analysis on four different product catalogs and a user experiment. The clickstream analysis shows that for each product catalog the standard equal weights model is outperformed by at least one of the weighting models. The user experiment shows that users seem to have a different notion of product similarity in an experimental context

An architecture for a focused trend parallel web crawler with the application of clickstream analysis

The tremendous growth of the Web poses many challenges for all-purpose single-process crawlers including the presence of some irrelevant answers among search results and the coverage and scaling issues regarding the enormous dimension of the World Wide Web. Hence, more enhanced and convincing algorithms are on demand to yield more precise and relevant search results in an appropriate amount of time. Since employing link based Web page importance metrics within a multi-processes crawler bears a considerable communication overhead on the overall system and cannot produce the precise answer set, employing these metrics in search engines is not an absolute solution to identify the best search answer set by the overall search system. Thus considering the employment of a link independent Web page importance metric is required to govern the priority rule within the queue of fetched URLs. The aim of this paper is to propose a modest weighted architecture for a focused structured parallel Web crawler which employs a link independent clickstream based Web page importance metric. The experiments of this metric over the restricted boundary Web zone of our crowded UTM University Web site shows the efficiency of the proposed metric

Revisiting the Use of Customer Information for CRM

For the past decade, customer relationship management (CRM) has been one of the priorities in marketing research and practice. However, many of the CRM systems did not perform as the companies expected. As such shortcoming could be due to inappropriate data input, this study provides a comprehensive overview of the empirical CRM literature. Along the phases of the CRM process, the authors show which kind of data has successfully proven to achieve the CRM objectives. The study provides researchers with a review of the empirical research on CRM and allows practitioners insights on the usability of customer data for CRM. --Customer Relationship Management (CRM),Customer Data

Usage Patterns and Perceptions of the Achievement, Reporting and Innovation System (ARIS)

This report offers the first systematic examination of actual usage of New York City's Achievement Reporting and Innovation System (ARIS). ARIS is a comprehensive data system designed to put student information within easy reach of school administrators and teachers. The findings suggest that ARIS has been used successfully as a school-wide planning tool, but was less valuable as a direct aid to classroom instruction. The Research Alliance will continue its study of ARIS through 2013, including an examination of some of the new components and features that have been developed recently by the Department of Education

Intelligent Support for Information Retrieval of Web Documents

The main goal of this research was to investigate the means of intelligent support for retrieval of web documents. We have proposed the architecture of the web tool system --- Trillian, which discovers the interests of users without their interaction and uses them for autonomous searching of related web content. Discovered pages are suggested to the user. The discovery of user interests is based on analysis of documents visited by the users previously. We have created a module for completely transparent tracking of the user's movement on the web, which logs both visited URLs and contents of web pages. The post analysis step is based on a variant of the suffix tree clustering algorithm. We primarily focus on overall Trillian architecture design and the process of discovering topics of interests. We have implemented an experimental prototype of Trillian and evaluated the quality, speed and usefulness of the proposed system. We have shown that clustering is a feasible technique for extraction of interests from web documents. We consider the proposed architecture to be quite promising and suitable for future extensions

Development and Use of a Tablet-Based Resuscitation Sheet for Improving Outcomes During Intensive Patient Care

Data documentation from resuscitation events in hospitals, termed code blue events, utilizes a paper form, which is institution-specific. Problems with data capture and transcription exists, due to the challenges of dynamic documentation of patient, event and outcome variables as the code blue event unfolds. We hypothesize that an electronic version of code blue real-time data capture would lead to improved resuscitation data transcription, and enable clinicians to address deficiencies in quality of care. To this effect, we present the design of a tablet-based application and its use by 20 nurses at the Mayo Clinic hospital. The results showed that the nurses preferred the tablet application over the paper based form. Furthermore, a qualitative survey showed the clinicians perceived the electronic version to be more accurate and efficient than paper-based documentation, both of which are essential for an emergency code blue resuscitation procedure