Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Efficiency Predictor: Predicting the Consumption Efficiency of Humans by Machine Learning Technique

As computer science advances and integrates with statistics in the field of machine learning, the predictability of future events is increasing. Our project focuses on leveraging this domain to forecast human performance using a minimal set of attributes, thereby reducing the need for extensive labels. As present solutions in machine learning helped humanity to predict natural events there is no accurate existing solution to predict the same for human beings. Human efficiency may include the development of an individual or the development of a team or collaboration. Making progress in a work without knowing the success rate might be a challenge as the final output may or may not give the expected results. The amount of hard work engaged in work that may fail in the future causes a great loss of time and energy. The involvement of computers integrated with the statistical models motivates and helps to predict the final output. So, we have taken the initiative to predict the future performance of a person in a more accurate and precise manner. This project aims to predict the consumption efficiency performance of a person using a machine learning algorithm by Ensemble-based Progressive Prediction. &nbsp

Survey of data mining approaches to user modeling for adaptive hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio

E-Commerce Broker Prototype Implementation and Investigation

Personalization has become a popular solution to today’s Ecomerce challenges. Various personalization techniques have been researched and marketed. But, one technique may not suit all businesses. What is required is a mechanism to enable different policies based possibly on different personalization techniques. The Ebroker architecture presented here provides a mechanism to enable different policies with minimal effort. We present here the various components of the architecture as well as the features that the architecture provides. The details of a prototype design and implementation are also discussed

Eye Tracking and Studying Examples: How Novices and Advanced Learners Study SQL Examples

Eye tracking provides information about a user’s eye gaze movements. For many years, eye tracking has been used in Human Computer Interaction (HCI) research. Similarly, research on computerised educational systems also relies heavily on students’ interactions with systems, and therefore eye tracking has been used to study and improve learning. We have recently conducted several studies on using worked examples in addition to scaffolded problem solving. The goal of the project reported in this paper was to investigate how novices and advanced students learn from examples. The study was performed in the context of SQL-Tutor, a mature Intelligent Tutoring System (ITS) that teaches SQL. We propose a new technique to analyse eye-gaze patterns named EGPA. In order to comprehend an SQL example, students require the information about tables’ names and their attributes which are available in a database schema. Thus, if students paid attention to the database schema, they would understand SQL examples more easily. We analysed students’ eye movement data from different perspectives, and found that advanced students paid more attention to database schema than novices. In future work, we will use the findings from this study to provide proactive feedback or individualised amounts of information

Design of a web-based LBS framework addressing usability, cost, and implementation constraints

This research investigates barriers that prevent Location Based Services (LBS) from reaching its full potential. The different constraints, including poor usability, lack of positioning support, costs, and integration difficulties are highlighted. A framework was designed incorporating components based on existing and new technologies that could help address the constraints of LBS and increase end-user acceptance. This research proposes that usability constraints can be addressed by adapting a system to user characteristics which are inferred on the basis of captured user context and interaction data. A prototype LBS system was developed to prove the feasibility and benefit of the framework design, demonstrating that constraints of positioning, cost, and integration can be overcome. Volunteers were asked to use the system, and to answer questions in relation to their proficiency and experience. User-feedback showed that the proposed combination of functionality was well-received, and the prototype was appealing to many users. Ground-truths from the survey were related back to data captured with a user monitoring component in order to investigate whether users can be classified according to their context and how they interact. The results have shown that statistically significant relationships exist, and that by using the C4.5 decision-tree, computer proficiency can be estimated within one class-width in 76.7% of the cases. These results suggest that it may be possible to build a user-model to estimate computer proficiency on the basis of user-interaction data. The user model could then used to improve usability through adaptive user-specific customisations

MoodleMiner: Data Mining Analysis Tool for Moodle Learning Management System

The purpose of this study is to develop a tool through which non-experts can carry out basic data mining analyses on logs they obtained via Moodle Learning Management System. The study also includes the findings obtained by applying the developed tool on a data set from a real course. The developed tool automatically extracts the features regarding student interactions with the learning system by using their click-stream data, and analyzes this data by using the data mining libraries available in the R programming language. The tool has enabled the users who do not have any expertise in data mining or programming to automatically carry out data mining analyses. The information generated by the tool will help researchers and educators alike in grouping students by their interaction levels, determining at-risk students, monitoring students' interaction levels, and identifying important features that impact students’ academic performances. The data processed by the tool can also be exported to be used in various other analyses. In the future versions of the tool, it is planned to add different analyzes such as association rule mining, sequential pattern mining etc