Track User Interactions Research Articles

CONSUMER BUYING BEHAVIOUR REGARDING ONLINE ADVERTISMENT

In the era of digitization, the landscape of marketing has undergone a profound transformation. Traditional methods of advertising are gradually being overshadowed by the pervasive influence of online platforms. Among the myriad forms of online marketing, online advertising stands out as a powerful tool for reaching consumers in an increasingly interconnected world. In the context of India, a country witnessing rapid digitalization and a burgeoning online population, understanding consumer buying behavior regarding online advertisements assumes paramount importance. This introduction serves to contextualize the significance of studying consumer behavior in the realm of online advertising within the Indian market. The proliferation of the internet and the widespread adoption of smartphones have revolutionized the way businesses engage with consumers. Online advertising, encompassing various formats such as display ads, social media promotions, search engine marketing, and influencer collaborations, has emerged as a cornerstone of modern marketing strategies. Its appeal lies in its ability to target specific demographics, track user interactions, and personalize content, thereby enhancing its effectiveness compared to traditional advertising channels.

Tracking User Interaction with Web and Assisting in Targeted Communication

Today, we spend most of our time online using some form of digital technology (such as search engines, news portals, or social media sites). Our online presence keeps us involved most of the time and provides a lot of information to Internet customers. The development of the web is excellent because every day about a million pages are added. Due to the massive use of the network, the log files of the network increase at a faster rate and the scope becomes enormous. Web Usage Mining uses mining technology on log data to extract user performance, which is used in different applications such as support design, e-commerce, service modification, prefetch, etc. In this paper, we propose a tool that users can use to collect data on their website, and then use this web log data to track user interactions on your website, which helps in targeted communication.

The Impact of Different Scoring Rubrics for Grading Virtual Patient-Based Exams

Virtual patient cases (VPs) are used for healthcare education and assessment. Most VP systems track user interactions to be used for assessment. Few studies have investigated how virtual exam cases should be scored and graded. We have applied eight different scoring models on a data set from 154 students. Issues studied included the impact of penalizing guessing, requiring a correct diagnose, different grading levels, and the effect of using weighted diagnose metrics. Controlling the random-guessing approach is necessary and can be accomplished by a rubric that measures a relative efficiency of the learner's inquiries and the total number of inquiries. Using a straight percentage score versus a curved exam score had a major impact on grades. Significant differences were found when using different metrics as only one of the eight rubric models resulted in a Gaussian distribution. Course directors need to analyze expected learning outcomes from a course to determine a scoring metric to assess those particular needs; the grading rubric must also control for guessing.

The Social Media Contradiction: Data Mining and Digital Death

The Social Media Contradiction: Data Mining and Digital Death

The LAILAPS Search Engine: Relevance Ranking in Life Science Databases

SummarySearch engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases.With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented.In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de

The LAILAPS search engine: relevance ranking in life science databases.

Search engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented. In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de.

The value of attention aware systems in educational settings

Research on human attention has provided designers with a wealth of information on conditions that promote effective information delivery. Attention aware systems attempt to apply those principles during computer-delivered experiences. These systems can track user interactions with computer presentations, dynamically updating interfaces and content to facilitate user focus on important material. Because the resources necessary to design and implement attention aware systems are not trivial, it is critical to outline the utility of these systems for applicable domains. In this article, the implications of such systems for educational settings are discussed. Attention aware systems may prove useful for teaching diverse learners, assessing student performance, providing feedback during curriculum development, and adding value to computer-assisted teaching methodologies. The implications described in this article are intended as a starting point for collaborative work on the application of attention aware systems to everyday educational settings.