The Impact of Web-Scale Discovery on the Use of Electronic Resources

In 2015, the University of California, Berkeley, launched EBSCO Discovery Service (EDS), a web-scale discovery tool, with a goal of improving visibility and usage of collections. This study applies linear regression analysis to usage data for ebooks, ejournals, and abstracts and indexing (A&I) databases before and after implementation of EDS in order to identify correlations between the discovery layer and usage of library electronic resources across platforms. Our findings diverge from conclusions drawn in the previous literature that indicate that resource use generally increases after a discovery tool is implemented. We examine data from a longer period of time than the previous literature had, looking for statistically significant changes in resource use. The discovery layer at UC Berkeley did not lead to equal increases across platforms, but rather to a complex array of increases and decreases in use according to a variety of factors.

Driving usage – what are publishers and librarians doing to evaluate and promote usage?

Although a relatively recent phenomenon, measuring the usage of published research has rapidly become one of the most important ways to evaluate the relative value of different publications. Libraries and publishers are also investigating the impact of interface and technology provision in improving resource discovery and content usage. Demand for such data is increasing throughout the industry, partly in response to greater scrutiny of return on investment. As a result the techniques used by publishers and librarians to promote and evaluate usage are also developing. This paper looks at some of the methods currently adopted and examines the issues faced by the industry in driving forward the application of usage data

Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative

Accurately predicting conversions in advertisements is generally a challenging task, because such conversions do not occur frequently. In this paper, we propose a new framework to support creating high-performing ad creatives, including the accurate prediction of ad creative text conversions before delivering to the consumer. The proposed framework includes three key ideas: multi-task learning, conditional attention, and attention highlighting. Multi-task learning is an idea for improving the prediction accuracy of conversion, which predicts clicks and conversions simultaneously, to solve the difficulty of data imbalance. Furthermore, conditional attention focuses attention of each ad creative with the consideration of its genre and target gender, thus improving conversion prediction accuracy. Attention highlighting visualizes important words and/or phrases based on conditional attention. We evaluated the proposed framework with actual delivery history data (14,000 creatives displayed more than a certain number of times from Gunosy Inc.), and confirmed that these ideas improve the prediction performance of conversions, and visualize noteworthy words according to the creatives' attributes.Comment: 9 pages, 6 figures. Accepted at The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019) as an applied data science pape

The Effectiveness of Guided Discovery Learning to Teach Integral Calculus for the Mathematics Students of Mathematics Education Widya Dharma University

The objectives of this research are (1) to develop Guided Discovery Learning in integral calculus subject; (2) to identify the effectiveness of Guided Discovery Learning in improving the students' understanding toward integral calculus subject. This research was quasy experimental research with the students of even semester in Mathematics Education Widya Dharma University as the sample. Cluster Random sampling was conducted to determine control group that was taught using Conventional model and experimental group that was taught using Guided Discovery Learning model. The instruments of this research included pre-test, post-test, and student's response questionnaire. The data of post-test was analyzed using T-test. The result was H0 was rejected for the level of significance The result of this data analysis found out that Guide Discovery Learning was more effective than Conventional Model. It was supported by the result questionnaire. The result of questionnaire that more than 75% questionnaire items got 67.65% positive response. It means Guided Discovery Learning can increase students' interest in joining integral calculus class

Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision suppor