Publications | College of business

We present the results of an experiment which indicates that automated alignment of electronic learning objects to educational standards may be more feasible than previously implied. We highlight some important deficiencies in existing alignment systems and formulate suggestions for improved future ones. We consider how the changing substance of newer educational standards, a multi-faceted view of standard alignment, and a more nuanced view of the ‘alignment’ concept may bring the long-sought goal of automated standard alignment closer. We explore how lexical similarity of documents, a World+Method representation of semantics, and network-based analysis can yield promising results. We furthermore investigate the nature of false positives to better understand how validity of match is evaluated so as to better focus future alignment system development.

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Academic Journal

BIS

“User-Centered Evaluation of Arizona BioPathway: An Information Extraction, Integration, and Visualization System”

Explosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This article presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature viewing method. Relation aggregation significantly contributes to knowledge acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multi-view, relation-based interface which supports user-controlled exploration of pathway information across multiple granularities.

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Academic Journal

Business Analytics

“User Opinion Classification in Social Media: A Global Consistency Maximization Approach”

Social media is a major platform for opinion sharing. To better understand and exploit opinions on social media, we aim to classify users with opposite opinions on a topic for decision support. Rather than mining text content, we introduce a link-based classification model named Global Consistency Maximization (GCM) that partitions a social network into two classes of users with opposite opinions. Experiments on a Twitter dataset show that: (1) our global approach achieves higher accuracy than two baseline approaches; and (2) link-based classifiers are more robust to small training samples if selected properly.

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