Citation recommendation via proximity full-text citation analysis and supervised topical prior

Currently the many publications are now available electronically and online, which has had a significant effect, while brought several challenges. With the objective to enhance citation recommendation based on innovative text and graph mining algorithms along with full-text citation analysis, we utilized proximity-based citation contexts extracted from a large number of full-text publications, and then used a publication/citation topic distribution to generate a novel citation graph to calculate the publication topical importance. The importance score can be utilized as a new means to enhance the recommendation performance. Experiment with full-text citation data showed that the novel method could significantly (p < 0.001) enhance citation recommendation performance

A literature review of the use of Web 2.0 tools in Higher Education

This review focuses on the use of Web 2.0 tools in Higher Education. It provides a synthesis of the research literature in the field and a series of illustrative examples of how these tools are being used in learning and teaching. It draws out the perceived benefits that these new technologies appear to offer, and highlights some of the challenges and issues surrounding their use. The review forms the basis for a HE Academy funded project, ‘Peals in the Cloud’, which is exploring how Web 2.0 tools can be used to support evidence-based practices in learning and teaching. The project has also produced two in-depth case studies, which are reported elsewhere (Galley et al., 2010, Alevizou et al., 2010). The case studies focus on evaluation of a recently developed site for learning and teaching, Cloudworks, which harnesses Web 2.0 functionality to facilitate the sharing and discussion of educational practice. The case studies aim to explore to what extent the Web 2.0 affordances of the site are successfully promoting the sharing of ideas, as well as scholarly reflections, on learning and teaching

Computational Models of Quality for Educational Digital Resource Assessment

Educational digital libraries and peer-produced open educational resources have become integral to efforts to incorporate personalized learning into the classroom. Assuring the quality of educational content from these sources has become a major concern of the curators of such materials, and of educators who want to use them. But quality of educational materials is a multi-faceted problem, not completely understood, and often disputed. In current practice, focused manual effort by trained experts is required to assess each resource. This work attempts to leverage the large existing corpus of work in the field of computational semantics to supplement and support human judgment in educational resource assessment. Based on an in-depth study of human expert decision processes, characterizing the quality of a resource is broken down into dimensions of quality, and further into low-level, more easily identified indicators of quality; these indicators of quality alone are strongly predictive of an expert\u27s overall quality assessment of a resource. A corpus of 1000 resources from the Digital Library for Earth System Education (DLESE) was manually annotated for the presence or absence of seven important quality indicators. Human experts were able to make these assessments quite consistently. Using a supervised machine learning and document classification approach, a baseline computational system was able to train models for each of the seven indicators that achieved some agreement with the human annotation. By adjusting the computational system to make better use of the data set, these models were improved to achieve good agreement on all seven indicators. To evaluate the generalizability of this approach, an additional corpus of 230 peer-produced open educational resources from the Instructional Architect (IA) project was manually annotated for quality indicators, using a slightly modified annotation protocol. In spite of the very different nature of the materials, the computational models trained on the DLESE corpus generalized to the new data to a small extent; models trained on the new data achieved mostly good agreement

Students' Criteria for Course Selection: Towards a Metadata Standard for Distributed Higher Education

By 2007, one half of higher education students are expected to enroll in distributed learning courses. Higher education institutions need to attract students searching the Internet for courses and need to provide students with enough information to select courses. Internet resource discovery tools are readily available, however, users have difficulty selecting relevant resources. In part this is due to the lack of a standard for representation of Internet resources. An emerging solution is metadata. In the educational domain, the IEEE Learning Technology Standards Committee (LTSC) has specified a Learning Object Metadata (LOM) standard. This exploratory study (a) determined criteria students think are important for selecting higher education courses, (b) discovered relationships between these criteria and students' demographic characteristics, educational status, and Internet experience, and (c) evaluated these criteria vis-à-vis the IEEE LTSC LOM standard. Web-based questionnaires (N=209) measured (a) the criteria students think are important in the selection of higher education courses and (b) three factors that might influence students' selections. Respondents were principally female (66%), employed full time (57%), and located in the U.S. (89%). The chi square goodness-of-fit test determined 40 criteria students think are important and exploratory factor analysis determined five common factors among the top 21 criteria, three evaluative factors and two descriptive. Results indicated evaluation criteria are very important in course selection. Spearman correlation coefficients and chi-square tests of independence determined the relationships between the importance of selection criteria and demographic characteristics, educational status, and Internet experience. Four profiles emerged representing groups of students with unique concerns. Side by side analysis determined if the IEEE LTSC LOM standard included the criteria of importance to students. The IEEE LOM by itself is not enough to meet students course selection needs. Recommendations include development of a metadata standard for course evaluation and accommodation of group differences in information retrieval systems

Interactive visualization systems and data integration methods for supporting discovery in collections of scientific information

Technological developments have been enabling additional sharing and reuse of scientific information. Current indexing methods support query-based search and filtering, however they do not support overviews and exploration. Due to these limitations of existing indexing methods, it is challenging to discover records and connections that relate information in new and potentially insightful ways. We developed prototype systems and computational methods for integrating collections from multiple sources within a domain into a single, unified graph data structure. Graph-theoretic measures and visualizations were then applied to identify relations and records that support discovery tasks. Three collections of molecular information were studied: (1) influenza protein sequences from the National Center for Biotechnology Information, (2) Open Notebook Science notebooks and databases from Drexel University and other academic chemical research laboratories, and (3) project data from drug discovery projects at Pfizer R&D. We designed methods for data integration within these collections. We then analyzed the integrated collections to design interactive visual tools and computational methods that could systematically identify relations and records that have a high potential to lead to novel discoveries in these areas. We conducted interviews with domain experts to evaluate the effectiveness of these designs. These studies demonstrate the feasibility of the new indexing methods to improve the discoverability of novel connections across multiple collections within a domain.Ph.D., Information Science -- Drexel University, 201

Deep Understanding of Technical Documents : Automated Generation of Pseudocode from Digital Diagrams & Analysis/Synthesis of Mathematical Formulas

The technical document is an entity that consists of several essential and interconnected parts, often referred to as modalities. Despite the extensive attention that certain parts have already received, per say the textual information, there are several aspects that severely under researched. Two such modalities are the utility of diagram images and the deep automated understanding of mathematical formulas. Inspired by existing holistic approaches to the deep understanding of technical documents, we develop a novel formal scheme for the modelling of digital diagram images. This extends to a generative framework that allows for the creation of artificial images and their annotation. We contribute on the field with the creation of a novel synthetic dataset and its generation mechanism. We propose the conversion of the pseudocode generation problem to an image captioning task and provide a family of techniques based on adaptive image partitioning. We address the mathematical formulas’ semantic understanding by conducting an evaluating survey on the field, published in May 2021. We then propose a formal synthesis framework that utilized formula graphs as metadata, reaching for novel valuable formulas. The synthesis framework is validated by a deep geometric learning mechanism, that outsources formula data to simulate the missing a priori knowledge. We close with the proof of concept, the description of the overall pipeline and our future aims