Drahomira Herrmannova

Predicting Student Performance from Combined Data Sources

This chapter will explore the use of predictive modeling methods for identifying students who will benefit most from tutor interventions. This is a growing area of research and is especially useful in distance learning where tutors and students do not meet face to face. The methods discussed will include decision-tree classification, support vector machine (SVM), general unary hypotheses automaton (GUHA), Bayesian networks, and linear and logistic regression. These methods have been trialed through building and testing predictive models using data from several Open University (OU) modules. The Open University offers a good test-bed for this work, as it is one of the largest distance learning institutions in Europe. The chapter will discuss how the predictive capacity of the different sources of data changes as the course progresses. It will also highlight the importance of understanding how a student’s pattern of behavior changes during the course.

Visual Search for Supporting Content Exploration in Large Document Collections

In recent years a number of new approaches for visualising and browsing document collections have been developed. These approaches try to address the problems associated with the growing amounts of content available and the changing patterns in the way people interact with information. Users now demand better support for exploring document collections to discover connections, compare and contrast information. Although visual search interfaces have the potential to improve the user experience in exploring document collections compared to textual search interfaces, they have not yet become as popular among users. The reasons for this range from the design of such visual interfaces to the way these interfaces are implemented and used. In this paper we study these reasons and determine the factors that contribute to an improved visual browsing experience. Consequently, by taking these factors into account, we propose a novel visual search interface that improves exploratory search and the discovery of document relations. We explain our universal approach, and how it could be applied to any document collection, such as news articles, cultural heritage artifacts or research papers.

An Analysis of the Microsoft Academic Graph

In this paper we analyse a new dataset of scholarly publications, the Microsoft Academic Graph (MAG). The MAG is a heterogeneous graph comprised of over 120 million publication entities and related authors, institutions, venues and fields of study. It is also the largest publicly available dataset of citation data. As such, it is an important resource for scholarly communications research. As the dataset is assembled using automatic methods, it is important to understand its strengths and limitations, especially whether there is any noise or bias in the data, before applying it to a particular task. This article studies the characteristics of the dataset and provides a correlation analysis with other publicly available research publication datasets to answer these questions. Our results show that the citation data and publication metadata correlate well with external datasets. The MAG also has very good coverage across different domains with a slight bias towards technical disciplines. On the other hand, there are certain limitations to completeness. Only 30 million papers out of 127 million have some citation data. While these papers have a good mean total citation count that is consistent with expectations, there is some level of noise when extreme values are considered. Other current limitations of MAG are the availability of affiliation information, as only 22 million papers have these data, and the normalisation of institution names.

Citations and Readership are Poor Indicators of Research Excellence: Introducing TrueImpactDataset, a New Dataset for Validating Research Evaluation Metrics

In this paper we show that citation counts and Mendeley readership are poor indicators of research excellence. Our experimental design builds on the assumption that a good evaluation metric should be able to distinguish publications that have changed a research field from those that have not. The experiment has been conducted on a new dataset for bibliometric research which we call TrueImpactDataset. TrueImpactDataset is a collection of research publications of two types -- research papers which are considered seminal work in their area and papers which provide a survey (a literature review) of a research area. The dataset also contains related metadata, which include DOIs, titles, authors and abstracts. We describe how the dataset was built and provide overview statistics of the dataset. We propose to use the dataset for validating research evaluation metrics. By using this data, we show that widely used research metrics only poorly distinguish excellent research.

Semantometrics: Towards Fulltext-based Research Evaluation

Over the recent years, there has been a growing interest in developing new research evaluation methods that could go beyond the traditional citation-based metrics. This interest is motivated on one side by the wider availability or even emergence of new information evidencing research performance, such as article downloads, views and Twitter mentions, and on the other side by the continued frustrations and problems surrounding the application of purely citation-based metrics to evaluate research performance in practice. Semantometrics are a new class of research evaluation metrics which build on the premise that full-text is needed to assess the value of a publication. This paper reports on the analysis carried out with the aim to investigate the properties of the semantometric contribution measure [1], which uses semantic similarity of publications to estimate research contribution, and provides a comparative study of the contribution measure with traditional bibliometric measures based on citation counting.

Do citations and readership identify seminal publications

This work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline.

Workshop on Scholarly Web Mining (SWM 2017)

Researchers increasingly report their results through online publications, from research papers, data and software to experiments, observations and ideas. Immense amount of research-related data is available on the web on interlinked pages, in repositories, databases, social networking sites, etc. Consequently, researchers rely on online sources, often through search engines, to perform literature searches for their research âĂŤ to search for papers, topics, people etc. to be able to produce new research. However, these publications can be used not only for traditional literature searches, but also as a source for discovering popular and emerging research topics, key publications and people or evaluating research excellence. To aid research, it is important to leverage the potential of data mining technologies to improve the process of how research is being done. This workshop aims to bring together people from different backgrounds who are interested in analysing and mining scholarly data available via web and social media sources using various approaches such as query log mining, graph analysis, text mining, etc., and/or who develop systems that enable such analysis and mining. The topics of this workshop include, but are not limited to, the following areas:

5th International Workshop on Mining Scientific Publications (WOSP 2016)

Digital libraries that store scientific publications are becoming increasingly central to the research process. They are not only used for traditional tasks, such as finding and storing research outputs, but also as a source for discovering new research trends or evaluating research excellence. With the current growth of scientific publications deposited in digital libraries, it is no longer sufficient to provide only access to content. To aid research, it is especially important to leverage the potential of text and data mining technologies to improve the process of how research is being done.