Learning Reputation in an Authorship Network

The problem of searching for experts in a given academic field is hugely important in both industry and academia. We study exactly this issue with respect to a database of authors and their publications. The idea is to use Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform topic modelling in order to find authors who have worked in a query field. We then construct a coauthorship graph and motivate the use of influence maximisation and a variety of graph centrality measures to obtain a ranked list of experts. The ranked lists are further improved using a Markov Chain-based rank aggregation approach. The complete method is readily scalable to large datasets. To demonstrate the efficacy of the approach we report on an extensive set of computational simulations using the Arnetminer dataset. An improvement in mean average precision is demonstrated over the baseline case of simply using the order of authors found by the topic models

Survey of teachers 2010 : support to improve teaching practice

In 2010 the annual survey of teachers, conducted on behalf of the General Teaching Council for England (GTC), explored teachers’ experiences of the different forms of support they receive to help them maintain and develop their teaching practice. Teachers were asked for their views on the following: • their participation in Continuing Professional Development (CPD) • their involvement in activities to improve teaching practice • use of observation and feedback • use of research • performance management, and • the professional standards

Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

Clustering documents with active learning using Wikipedia

Wikipedia has been applied as a background knowledge base to various text mining problems, but very few attempts have been made to utilize it for document clustering. In this paper we propose to exploit the semantic knowledge in Wikipedia for clustering, enabling the automatic grouping of documents with similar themes. Although clustering is intrinsically unsupervised, recent research has shown that incorporating supervision improves clustering performance, even when limited supervision is provided. The approach presented in this paper applies supervision using active learning. We first utilize Wikipedia to create a concept-based representation of a text document, with each concept associated to a Wikipedia article. We then exploit the semantic relatedness between Wikipedia concepts to find pair-wise instance-level constraints for supervised clustering, guiding clustering towards the direction indicated by the constraints. We test our approach on three standard text document datasets. Empirical results show that our basic document representation strategy yields comparable performance to previous attempts; and adding constraints improves clustering performance further by up to 20%

The engagement of mature distance students

This is an Accepted Manuscript of an article published by Taylor & Francis in Higher Education Research and Development in 2013, available online: http://www.tandfonline.com/10.1080/07294360.2013.777036.Publishe

The relationships between personality, approaches to learning, and academic success in first-year psychology distance education students

[Abstract]: The first aim of this study was to examine the relationships between the big five personality traits and approaches to learning in a sample of first-year psychology distance students. Approaches to learning are the intentions a student has when faced with a learning task. A deep approach reflects an intention to understand the material, a strategic approach reflects an intention to achieve the highest grades possible, and a surface approach reflects an intention to cope with the course requirements by memorising facts. Consistent with previous research of on-campus students, the Intellect trait predicted the deep learning approach; the Conscientiousness trait predicted the strategic learning approach; and the Emotional Stability trait negatively predicted the surface learning approach. The second aim of this study was to investigate whether approaches to learning predict academic success, as measured by grade point average. As expected, the surface learning approach negatively predicted achievement. However, contrary to expectations, neither the deep nor the strategic learning approach predicted academic success. This finding may partly be explained by these first-year distance students undergoing a transition to the expectations and requirements of their flexible learning environments. Further research is warranted to establish whether the deep and strategic learning approaches become more likely to predict academic success in the latter years of study, after distance students have adapted to the flexible delivery methods. To this end, a longitudinal study that tracks the academic performance of these students until they complete their degrees or leave the university is recommended

Active Semi-Supervised Learning Using Sampling Theory for Graph Signals

We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by the vertices of an undirected graph with the similarity between them captured by the edge weights. Given a target number of nodes to label, the goal is to choose those nodes that are most informative and then predict the unknown labels. We propose a novel framework for this problem based on our recent results on sampling theory for graph signals. A graph signal is a real-valued function defined on each node of the graph. A notion of frequency for such signals can be defined using the spectrum of the graph Laplacian matrix. The sampling theory for graph signals aims to extend the traditional Nyquist-Shannon sampling theory by allowing us to identify the class of graph signals that can be reconstructed from their values on a subset of vertices. This approach allows us to define a criterion for active learning based on sampling set selection which aims at maximizing the frequency of the signals that can be reconstructed from their samples on the set. Experiments show the effectiveness of our method.Comment: 10 pages, 6 figures, To appear in KDD'1