The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student’s likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those “at risk” of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially “at risk.”.Peer reviewe

The UK Clinical Research Collaboration (UKCRC) Tissue Directory and Coordination Centre: the UK’s centre for facilitating the usage of human samples for medical research

The UKCRC Tissue Directory and Coordination Centre was established to improve access to and utilisation of UK human tissue samples for medical research. The key output of the Centre is the creation of the UK’s first pan-disease Tissue Directory (https://directory.biobankinguk.org/). Any researcher can search the Directory based on a series of simple key words including disease classification, age, sex, sample type, preservation details, quality indicators and datasets available. The Directory as of April 2017 contains 100 Bioresources. Researchers seeking fresh samples can also search for facilities that offer bespoke collection services. Future work of the Centre will be to explore greater standardisation of biobanking activities across the UK and to facilitate an inter-connected research infrastructure related to the use of human biosamples

An intelligent assistant for exploratory data analysis

In this paper we present an account of the main features of SNOUT, an intelligent assistant for exploratory data analysis (EDA) of social science survey data that incorporates a range of data mining techniques. EDA has much in common with existing data mining techniques: its main objective is to help an investigator reach an understanding of the important relationships ina data set rather than simply develop predictive models for selectd variables. Brief descriptions of a number of novel techniques developed for use in SNOUT are presented. These include heuristic variable level inference and classification, automatic category formation, the use of similarity trees to identify groups of related variables, interactive decision tree construction and model selection using a genetic algorithm

Neutron scattering in a d_{x^2-y^2}-wave superconductor with strong impurity scattering and Coulomb correlations

We calculate the spin susceptibility at and below T_c for a d_{x^2-y^2}-wave superconductor with resonant impurity scattering and Coulomb correlations. Both the impurity scattering and the Coulomb correlations act to maintain peaks in the spin susceptibility, as a function of momentum, at the Brillouin zone edge. These peaks would otherwise be suppressed by the superconducting gap. The predicted amount of suppression of the spin susceptibility in the superconducting state compared to the normal state is in qualitative agreement with results from recent magnetic neutron scattering experiments on La_{1.86}Sr_{0.14}CuO_4 for momentum values at the zone edge and along the zone diagonal. The predicted peak widths in the superconducting state, however, are narrower than those in the normal state, a narrowing which has not been observed experimentally.Comment: 24 pages (12 tarred-compressed-uuencoded Postscript figures), REVTeX 3.0 with epsf macros, UCSBTH-94-1

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Cluster analyses of the TCGA and a TMA dataset using the coexpression of HSP27 and CRYAB improves alignment with clinical-pathological parameters of breast cancer and suggests different epichaperome influences for each sHSP

Our cluster analysis of the Cancer Genome Atlas for co-expression of HSP27 and CRYAB in breast cancer patients identified three patient groups based on their expression level combination (high HSP27 + low CRYAB; low HSP27 + high CRYAB; similar HSP27 + CRYAB). Our analyses also suggest that there is a statistically significant inverse relationship between HSP27 and CRYAB and known clinicopathological markers in breast cancer. Screening an unbiased 248 breast cancer patient tissue microarray (TMA) for the protein expression of HSP27 and phosphorylated HSP27 (HSP27-82pS) with CRYAB also identified three patient groups based on HSP27 and CRYAB expression levels. TMA24 also had recorded clinical-pathological parameters, such as ER and PR receptor status, patient survival, and TP53 mutation status. High HSP27 protein levels were significant with ER and PR expression. HSP27-82pS associated with the best patient survival (Log Rank test). High CRYAB expression in combination with wild-type TP53 was significant for patient survival, but a different patient outcome was observed when mutant TP53 was combined with high CRYAB expression. Our data suggest that HSP27 and CRYAB have different epichaperome influences in breast cancer, but more importantly evidence the value of a cluster analysis that considers their coexpression. Our approach can deliver convergence for archival datasets as well as those from recent treatment and patient cohorts and can align HSP27 and CRYAB expression to important clinical-pathological features of breast cancer

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

Infrared conductivity of a d_{x^2-y^2}-wave superconductor with impurity and spin-fluctuation scattering

Calculations are presented of the in-plane far-infrared conductivity of a d_{x^2-y^2}-wave superconductor, incorporating elastic scattering due to impurities and inelastic scattering due to spin fluctuations. The impurity scattering is modeled by short-range potential scattering with arbitrary phase shift, while scattering due to spin fluctuations is calculated within a weak-coupling Hubbard model picture. The conductivity is characterized by a low-temperature residual Drude feature whose height and weight are controlled by impurity scattering, as well as a broad peak centered at 4 Delta_0 arising from clean-limit inelastic processes. Results are in qualitative agreement with experiment despite missing spectral weight at high energies.Comment: 29 pages (11 tar-compressed-uuencoded Postscript figures), REVTeX 3.0 with epsf macro

Different Approaches to Community Evolution Prediction in Blogosphere

Predicting the future direction of community evolution is a problem with high theoretical and practical significance. It allows to determine which characteristics describing communities have importance from the point of view of their future behaviour. Knowledge about the probable future career of the community aids in the decision concerning investing in contact with members of a given community and carrying out actions to achieve a key position in it. It also allows to determine effective ways of forming opinions or to protect group participants against such activities. In the paper, a new approach to group identification and prediction of future events is presented together with the comparison to existing method. Performed experiments prove a high quality of prediction results. Comparison to previous studies shows that using many measures to describe the group profile, and in consequence as a classifier input, can improve predictions.Comment: SNAA2013 at ASONAM2013 IEEE Computer Societ