The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

PICES Press, Vol. 17, No. 1, January 2009

Major Outcomes from the 2008 PICES Annual Meeting: A Note from the Chairman (pdf, 0.1 Mb) PICES Science – 2008 (pdf, 0.1 Mb) 2008 PICES Awards (pdf, 0.3 Mb) Charles B. Miller – A Selective Biography (pdf, 0.4 Mb) Latest and Upcoming PICES Publications (pdf, 0.1 Mb) 2008 OECOS Workshop in Dalian (pdf, 0.2 Mb) PICES Calendar (pdf, 0.1 Mb) 2008 PICES Workshop on “Climate Scenarios for Ecosystem Modeling (II)” (pdf, 0.1 Mb) PICES/ESSAS Workshop on “Marine Ecosystem Model Inter-Comparisons” (pdf, 0.2 Mb) Highlights of the PICES Seventeenth Annual Meeting (pdf, 0.5 Mb) 2008 PICES Summer School on “Ecosystem-Based Management” (pdf, 0.3 Mb) 4th PICES Workshop on “The Okhotsk Sea and Adjacent Areas” (pdf, 0.2 Mb) PICES WG 21 Rapid Assessment Surveys (pdf, 0.4 Mb) PICES Interns (pdf, 0.3 Mb) PICES @ Oceans in a High CO2 World (pdf, 0.1 Mb) Coping with Global Change in Marine Social–Ecological Systems: An International Symposium (pdf, 0.1 Mb) The State of the Western North Pacific in the First Half of 2008 (pdf, 1.3 Mb) State of the Northeast Pacific through 2008 (pdf, 0.3 Mb) The Bering Sea: Current Status and Recent Events (pdf, 0.2 Mb) An Opinion Born of Years of Observing Timeseries Observations (pdf, 0.1 Mb) New Chairman for the PICES Fishery Science Committee (pdf, 0.1 Mb

Discovery and Communication of Important Marketing Findings: Evidence and Proposals

My review of empirical research on scientific publication led to the following conclusions. Three criteria are useful for identifying whether findings are important: replication, validity, and usefulness. A fourth criterion, surprise, applies in some situations. Based on these criteria, important findings resulting from academic research in marketing seem to be rare. To a large extent, this rarity is due to a reward system that is built around subjective peer review. Rather than using peer review as a secret screening process, using an open process likely will improve papers and inform readers. Researchers, journals, business schools, funding agencies, and professional organizations can all contribute to improving the process. For example, researchers should do directed research on papers that contribute to principles. Journals should invite papers that contribute to principles. Business school administrators should reward researchers who make important findings. Funding agencies should base decisions on researchers' prior success in making important findings, and professional organizations should maintain web sites that describe what is known about principles and what research is needed on principles

The Scientist, Spring 2007

https://scholarworks.sjsu.edu/scientist/1000/thumbnail.jp

Selection of Statistical Software for Solving Big Data Problems: A Guide for Businesses, Students, and Universities

The need for analysts with expertise in big data software is becoming more apparent in today’s society. Unfortunately, the demand for these analysts far exceeds the number available. A potential way to combat this shortage is to identify the software taught in colleges or universities. This article will examine four data analysis software—Excel add-ins, SPSS, SAS, and R—and we will outline the cost, training, and statistical methods/tests/uses for each of these software. It will further explain implications for universities and future students

An investigation into the attitudes of academic librarians towards Internet plagiarism of HE students

This paper investigates the attitudes of academic librarians towards Internet plagiarism by higher education students in the United Kingdom (UK), examining how they define Internet plagiarism, their perceived role in combating this phenomenon, and the skills and techniques they have or will adopt to achieve this. A Delphi study was undertaken using a sample of 10 respondents. The responses demonstrated that plagiarism is a multifaceted term and not easily definable, however respondents were unanimous in their opinion that the Internet has made it easier to plagiarise. The potential for active collaboration between librarians and academics to jointly address Internet plagiarism was seen as vital by all respondents, although opinion was divided on the role of librarians and academics. A blended approach is recommended, which involves policing and prevention; in addition to ensuring that students are achieving information literacy well before they reach the gates of the University

PICES Press, Vol. 18, No. 2, Summer 2010

•The 2010 Inter-sessional Science Board Meeting: A Note from the Science Board Chairman (pp. 1-3) •2010 Symposium on “Effects of Climate Change on Fish and Fisheries” (pp. 4-11) •2009 Mechanism of North Pacific Low Frequency Variability Workshop (pp. 12-14) •The Fourth China-Japan-Korea GLOBEC/IMBER Symposium (pp. 15-17, 23) •2010 Sendai Ocean Acidification Workshop (pp. 18-19, 31) •2010 Sendai Coupled Climate-to-Fish-to-Fishers Models Workshop (pp. 20-21) •2010 Sendai Salmon Workshop on Climate Change (pp. 22-23) •2010 Sendai Zooplankton Workshop (pp. 24-25, 28) •2010 Sendai Workshop on “Networking across Global Marine Hotspots” (pp. 26-28) •The Ocean, Salmon, Ecology and Forecasting in 2010 (pp. 29, 44) •The State of the Northeast Pacific during the Winter of 2009/2010 (pp. 30-31) •The State of the Western North Pacific in the Second Half of 2009 (pp. 32-33) •The Bering Sea: Current Status and Recent Events (pp. 34-35, 39) •PICES Seafood Safety Project: Guatemala Training Program (pp. 36-39) •The Pacific Ocean Boundary Ecosystem and Climate Study (POBEX) (pp. 40-43) •PICES Calendar (p. 44

Forecasting inflation via electronic markets: Results from a prototype market

Forecasts of macroeconomic variables as the inflation rate serve as important guidelines for the private as well as the public sector. At least central banks that adopted an inflation targeting regime are in urgent need of high quality inflation forecasts. Accurate inflation forecasts are also needed within private sector wage negotiations. In this paper we present a new method to predict future inflation via conducting electronic markets. We show at the example of a prototype market how the market data of such an experimental market can be used to generate predictions of the future inflation rate. We also show that the market data provide important evidence on the distribution of inflation expectations. --Inflation Forcasting,Inflation Targeting,Electronic Markets