Expansion vs. Quality: Emerging Issues of For-Profit Private Higher Education Institutions in Ethiopia

Private for-profit higher education has been rapidly expanding in developing countries worldwide since the early 1990s. This global trend has been particularly evident in Ethiopia, where only three public universities existed until 1996. By 2005, about 60 private for-profit higher education institutions had been founded in Ethiopia. This has led to mixed feelings among the Ethiopian public. While some laud the opportunities and advantages these new institutions bring, others are apprehensive that the quality of education might be compromised by an expansion motivated by monetary gain. This article sheds light on these paradoxes and provides suggestions for policy and practices.     

Perceived Causes of Mental Health Problems and Help-Seeking Behavior among University Students in Ethiopia

The study examined perceived causes of mental health problems and professional help-seeking behavior among university students in Ethiopia. Data were collected from 370 students from four randomly selected colleges. The results revealed that the majority of the participants were able to recognize major mental health problems such as schizophrenia and major depressive disorder. They attributed mental health problems to psychosocial and biomedical factors and most of the participants had positive attitudes toward seeking professional psychological help.     

Approaching lean product development using system dynamics：investigating front-load effects

Competing with successful products has become perplexing with several uncertainties and transmutes from time to time as customers＇ expectations are dynamic.That is why manufacturing firms exhaustively strive to look for a better competitive frontier using wellestablished and innovative product development（PD）processes.In this paper,we would like to answer three research questions：（i）What would be the effects of frontloading in PD？（ii）Can we improve our PD process endlessly？（iii）When is the critical time that the firm should take remedial action for improvements？As a contribution to the vast numbers of improvement methods in new product development（NPD）,this paper investigates the effects of front-loading using set-based concurrent engineering（SBCE）on cost and lead time.Models are developed and treated using a system dynamics（SD）approach.We assign a hypothetical upfront investment for SBCE and compare its effects on total cost and lead time of the development process.From the research,it is found that the total cost of PD is reduced almost by half-although the front loading is higher in order to encompass multiple design alternatives.The total product lead time is reduced by almost 20%.The model reveals the critical time for improvement of the PD process.We use SD tool（e.g.,STELLA）for simulation and visualization of the complex PD model,using SBCE as one of several strategies to frontload activities in the NPD process.     

Classifying Amharic webnews

We present work aimed at compiling an Amharic corpus from the Web and automatically categorizing the texts. Amharic is the second most spoken Semitic language in the World (after Arabic) and used for countrywide communication in Ethiopia. It is highly inflectional and quite dialectally diversified. We discuss the issues of compiling and annotating a corpus of Amharic news articles from the Web. This corpus was then used in three sets of text classification experiments. Working with a less-researched language highlights a number of practical issues that might otherwise receive less attention or go unnoticed. The purpose of the experiments has not primarily been to develop a cutting-edge text classification system for Amharic, but rather to put the spotlight on some of these issues. The first two sets of experiments investigated the use of Self-Organizing Maps (SOMs) for document classification. Testing on small datasets, we first looked at classifying unseen data into 10 predefined categories of news items, and then at clustering it around query content, when taking 16 queries as class labels. The second set of experiments investigated the effect of operations such as stemming and part-of-speech tagging on text classification performance. We compared three representations while constructing classification models based on bagging of decision trees for the 10 predefined news categories. The best accuracy was achieved using the full text as representation. A representation using only the nouns performed almost equally well, confirming the assumption that most of the information required for distinguishing between various categories actually is contained in the nouns, while stemming did not have much effect on the performance of the classifier.