Evaluation of the Project P.A.T.H.S. Using Multiple Evaluation Strategies

The aims of Vocational Colleges (VCs) are to give training and impart necessary skills leading to the production of craftsmen who will be enterprising and self-reliant. Many developing countries face the problem of unemployment among graduates; this may not be far from curricula modules mismatching job requirements. The scenario of low participation of private sector in skills development of graduates exists in Nigeria as a result of which the needs of local industries is not met. The objective of this paper is to compare the curriculum framework, courses/subjects-matter, aims, modes of transaction, and evaluation strategies in VCs in Malaysia and Nigeria. The methodology used in this study involved gathering previous studies on comparative analysis in education through Google Scholar, Science Direct, and JSTOR. Related Procedia were also retrieved from Elsevier. Literatures show that students have to adapt with 21st century knowledge, skills, innovative practice and competence as key points to job creation and wealth generation. The findings of this paper show that the curriculum structures in VCs in Malaysia are more updated than in Nigeria. However, curricula in VCs in Malaysia and Nigeria still need some improvements in entrepreneurship skills. This is necessary for effective transmission of knowledge and skills from school to work environment in the 21st century.

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Cost-Effective Live-Fire Test and Evaluation Strategies: The Missions and Means Framework

10.21236/ada460552 ◽

2006 ◽

Author(s):

Martha K. Nelson ◽

Dennis C. Bely

Keyword(s):

Fire Test ◽

Cost Effective ◽

Test And Evaluation ◽

Evaluation Strategies

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Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

Current Pharmaceutical Design ◽

10.2174/1381612826666201124112710 ◽

2020 ◽

Vol 26 ◽

Author(s):

Xiaoping Min ◽

Fengqing Lu ◽

Chunyan Li

Keyword(s):

Gene Expression ◽

Deep Learning ◽

Dna Sequences ◽

Experimental Methods ◽

Learning Methods ◽

Comprehensive Review ◽

Genomic Features ◽

Disease Mechanisms ◽

Evaluation Strategies ◽

Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

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