Ontology for Academic Program Accreditation
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 7
Abstract
Many educational institutions are adopting national and international accreditation programs to improve teaching, student learning, and curriculum. There is a growing demand across higher education for automation and helpful educational resources to continuously improve student outcomes. The student outcomes are the required knowledge and skill set that graduates of any accredited program have to gain in order entry into the workforce or for to continue with their future education. To evaluate student outcomes, each assessment activities must map to a course learning outcomes which maps students’ outcomes. The problem is that all course learning outcomes and student outcome mapping are placed in documents or database which requires more work and time to access and understand. This paper proposes an ontology based solution to enable visual discover of all course learning outcomes that maps to a particular student outcome and related assessments to help faculty or curriculum committees avoid over mapping or under mapping students’ outcomes.
Authors and Affiliations
Jehad Alomari
Mobile Software Testing: Thoughts, Strategies, Challenges, and Experimental Study
Mobile devices have become more pervasive in our daily lives, and are gradually replacing regular computers to perform traditional processes like Internet browsing, editing photos, playing videos and sound track, and rea...
A General Evaluation Framework for Text Based Conversational Agent
This paper details the development of a new evaluation framework for a text based Conversational Agent (CA). A CA is an intelligent system that handle spoken or/and text based conversations between machine and human. Gen...
Static Filtered Sky Color Constancy
In Computer Vision, the sky color is used for lighting correction, image color enhancement, horizon alignment, image indexing, and outdoor image classification and in many other applications. In this article, for robust...
Exploiting Document Level Semantics in Document Clustering
Document clustering is an unsupervised machine learning method that separates a large subject heterogeneous collection (Corpus) into smaller, more manageable, subject homogeneous collections (clusters). Traditional metho...
Iterative Removing Salt and Pepper Noise based on Neighbourhood Information
Denoising images is a classical problem in low-level computer vision. In this paper, we propose an algorithm which can remove iteratively salt and pepper noise based on neighbourhood while preserving details. First, we c...