1.1. Project Context
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning and self-correction. Learning process is the acquisition of information and rules for using information while reasoning is the process in using the rules to reach approximate or definite conclusions. Two common applications of AI that figured out in this development are the Knowledge-based Systems and Decision Support System (DSS). Knowledge-based system is a computer system that is programmed to imitate human problem solving by means of artificial intelligence and reference to a database of knowledge on a particular subject. On the other hand, DSS refers to an interactive computerized system that gathers and presents data from a wide range of sources. DSS applications are systems and subsystems that help people make decisions based on the data that is culled from a wide range of sources. The DSS will collect and analyze the data then present it in a way that can be interpreted by humans. Turban (1995) defines it more specifically as "an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a non-structured problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker's own insights." These two definitions are what defines the type of DSS to be developed in the study, one that utilizes captured data and aids the user make appropriate decisions based on logical comparison of alternatives. Generally, DSS is a collection of integrated software applications and hardware that form the backbone of an organization’s decision making process. Companies across all industries rely on decision support tools, techniques, and models to help them assess and resolve everyday business questions. The decision support system is data-driven, as the entire process feeds off of the collection and availability of data to analyze. Business Intelligence (BI) reporting tools, processes, and methodologies are key components to any decision support system and provide end users with rich reporting, monitoring, and data analysis. The Student Academic Advising System (SAAS) is a decision support system designed to facilitate student mentoring and advising on what course/subject they would take. The system provides access to students' current term enrollment, GPA (by term and cumulative), units earned, degree audit options, and the ability to record and view notes from mentoring and advising sessions. SAAS includes the ability to grades, earned units and other academic information for individual students. The system also allows advisors to run a degree audit against the student's major or a prospective major, and record notes for future use.
1.2. Purpose and Description
Student Academic Advising System (SAAS) is designed to help program heads and academic coordinators in giving students loads for enrollment. The system helps the programs head in deciding what subject a student must take depends on their course and academic status.
Furthermore, this study proposes to formulate a decision rules that helps the system to (i) decide on what subject must be taken by the students and (ii) to make the advising fast by the help of SAAS.
1.3. Objectives of the study
The general objective of this study is to design and implement an computer automated decision rules for Student Academic Advising System (SAAS). Specifically, this study aims to:
a. make a graphical user interface for the automated scheduling system for user inter action; b. conduct evaluation of the performances of the proposed system; and, c. lessen man-power workloads and minimize time consuming enrollment activities using proposed system;
1.4. Scope and Limitations
This study focuses on Student Academic...
Please join StudyMode to read the full document