Abstract
the supervision of the academic performance of engineering students is vital during an early stage of their curricula. Indeed, their grades in specific core/major courses as well as their cumulative General Point Average (GPA) are decisive when pertaining to their ability/condition to pursue Masters’ studies or graduate from a five-year Bachelor-of-Engineering program. Furthermore, these compelling strict requirements not only significantly affect the attrition rates in engineering studies (on top of probation and suspension) but also decide of grant management, developing courseware, and scheduling of programs. In this paper, we present a study that has a twofold objective. First, it attempts at correlating the aforementioned issues with the engineering students’ performance in some key courses taken at early stages of their curricula, then, a predictive model is presented and refined in order to endow advisors and administrators with a powerful decision-making tool when tackling such highly important issues. Matlab Neural Networks Pattern Recognition tool as well as Classification and Regression Trees (CART) are fully deployed with important cross validation and testing. Simulation and prediction results demonstrated a high level of accuracy and offered efficient analysis and information pertinent to the management of engineering schools and programs in the frame of the aforementioned perspective.
Highlights
Data mining has attracted exceptionally diversified businesses for both the descriptive and predictive capabilities it promises, one of which is Education in its broad fields and organizational hierarchies [15] [36] [50]
While in the context of KDD description tends to be more important than prediction, prediction is often the primary goal in pattern recognition and machine learning applications
We carried out a study to find a reasonably accurate and reliable predictive tool that enables academicians and administrators to decide about the enrollment of engineering students in Masters’ studies or to succeed a Bachelor-of-Engineering program
Summary
Data mining has attracted exceptionally diversified businesses for both the descriptive and predictive capabilities it promises, one of which is Education in its broad fields and organizational hierarchies [15] [36] [50]. Data mining, which is the science of digging into databases for information and knowledge retrieval, has recently developed new axes of applications and engendered an emerging discipline, called Educational Data Mining or EDM. This discipline seems to be a lot promising. One can find many definitions for data mining in books, journal papers, and e-articles [11] [12] [13] [14] They all refer to data mining as a young and interdisciplinary field in computer science which is described as an interactive and iterative process aiming at sundering out/revealing hidden/unobvious, but existing, patterns, trends and/or relationships amidst data using statistical and mathematical procedures with a prime objective of providing decision support systems with information and knowledge. The core objective of the paper will be summarized and a conclusion is presented as well
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