Amin Y. Noaman

Early dropout prediction using data mining: a case study with high school students

Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well-known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4-6weeks of the course and trustworthy enough to be used in an early warning system.

Saudi-KAU Coupled Global Climate Model: Description and Performance

BackgroundA new coupled global climate model (CGCM) has been developed at the Center of Excellence for Climate Change Research (CECCR), King Abdulaziz University (KAU), known as Saudi-KAU CGCM.PurposeThe main aim of the model development is to generate seasonal to subseasonal forecasting and long-term climate simulations.MethodsThe Saudi-KAU CGCM currently includes two atmospheric dynamical cores, two land components, three ocean components, and multiple physical parameterization options. The component modules and parameterization schemes have been adopted from different sources, and some have undergone modifications at CECCR. The model is characterized by its versatility, ease of use, and the physical fidelity of its climate simulations, in both idealized and realistic configurations. A description of the model, its component packages, and parameterizations is provided.ResultsResults from selected configurations demonstrate the model’s ability to reasonably simulate the climate on different time scales. The coupled model simulates El Niño-Southern Oscillation (ENSO) variability, which is fundamental for seasonal forecasting. It also simulates Madden-Julian Oscillation (MJO)-like disturbances with features similar to observations, although slightly weaker.ConclusionsThe Saudi-KAU CGCM ability to simulate the ENSO and the MJO suggests that it is capable of making useful predictions on subseasonal to seasonal timescales.

Higher education quality assessment model: towards achieving educational quality standard

This paper presents a developed higher education quality assessment model (HEQAM) that can be applied for enhancement of university services. This is because there is no universal unified quality standard model that can be used to assess the quality criteria of higher education institutes. The analytical hierarchy process is used to identify the priority and weights of the model criteria and their alternatives. The model has 3 levels with 8 main objectives and 53 alternatives. It included e-services criteria, which is one of the recent modern university components, in addition to new sub-criteria for enhancing the model. It produces important recommendations for university higher authorities for achieving demanded quality services. A questionnaire was developed to examine the quality criteria for evaluating the model at King Abdulaziz University, as an applied case study. The model proposed is flexible and can be applied in many other universities.

Recommending degree studies according to students’ attitudes in high school by means of subgroup discovery

AbstractThe transition from high school to university is a critical step and many students head toward failure just because their final degree option was not the right choice. Both students’ preferences and skills play an important role in choosing the degree that best fits them, so an analysis of these attitudes during the high school can minimize the drop out in a posteriori learning period like university. We propose a subgroup discovery algorithm based on grammars to extract itemsets and relationships that represent any type of homogeneity and regularity in data from a supervised context. This supervised context is cornerstone, considering a single item or a set of them as interesting and distinctive. The proposed algorithm supports the students’ final degree decision by extracting relations among different students’ skills and preferences during the high school period. The idea is to be able to provide advices with regard to what is the best degree option for each specific skill and...

A Nonlinear Goal Programming Model for University Admission Capacity Planning with Modified Differential Evolution Algorithm

This paper proposes a nonlinear Goal Programming Model (GPM) for solving the problem of admission capacity planning in academic universities. Many factors of university admission capacity planning have been taken into consideration among which are number of admitted students in the past years, total population in the country, number of graduates from secondary schools, desired ratios of specific specialties, faculty-to-students ratio, and the past number of graduates. The proposed model is general and has been tested at King Abdulaziz University (KAU) in the Kingdom of Saudi Arabia, where the work aims to achieve the key objectives of a five-year development plan in addition to a 25-year future plan (AAFAQ) for universities education in the Kingdom. Based on the results of this test, the proposed GPM with a modified differential evolution algorithm has approved an ability to solve general admission capacity planning problem in terms of high quality, rapid convergence speed, efficiency, and robustness.

An Algorithm Evaluation for Discovering Classification Rules with Gene Expression Programming

AbstractIn recent years, evolutionary algorithms have been used for classification tasks. However, only a limited number of comparisons exist between classification genetic rule-based systems and gene expression programming rule-based systems. In this paper, a new algorithm for classification using gene expression programming is proposed to accomplish this task, which was compared with several classical state-of-the-art rule-based classifiers. The proposed classifier uses a Michigan approach; the evolutionary process with elitism is guided by a token competition that improves the exploration of fitness surface. Individuals that cover instances, covered previously by others individuals, are penalized. The fitness function is constructed by the multiplying three factors: sensibility, specificity and simplicity. The classifier was constructed as a decision list, sorted by the positive predictive value. The most numerous class was used as the default class. Until now, only numerical attributes are allowed and...

A STUDY TO ASSESS THE KNOWLEDGE OF THE STAFF NURSES REGARDING CARE OF UNCONSCIOUS PATIENTS IN A SELECTED HOSPITAL, MANGLORE

In this paper we introduce an integration system that consists of two subsystems (tools): integration sub-system (tool) and query (sub-system) tool. The integration tool has been built for integrating data from different data stores (databases) that were created with different database engines. The query sub-system (tool) has been built to help a user to query in a structured natural language or structured query language. The integration system has been built based on the web services technology to be adaptable, reusable, maintainable, and distributed. The integration subsystem collects data from heterogeneous data sources, unifies them based on ontology and stores the unified data in a data warehousing, which its schema is generated automatically by the tool. The integration tool is a database engine independent, domain independent and based on ontology scheme. The query tool has been built to accept the requests from a user and manipulate data in the data warehouse and return the results to the user. The query tool generates queries automatically based on the user requirements and data warehouse schema. The user can write his query as structured natural language or structured query language. The system has been implemented and tested.

Agent-based Managing for Grid Cloud System — Design and Prototypal Implementation

Here, we present the design and architecture of an Agent-based Manager for Grid Cloud Systems (AMGCS) using software agents to ensure independency and scalability when the number of resources and jobs increase. AMGCS handles IaaS resources (Infrastructure-as-a-Service — compute, storage and physical resources), and schedules compute-intensive jobs for execution over available resources based on QoS criteria, with optimized task-execution and high resource-utilization, through the capabilities of grid clouds. This prototypal design and implementation has been tested and shown a proven ability to increase the reliability and performance of cloud application by distributing its tasks to more than one cloud system, hence increase the reliability of user jobs and complex tasks submitted from regular machines.

Software Tool to Support the Improvement of the Collaborative Learning Process

Computer supported collaborative learning brings together the same characteristics and qualities of traditional learning, and includes benefits at the level of interaction and collective learning, as well as the inclusion of a motivating element associated with technology, which allows monitoring more detailed, incorporate an activities record, guide, evaluate and observe the process that is executing in a collaborative activity. However, one of its main problems are caused by a lack of software tools to guarantee effective collaboration, to support the monitoring and evaluation of the process in each of its phases (Pre-Process, Process and Post-Process), and provide a compendium of mechanisms that allow the execution of a collaborative activity and increase collaboration among participants. In this paper, the MEPAC (Monitoreo y Evaluacion del Proceso de Aprendizaje Colaborativo) software tool is presented to support the improvement of the collaborative learning process in each of its phases, through the integration of monitoring and evaluation. The evaluation of the MEPAC usefulness, applicability and complexity through a case study, allowed us to conclude that the development of collaborative learning activities is suitable, using monitoring and evaluation mechanisms, thus improving the collaboration between participants.

SCADM: A Developed Smart Classroom Acoustics Design Model for Enhancing Educational Environment@@@نموذج مطور لتصميم الصوتيات داخل الفصول الدراسية الذكية لتحسين البيئة التعليمية

The quality and efficiency of learning and teaching processes in academic universities depend on many important factors. Acoustic quality within the learning environment of the classroom and its surroundings is one of the most important factors that affect the quality of the learning process. Several studies and researches focused on the subject of acoustical comfort in university classrooms. In this paper we study smart classroom acoustics design issues required to achieve high quality acoustics conditions. These designing issues include inside and outside noise sources, designing for optimum reverberation time, selection of sound insulation and acoustical treatment materials, designing for speech intelligibility, and auralization inside the KAU classrooms. The aim is to suggest a developed smart classroom acoustics design model (SCADM) that can be used by architects, acoustics engineers and designers in an early stage of classroom design in order to achieve the acoustical conditions of KAU university classrooms. The goal of this research is to raise the quality and efficiency of the educational environment to reach an excellent learning environment, and hence increasing students learning outcomes.