In the universities where students have a chance to select and enroll in a particular course, they require special support to avoid the wrong combination of courses that might lead to delay their study. Analysis shows that the students' selection is mainly influenced by list of factors which we categorized them into three groups of concern: course factors, social factors, and individual factors. This paper proposed a two-phased model where the most correlated courses are generated and prioritized based on the student preferences. At this end, we have applied the multi-criteria analytic hierarchy process (MC-AHP) in order to generate the optimum set of courses from the available courses pool. To validate the model, we applied it to the data from students of the Information System Department at Taibah University, Kingdom of Saudi Arabia.
The study aimed at investigating the levels of geometrical thinking among students receiving blended learning in Jordan. The study sample consisted of (104) students/teachers of open education systems in Jordan for the 2015-2016 academic year. In order to answer the questions of study, the researcher developed a scale of geometric thinking, it is validity and reliability has been verified. The results of the study showed a low level of geometrical thinking among students receiving blended learning. The percentage of students in the first level (visual) (51%), the percentage of students in the second level (descriptive) (15%), and the percentage of students in the third level (logical) (3%). Also it showed differences in the levels of geometric thinking between males and females in favor of males, as well as differences in the level of geometrical thinking between students from the scientific stream and students from the literary stream in favor of scientific stream. In the light of the results of the study, the researcher recommends that pre-service teachers should be trained on programs contains geometrical thinking at open learning universities.
The Effectiveness of Problem based learning Model to improve the understandin...AI Publications
ย
The aim of the study is to find out the ability to understand mathematical concepts of teachers prospective in Mathematics Education Program FKIP University Nommensen in the fractional count operations material. This research is a descriptive study with the aim of the study describing the effectiveness of the Problem Based Learning Model. The results of the second trial described that the effectiveness of the Problem Based Learning Model was concluded: (i) the percentage of the students who understood the concept at least 85.29% or 29 students out of 34 students who took the test. (ii) the achievement of the ideal percentage of time for each category of student activity, (iii) the average of the lecturer ability to manage learning is 3.54, including the good category, (iv) student responses to the components and learning activities are positive is 90%.
Outcome-based education (OBE) is an educational theory that bases each part of an educational system around goals (outcomes). By the end of the educational experience, each student should have achieved the goal.
The study aimed at investigating the levels of geometrical thinking among students receiving blended learning in Jordan. The study sample consisted of (104) students/teachers of open education systems in Jordan for the 2015-2016 academic year. In order to answer the questions of study, the researcher developed a scale of geometric thinking, it is validity and reliability has been verified. The results of the study showed a low level of geometrical thinking among students receiving blended learning. The percentage of students in the first level (visual) (51%), the percentage of students in the second level (descriptive) (15%), and the percentage of students in the third level (logical) (3%). Also it showed differences in the levels of geometric thinking between males and females in favor of males, as well as differences in the level of geometrical thinking between students from the scientific stream and students from the literary stream in favor of scientific stream. In the light of the results of the study, the researcher recommends that pre-service teachers should be trained on programs contains geometrical thinking at open learning universities.
The Effectiveness of Problem based learning Model to improve the understandin...AI Publications
ย
The aim of the study is to find out the ability to understand mathematical concepts of teachers prospective in Mathematics Education Program FKIP University Nommensen in the fractional count operations material. This research is a descriptive study with the aim of the study describing the effectiveness of the Problem Based Learning Model. The results of the second trial described that the effectiveness of the Problem Based Learning Model was concluded: (i) the percentage of the students who understood the concept at least 85.29% or 29 students out of 34 students who took the test. (ii) the achievement of the ideal percentage of time for each category of student activity, (iii) the average of the lecturer ability to manage learning is 3.54, including the good category, (iv) student responses to the components and learning activities are positive is 90%.
Outcome-based education (OBE) is an educational theory that bases each part of an educational system around goals (outcomes). By the end of the educational experience, each student should have achieved the goal.
This research is aimed to know studentsโ concept understanding profile on heat and temperature. This research is descriptive research using a qualitative approach. Subjects of this research are 10th-grade students in Surakarta in the academic year of 2016/ 2017. They represent high, medium, and low categorized school. Subject selection is based on the average score of physics at the latest four years. This research uses the question of extended response test through essay question. Before having given to the subjects, essay questions are validated by the experts. Based on the research result and the data analysis, studentsโ concept understanding on heat and temperature as follows: (1) The average percentage of studentsโ concept understanding at high categorized school is 60,66%, at medium categorized school is 51,47%, and at low categorized school is 48,83%, (2) Most students have misconception on basic concept of heat and temperature such as heat capacity, thermal conductivity, and expansion concept.
The purpose of this research is to analyze the improvement of students' mathematical literacy ability through the use of mathematics teaching materials with metacognitive approach guidance. This research will be held in the city of Kendari to the subject of this research target is students who are at grade 5 Land in Junior High VIIID Kendari years lessons 2017/2018 with many limited scale trial class is only required as much as 1 class. To know the significance of the increase in the literacy abilities of students using paired t-test. Data processing using the SPSS program with criteria if ฮฑ=0,05 then there is an increased of student's mathematical literacy ability. The results of the analysis on the stages of the evaluation shows the learning materials with metacognitive approach guidance can provide better against an increase in student learning. The ability of the early mathematical literacy against students is very less because of learning during this time students have not been directed with the ability of mathematical literacy. After the students get learning by using learning materials through metacognitive approach guidance, the ability of mathematical literacy studentsโ level 3 and level 4 underwent significant improvement.
Project-Based Learning Guided Lesson Study Improve the Achievement of Learnin...iosrjce
ย
The research impact project-based learning guided lesson study in Seminar Accounting Education
course has been conducted in 2012. The research was focused to improve student achievement of learning
outcomes in Seminar Accounting Education course at Department of Accounting, State University of Malang.
The learning outcome were defined on skill levels, the exploration ability and reviewing issues (contemporary)
in the field of accounting education both conceptual and factual, creating of research proposal, and final grade.
The research approach was classroom action research guided lesson study. The data were analyzed by
comparing student score with the minimum requirement score and the improvement of score from cycle 1 to
cycle 2. The implementation of project-based learning guided lesson study improved the ability of student to
create research proposal. The average score achieved by the students has surpassed the minimum requirement
(75) in the cycle 1 and 84% of the students have surpassed the minimum requirement, and in the cycle 2, 100%
of the students have surpassed the minimum requirement.
Students Effort to Improve Learning Results by Using Quantum Learning Method ...AI Publications
ย
This research aims to determine student learning outcomes by applying Quantum Learning method of prism and pyramid by eight grade students of SMP Negeri 2 Sipoholon in academic year 2017/2018. This type of research is Class Action Research (CAR). The subjects in this study were eight grade students of SMP Negeri 2 Sipoholon in academic year 2017/2018 were 28 student. The objective of this research is improving student learning outcomes. Instruments used were: description tests, observation sheets and interviews. Descriptive tests use to find out student learning outcomes, observation sheets use to find out ability of the teacher to apply learning and interviews to find out where the students are wrong. The average initial test score was 35.46 and the classical completeness was 0%. The average value of the first cycle was 59.78. Students who completed were 11 students (39.29%), 17 students (60.71%) were not finished, and the implementation of learning was in a less category (value 2.00). The increase in classical completeness was 39.29% and the average increase was 24.32 from the results of the initial tests. The average value of the second cycle was 76.04 and 25 students (89.29%) from 28 students had achieved mastery learning while the other 3 students (10.71%) had not yet completed, and the learning went well (average 3, 00). Cycle II has achieved classical completeness. Classical completeness has increased by 50% and the average has increased by 16.25 from cycle I. Thus the Quantum Learning method can improve student learning outcomes on the Prism and Piramid by eight grade students of SMP Negeri 2 Sipoholon in academic year 2017/2018.
Metacognitive Strategies: Instructional Approaches in Teaching and Learning o...IJAEMSJORNAL
ย
The purpose of the study is to determine the effectiveness of the metacognitive strategies as instructional approaches in teaching and learning of Basic Calculus. A number of 48 students consisting of 24 boys and 24 girls were purposively sampled in this study. Pretest-posttest quasi experimental research design was used which applied t-test and descriptive statistics. Both groups were subject to two instruments that were comprised of problem-solving test (pretest and posttest) and observation guide. Experimental group was taught Basic Calculus using metacognitive strategies while the control group was taught Basic Calculus using traditional teaching strategies. Both groups were subject to a pretest. Class observation was done while the two teaching strategies were applied. In the end, the posttest was administered to both groups to identify the effectiveness of the two teaching strategies. The data gathered were treated using paired sample t-test and independent sample t-test. The results of the study showed that the experimental group had significantly higher posttest scores as compared to control group which proved that metacognitive teaching strategies were more effective in improving the performance and problem-solving skills of the students than the traditional teaching strategies. It was also observed that students who taught using metacognitive strategies helped the students to be extremely engaged in Basic Calculus lessons cognitively, behaviorally, and affectively. The study reveals that the significant increase of the studentsโ learning engagement in Basic Calculus lessons led the students to a corresponding increase in their posttest scores.
The Empirical Analysis of Curriculum Quality Evaluation Based on Students Eva...ijtsrd
ย
The data of curriculum quality evaluation based on students evaluation of teaching contains learners perception of curriculum quality is helpful to provide reference for the improvement of curriculum quality. The evaluation system of curriculum quality should be built around ten evaluation dimensions, such as teaching objectives, subject frontier, case teaching, teacher literacy, classroom discussion, teaching methods, teaching courseware, information technology, teachers Q and A, curriculum ideological and political. Taking the students of school of physics and electronic information of Nanchang Normal University as the research object, and the sample size of curriculum quality evaluation is 5443, the Pearson correlation analysis is carried out on ten evaluation dimensions, and the correlation coefficient is 0.770, which indicates strong positive correlation. A regression model with ten evaluation dimensions as independent variables and curriculum satisfaction as dependent variables is established. The resolution of the model is 77.4 , which can predict curriculum satisfaction, and which has theoretical support for teachers to pay attention to the curriculum quality and improve the curriculum quality. It emphasizes that teaching department should pay attention to the effect of evaluation results on the continuous improvement of curriculum quality. It is suggested that a substantial and effective mechanism for continuous improvement should be established. Chen Rong "The Empirical Analysis of Curriculum Quality Evaluation Based on Students' Evaluation of Teaching" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38156.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38156/the-empirical-analysis-of-curriculum-quality-evaluation-based-on-students-evaluation-of-teaching/chen-rong
EFFECTIVE COVERAGE OF LEARNING DOMAINS IN STUDENTS ASSESSMENT BY LECTURERS OF...ijejournal
ย
The study investigated the degree of coverage of the three domains of learning during assessment of students by lecturers in Cross River University of Technology Calabar, Nigeria. The three learning domains in education are the cognitive, affective and psychomotor domains. Cognitive learning domain behavior involves thinking, reasoning, memorizing, comprehension, calculation, analyzing. Affective learning involves feeling, valuing, interest, responding. While psychomotor includes physical activities such as coordination and use of motor skills measured in speed, precision, distance and procedures. Two
hypotheses were formulated to test the expected level coverage of learning domains by lecturers in Cross River University of Technology. The study population comprised all academic staff in Cross River University of Technology, and 100 lecturers were randomly selected as sample for this study. Data collection instrument was a facts finding questionnaire titled learning domain coverage questionnaire. The major findings were that learning domains coverage in continuous assessment tests by academics in Cross River University of Technology was not significantly higher than the expected. There was also no
significant difference in learning domain coverage among faculties in Cross River University of Technology. It was recommended that continuous assessment tests should ensure adequate coverage of the learning domain particularly the cognitive and psychomotor domains. Seminars and workshops should be organized for all academic staff by test experts.
Predictive and Statistical Analyses for Academic Advisory Supportijcsit
ย
The ability to recognize studentsโ weakness and solving any problem may confront them in timely fashion is
always a target of all educational institutions. This study was designed to explore how can predictive and
statistical analysis support the academic advisorโs work mainly in analysis studentsโ progress. The sample
consisted of a total of 249 undergraduate students; 46% of them were Female and 54% Male. A one-way
analysis of variance (ANOVA) and t-test were conducted to analysis if there was different behaviour in
registering courses. Predictive data mining is used for support advisor in decision making. Several
classification techniques with 10-fold Cross-validation were applied. Among of them, C4.5 constitutes the
best agreement among the finding results.
Performance of student is dependent on their subject select and facultyโs expertise who teach the subject. Sometimes subject selection done by the student. Few studentsโ select subject wisely, few of them select because of friend influence, faculty influence, without thinking on it. To reduce the cost and related overhead department tries to reduce the variety of elective. Wisely selected and offered subject will increase the performance of student and throughput of the faculty. In this paper a recommender system is proposed which find the score of knowledge level for student and score of faculty score. These scores will be used to recommend the right subject to adept faculty of that subject.
This research is aimed to know studentsโ concept understanding profile on heat and temperature. This research is descriptive research using a qualitative approach. Subjects of this research are 10th-grade students in Surakarta in the academic year of 2016/ 2017. They represent high, medium, and low categorized school. Subject selection is based on the average score of physics at the latest four years. This research uses the question of extended response test through essay question. Before having given to the subjects, essay questions are validated by the experts. Based on the research result and the data analysis, studentsโ concept understanding on heat and temperature as follows: (1) The average percentage of studentsโ concept understanding at high categorized school is 60,66%, at medium categorized school is 51,47%, and at low categorized school is 48,83%, (2) Most students have misconception on basic concept of heat and temperature such as heat capacity, thermal conductivity, and expansion concept.
The purpose of this research is to analyze the improvement of students' mathematical literacy ability through the use of mathematics teaching materials with metacognitive approach guidance. This research will be held in the city of Kendari to the subject of this research target is students who are at grade 5 Land in Junior High VIIID Kendari years lessons 2017/2018 with many limited scale trial class is only required as much as 1 class. To know the significance of the increase in the literacy abilities of students using paired t-test. Data processing using the SPSS program with criteria if ฮฑ=0,05 then there is an increased of student's mathematical literacy ability. The results of the analysis on the stages of the evaluation shows the learning materials with metacognitive approach guidance can provide better against an increase in student learning. The ability of the early mathematical literacy against students is very less because of learning during this time students have not been directed with the ability of mathematical literacy. After the students get learning by using learning materials through metacognitive approach guidance, the ability of mathematical literacy studentsโ level 3 and level 4 underwent significant improvement.
Project-Based Learning Guided Lesson Study Improve the Achievement of Learnin...iosrjce
ย
The research impact project-based learning guided lesson study in Seminar Accounting Education
course has been conducted in 2012. The research was focused to improve student achievement of learning
outcomes in Seminar Accounting Education course at Department of Accounting, State University of Malang.
The learning outcome were defined on skill levels, the exploration ability and reviewing issues (contemporary)
in the field of accounting education both conceptual and factual, creating of research proposal, and final grade.
The research approach was classroom action research guided lesson study. The data were analyzed by
comparing student score with the minimum requirement score and the improvement of score from cycle 1 to
cycle 2. The implementation of project-based learning guided lesson study improved the ability of student to
create research proposal. The average score achieved by the students has surpassed the minimum requirement
(75) in the cycle 1 and 84% of the students have surpassed the minimum requirement, and in the cycle 2, 100%
of the students have surpassed the minimum requirement.
Students Effort to Improve Learning Results by Using Quantum Learning Method ...AI Publications
ย
This research aims to determine student learning outcomes by applying Quantum Learning method of prism and pyramid by eight grade students of SMP Negeri 2 Sipoholon in academic year 2017/2018. This type of research is Class Action Research (CAR). The subjects in this study were eight grade students of SMP Negeri 2 Sipoholon in academic year 2017/2018 were 28 student. The objective of this research is improving student learning outcomes. Instruments used were: description tests, observation sheets and interviews. Descriptive tests use to find out student learning outcomes, observation sheets use to find out ability of the teacher to apply learning and interviews to find out where the students are wrong. The average initial test score was 35.46 and the classical completeness was 0%. The average value of the first cycle was 59.78. Students who completed were 11 students (39.29%), 17 students (60.71%) were not finished, and the implementation of learning was in a less category (value 2.00). The increase in classical completeness was 39.29% and the average increase was 24.32 from the results of the initial tests. The average value of the second cycle was 76.04 and 25 students (89.29%) from 28 students had achieved mastery learning while the other 3 students (10.71%) had not yet completed, and the learning went well (average 3, 00). Cycle II has achieved classical completeness. Classical completeness has increased by 50% and the average has increased by 16.25 from cycle I. Thus the Quantum Learning method can improve student learning outcomes on the Prism and Piramid by eight grade students of SMP Negeri 2 Sipoholon in academic year 2017/2018.
Metacognitive Strategies: Instructional Approaches in Teaching and Learning o...IJAEMSJORNAL
ย
The purpose of the study is to determine the effectiveness of the metacognitive strategies as instructional approaches in teaching and learning of Basic Calculus. A number of 48 students consisting of 24 boys and 24 girls were purposively sampled in this study. Pretest-posttest quasi experimental research design was used which applied t-test and descriptive statistics. Both groups were subject to two instruments that were comprised of problem-solving test (pretest and posttest) and observation guide. Experimental group was taught Basic Calculus using metacognitive strategies while the control group was taught Basic Calculus using traditional teaching strategies. Both groups were subject to a pretest. Class observation was done while the two teaching strategies were applied. In the end, the posttest was administered to both groups to identify the effectiveness of the two teaching strategies. The data gathered were treated using paired sample t-test and independent sample t-test. The results of the study showed that the experimental group had significantly higher posttest scores as compared to control group which proved that metacognitive teaching strategies were more effective in improving the performance and problem-solving skills of the students than the traditional teaching strategies. It was also observed that students who taught using metacognitive strategies helped the students to be extremely engaged in Basic Calculus lessons cognitively, behaviorally, and affectively. The study reveals that the significant increase of the studentsโ learning engagement in Basic Calculus lessons led the students to a corresponding increase in their posttest scores.
The Empirical Analysis of Curriculum Quality Evaluation Based on Students Eva...ijtsrd
ย
The data of curriculum quality evaluation based on students evaluation of teaching contains learners perception of curriculum quality is helpful to provide reference for the improvement of curriculum quality. The evaluation system of curriculum quality should be built around ten evaluation dimensions, such as teaching objectives, subject frontier, case teaching, teacher literacy, classroom discussion, teaching methods, teaching courseware, information technology, teachers Q and A, curriculum ideological and political. Taking the students of school of physics and electronic information of Nanchang Normal University as the research object, and the sample size of curriculum quality evaluation is 5443, the Pearson correlation analysis is carried out on ten evaluation dimensions, and the correlation coefficient is 0.770, which indicates strong positive correlation. A regression model with ten evaluation dimensions as independent variables and curriculum satisfaction as dependent variables is established. The resolution of the model is 77.4 , which can predict curriculum satisfaction, and which has theoretical support for teachers to pay attention to the curriculum quality and improve the curriculum quality. It emphasizes that teaching department should pay attention to the effect of evaluation results on the continuous improvement of curriculum quality. It is suggested that a substantial and effective mechanism for continuous improvement should be established. Chen Rong "The Empirical Analysis of Curriculum Quality Evaluation Based on Students' Evaluation of Teaching" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38156.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38156/the-empirical-analysis-of-curriculum-quality-evaluation-based-on-students-evaluation-of-teaching/chen-rong
EFFECTIVE COVERAGE OF LEARNING DOMAINS IN STUDENTS ASSESSMENT BY LECTURERS OF...ijejournal
ย
The study investigated the degree of coverage of the three domains of learning during assessment of students by lecturers in Cross River University of Technology Calabar, Nigeria. The three learning domains in education are the cognitive, affective and psychomotor domains. Cognitive learning domain behavior involves thinking, reasoning, memorizing, comprehension, calculation, analyzing. Affective learning involves feeling, valuing, interest, responding. While psychomotor includes physical activities such as coordination and use of motor skills measured in speed, precision, distance and procedures. Two
hypotheses were formulated to test the expected level coverage of learning domains by lecturers in Cross River University of Technology. The study population comprised all academic staff in Cross River University of Technology, and 100 lecturers were randomly selected as sample for this study. Data collection instrument was a facts finding questionnaire titled learning domain coverage questionnaire. The major findings were that learning domains coverage in continuous assessment tests by academics in Cross River University of Technology was not significantly higher than the expected. There was also no
significant difference in learning domain coverage among faculties in Cross River University of Technology. It was recommended that continuous assessment tests should ensure adequate coverage of the learning domain particularly the cognitive and psychomotor domains. Seminars and workshops should be organized for all academic staff by test experts.
Predictive and Statistical Analyses for Academic Advisory Supportijcsit
ย
The ability to recognize studentsโ weakness and solving any problem may confront them in timely fashion is
always a target of all educational institutions. This study was designed to explore how can predictive and
statistical analysis support the academic advisorโs work mainly in analysis studentsโ progress. The sample
consisted of a total of 249 undergraduate students; 46% of them were Female and 54% Male. A one-way
analysis of variance (ANOVA) and t-test were conducted to analysis if there was different behaviour in
registering courses. Predictive data mining is used for support advisor in decision making. Several
classification techniques with 10-fold Cross-validation were applied. Among of them, C4.5 constitutes the
best agreement among the finding results.
Performance of student is dependent on their subject select and facultyโs expertise who teach the subject. Sometimes subject selection done by the student. Few studentsโ select subject wisely, few of them select because of friend influence, faculty influence, without thinking on it. To reduce the cost and related overhead department tries to reduce the variety of elective. Wisely selected and offered subject will increase the performance of student and throughput of the faculty. In this paper a recommender system is proposed which find the score of knowledge level for student and score of faculty score. These scores will be used to recommend the right subject to adept faculty of that subject.
Clustering Students of Computer in Terms of Level of ProgrammingEditor IJCATR
ย
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
Data mining in higher education university student dropout case studyIJDKP
ย
In this paper, we apply different data mining approaches for the purpose of examining and predicting studentsโ dropouts through their university programs. For the subject of the study we select a total of 1290 records of computer science students Graduated from ALAQSA University between 2005 and 2011. The
collected data included student study history and transcript for courses taught in the first two years of
computer science major in addition to student GPA , high school average , and class label of (yes ,No) to
indicate whether the student graduated from the chosen major or not. In order to classify and predict
dropout students, different classifiers have been trained on our data sets including Decision Tree (DT),
Naive Bayes (NB). These methods were tested using 10-fold cross validation. The accuracy of DT, and NlB
classifiers were 98.14% and 96.86% respectively. The study also includes discovering hidden relationships
between student dropout status and enrolment persistence by mining a frequent cases using FP-growth
algorithm.
Studentsโ Perception about Fundamental Programming Course Teaching and Learningrahulmonikasharma
ย
Programming learning has unique characteristics as it is a subject that requires skill and higher order thinking. Students come to class with a perception about the subject mostly obtained from their seniors including fear or perceived difficulty. Senior students have a perception about programming learning that was supported by their experience during the subject learning. Studentsโ views (+ / -) about the course could affect their performance. A qualitative survey was conducted with 93 third year students to obtain their views about the studentsโ point of views while learning programming and the recommendation for modifying the course. Obstacles identified by students could be tackled with the aid of technology enhanced learning (TEL) including tutoring system. This survey is done as a preliminary step in developing and incorporating technical solution to studentsโ problems. The findings were: Mostly, students are satisfied with the amount of time and effort they dedicated to the subject. While some mentioned that they would practice coding more and perform some projects beyond the course level. Majority of the students pointed out that they got useful advice from seniors about the subject learning. Less feedback was discouraging to students. About their suggested modification about the way the course setup, their overall responses approved the course design. There were minor comments about the proportions of the theoretical to practical components and the suitable amount of assignments.
June 12, 2019 Developed Page 1 of 22 .docxcroysierkathey
ย
June 12, 2019 Developed
Page 1 of 22
Course Information:
Course Number and Title: MG5615 โ Organizational Economics
Term/Year: Fall I 2019 Executive Term
Term Dates: August 26, 2019 โ January 12, 2020
Delivery Method: Online with Residency (December 20 โ 22, 2019 in Henniker, NH)
Meeting Place and Time: Online via Blackboard
Residency Dates: December 20, 2019 โ December 22, 2019
Credits: 3
Prerequisites: N/A
Instructor Information:
Faculty Name: Vlad Dolgopolov, PhD
Email Address: [emailย protected]
Phone Number: (518) 368-8707 (Central Time)
Response time: 24-48 hours
Required Materials and Textbook(s):
Managerial Economics: Applications, Strategy, and Tactics (14th Edition)
James McGuigan, R. Charles Moyer, & Frederick Harris
Cengage Learning, ยฉ 2017, 2014
ISBN-13: 978-1-305-50638-1
ISBN-10: 1-305-50638-3
Optional or Supplemental Materials:
Cengage PowerPoint Summary Slides of the covered chapters in the textbook and any other articles or reading
material are provided in the weekly Course Content Folder in Blackboard.
Course Description & Outcomes:
This course will develop studentsโ capacity to analyze the economic environment and to employ economic
analyses when making key management decisions. Students will review how economics impacts the way in
which an organization operates, to understand the constraints this environment places on the organizationโs
pursuit of its goals, how these constraints may change with time, and to apply economic reasoning to internal
decision making. Students will examine a variety of issues including demand analysis, forecasting, production
and cost, pricing policies, government regulations. (3 credits)
Upon completion of this class, students should have developed or enhanced their systematic investigative skills,
abstract thinking power, and analytical abilities along with logical reasoning required by decision-makers in
addressing domestic and international business issues. Additionally, students should be able to:
๏ Understand the objectives of a firm, explain the theory of profit, and recognize the responsibility of
management through defining managerial economics and fundamental economic concepts
June 12, 2019 Developed
Page 2 of 22
๏ Analyze demand, supply, operation of a market economy, differentiate various elasticities of demand
and supply, and recognize the business condition (trends, cycles, and seasonal effects)
๏ Demonstrate knowledge of Economic Forecasting and analyze the strength and weaknesses of
forecasting techniques.
๏ Examine the economic theory of production and cost analysis
๏ Practice the application of cost theory and examine the break-even analysis
๏ Demonstrate knowledge of pricing and output decisions as it relates to pure competition, monopolies,
oligopolies, and cartels; examine pricing techniques and identify competitorโs reactions and tactical
responses
๏ I ...
14Using Gagneโs Nine Events in improving Computer-Based Ma.docxhyacinthshackley2629
ย
14
Using Gagneโs Nine Events in improving Computer-Based Math Instructional Strategies for College Students in Saudi Arabia
Abstract
This paper intends to introduce the way in applying Gagneโs nine events of instruction in developing computer-based Math Instruction for College Students in Saudi Arabia. It particularly provides directing in the process of improving the interface of users and the presentation of the content within the instruction. So, the instruction based on the nine events can be delivered. This guidance or direction is aimed to activate all of the necessary cognitive process of the students and support the students reach the instructional goal.
Using Gagneโs Nine Events in improving Computer-Based Math Instructional Strategies for College Students in Saudi Arabia
From kindergarten through high school, mathematics is a required course through all levels of public instruction. It is also one of the determining methods that college study used in many standardized tests, such as GRE and SAT. In real life, mathematics skills are also required. In everyday life, many activities are related to mathematical skills such as counting money, exchanging currencies, doing measurements, determining interest rate, etc.
In the last five years, the Graduate Record Examinations (GRE) became very popular in Saudi Arabia. Despite its main goal as one of the necessary to enroll in for most graduate schools in the United States, GREยฎ is also given a better chance to enroll King Abdullah Foreign Scholarship Program especially for graduate students(MHE, 2014). Moreover, GREยฎ becomes used by some Saudi universities to measure their learnersโ skills proficiency in both English and Math. Plus, Some international business companies and institutions require a certain score to be eligible to get a job or enrollment in the training program. For example, the Kin Fahad University requires students to have 260 scored in the GRE test to be eligible to apply in some programs (Almawi, 2013). Recently, the Ministry of Higher Education in Saudi Arabia announced that the graduate students who have good score in GRE would get opportunities to pursue their studies at the North American Universities (MHE, 2014).
The GREยฎ uses the multiple choice question format in which each question has four or five options market. The GREยฎ test consists of three sections, Verbal Reasoning, Quantitative Reasoning, and Analytical Writing. Verbal Reasoning section consists of two parts, each containing 20 questions, Quantitative Reasoning section consists of two parts, each containing 20 questions, and Analytical Writing section consists of one section with 2 separately timed tasks: one "Analyze an Issue" task and one "Analyze an Argument" task (Educational Testing Service, 2014).
The Verbal Reasoning section measures students ability to understand what they have read and how they apply their reasoning skills .This section includes three parts, Text Complet.
Automating Studentsโ Activities in Higher Educational InstitutionsEditor IJCATR
ย
The modern educational systems consist of curricular and extracurricular activities. Students are encouraged to form unions.
These unions have representative roles, provide academic support and advice, offer welfare advice and support; engage in sports and
social activities of which most of them are free to join. In order to make life enjoyable, students have to be better time managers.
However, some of them find it difficult to prioritize their activities. This paper automates studentsโ activities in higher educational
institutions. It determines the preference of studentsโ activities in higher educational institutions and then prioritizes them with the help
of statistical tools. The associations between critical activities are determined and with the help of if-then rule set mechanism, a system
that has all the users activities stored in database and alert the user the right task to perform is designed. Higher educational institutions
were chosen for this study due to the dynamics of student life in these institutions
Predicting student performance in higher education using multi-regression modelsTELKOMNIKA JOURNAL
ย
Supporting the goal of higher education to produce graduation who will be a professional leader is a crucial. Most of universities implement intelligent information system (IIS) to support in achieving their vision and mission. One of the features of IIS is student performance prediction. By implementing data mining model in IIS, this feature could precisely predict the studentโ grade for their enrolled subjects. Moreover, it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. In this research, multi-regression model was proposed to build model for every student. In our model, learning management system (LMS) activity logs were computed. Based on the testing result on big students datasets, courses, and activities indicates that these models could improve the accuracy of prediction model by over 15%.
Similar to Solving Course Selection Problem by a Combination of Correlation Analysis and Analytic Hierarchy Process (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
ย
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
ย
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
ย
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
ย
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
ย
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
ย
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
ย
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
ย
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
ย
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances studentsโ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
ย
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
ย
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
ย
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
ย
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
ย
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
ย
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
ย
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
ย
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
ย
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
ย
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
ย
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Nuclear Power Economics and Structuring 2024Massimo Talia
ย
Title: Nuclear Power Economics and Structuring - 2024 Edition
Produced by: World Nuclear Association Published: April 2024
Report No. 2024/001
ยฉ 2024 World Nuclear Association.
Registered in England and Wales, company number 01215741
This report reflects the views
of industry experts but does not
necessarily represent those
of World Nuclear Associationโs
individual member organizations.
Vaccine management system project report documentation..pdfKamal Acharya
ย
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Courier management system project report.pdfKamal Acharya
ย
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
ย
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
ย
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the studentโs details, driverโs details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Solving Course Selection Problem by a Combination of Correlation Analysis and Analytic Hierarchy Process
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 6, December 2017, pp. 3536~3551
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i6.pp3536-3551 ๏ฒ 3536
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Solving Course Selection Problem by a Combination of
Correlation Analysis and Analytic Hierarchy Process
Mohammed Al-Sarem
Department of Information Science, Taibah University, Medina, Saudi Arabia
Article Info ABSTRACT
Article history:
Received Apr 4, 2017
Revised Jun 22, 2017
Accepted Jul 10, 2017
In the universities where students have a chance to select and enroll in a
particular course, they require special support to avoid the wrong
combination of courses that might lead to delay their study. Analysis shows
that the students' selection is mainly influenced by list of factors which we
categorized them into three groups of concern: course factors, social factors,
and individual factors. This paper proposed a two-phased model where the
most correlated courses are generated and prioritized based on the student
preferences. At this end, we have applied the multi-criteria analytic hierarchy
process (MC-AHP) in order to generate the optimum set of courses from the
available courses pool. To validate the model, we applied it to the data from
students of the Information System Department at Taibah University,
Kingdom of Saudi Arabia.
Keyword:
Course selection
Student preferences
Correlation analysis,
AHP method
Copyright ยฉ 2017Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Mohammed Al-Sarem,
Department of Information System, Taibah University,
PO box 344, Medina, KSA.
Email: mohsarem@gmail.com
1. INTRODUCTION
Course enrollment (CE) is one of the main administrative task that students faces each semester.
Often, the CE process starts few week before the start of the term itself and ends a week after the start of the
courses. During this period, the need to support students during selection and registration courses is increase.
At a time not so long ago, students were responsible for their own choices and the faculty advisor had
primarily become assisting students with the transition from high school to college [1]. Nowadays, situation
is extended to include guiding students to select courses, to register in each semester, and to fulfill the degree
requirement. Generally, the students aim to finish their study as soon as they can taking as many courses as
possible even if this affects negatively on their performance. From this end, colleges and universities began
to implement so-called academic advising affairs [2]. The academic advisory process is known as โprocess in
which advisor and advisee enter a dynamic relationship respectful of the student's concernsโ [3]. Faculty
academic advising has a significant impact on a studentโs academic success.
The academic advisor is responsible for: i) helping students in adaptation with specialization; ii)
following-up to the level of students each semester; iii) encouraging and drawing a good study plan that
ensures the improvement students' educational level; vi) determining which courses that may delay studentโs
graduation at the specified time; finally, v) helping students to correctly register their plan of study according
to the rules of deanship of admission and registration [2]. Current work discusses the influential factors that
drive students' selection. It suggests to combine the correlation analysis with the multi-criteria hierarchy
analytic method. The proposed model aims to present a framework for the future e-academic advisory
system. The work is organized as the follow: Section 2 presents the formulation of the course selection
problem. Section 3 presents related works and the methods have been applied to solve such problem. Section
4 discusses influential factors that might drive students decision making process. Section 5 presents the work
methodology, the used methods, application domain, and the data description. Section 6 describes the
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experiential part of this work. It presents an illustrative example showing how the model should to work; and
finally, Section 7 outlines the outlines the conclusions of this research and the future work.
2. PROBLEM DESCRIPTION and FORMULATION
Let โ be the set of all courses to be taught during the study plan โ (academic curriculum plan) for
obtaining a university degree. Each course c โ C gives a number of credits rC โ โค+
and are might be in
prerequisite relation (might find courses without any prerequisite such university required courses). The
study plan โ is divided into academic years, and each academic year is divided into semesters. Each
semesters, students are faced with selecting list of courses c โ Ct where Ct is list of available courses at an
academic semesters in case they satisfy the courses perquisites, Ct โ โ . The prerequisites are formalized as a
directed acyclic graph D = (Vc, A), where Vc represents a course, and each arc (i, j) โ A represents a
precedence relation between the course i and j in case the j โ th course cannot master without taught the
i โ th course. Let also โ(Ci) = (h, ct, N) represent impact of a ith course on the study plan โ, where h is
hierarchical level of Ci, ct is opened course in the next semesters t + i and i = 1,2,3, . . ., n, and N is the total
opened courses in the study plan โ.
Let also the ith course ci is taught by different instructors T at different time. Each semesters has an
allowed academic load ฮป. The academic load is determined based on the student performance (the average
grade point GPA) at the semester t โ1. Let ฮป obeys the following regulations:
- if student's GPA at the semester ๐ก โ1 is less than predefined threshold ๐, only the minimum course load
๐0 (a value of academic credits per semester required to consider a student as full time) is allowed to
register at the semester otherwise up to the maximum course load ๐1.
- if student is expected to graduate and still at least a quite little hours to accomplish his/her study, the
course load (extend course load ๐ ๐) is extend and students are allowed to register more hours at the
semester.
However, in real educational realm, in order to avoid the second above scenario, the academic
workload per semester need to balance keeping the prerequisite conditions. In addition to that, if courses
ci , cj and ci , cj โ C are in prerequisite relation, then it is better if a course ci is followed as close as possible
by a course cj [4]. Based on the aforementioned formulation, the course selection problem CSP ,now, is
formulated as follows: Finding these courses per semester that are, on one hand, meet student's preferences.
On the other hand, maximize his/her graduation final grades.
Practice shows that personalizing students' study plan according to their preferences leads to
enhance their learning performance. However, with a lot of opportunities to compose the university curricula,
restrictions, prerequisites and sometimes the university's rule, students may not be able to select course set
that meet their needs and preferences. Furthermore, if they do not know in advance, which performance skills
are challenged in the particular course, they may select/enroll in courses that are not adequate, at least, at a
particular term. We defend on the idea that, providing students with suitable courses set leads to maximize
their final GPA.
The course selection is also affected by other factors: instructor's reputation who give the course[5],
the course difficulty [6], GPA value for the course [7], course time scheduling [8], market demand [9], peers'
advices, and existing friends in a particular group/section (see Section 4).
3. RELATED WORKS
During the registration period, at an academic institution, commonly students should determine
which courses will be taking or dropping within available registration system. This process provides the
teaching staff and administration with clear vision about students' preferences, required class lists, and their
number in each class. However, the situation, in reality, is on the opposite. The timetable committee
constructs the whole time tables and then asks students to choose from the available course lists. Students, in
this case, need to consult their academic advisors before access the system. In case of unavailability of the
advisor or laziness to seek advice, these may cause to delay the registration process or the students make
decisions depending on their own experience and the available information [6].
Indeed, the described above problem can be tackled several ways. Just as examples, we can
mentioned the following approach: constraint programming (CP) [10], integer linear programming (ILP)
problem [11], [12], hybrid techniques based on genetic algorithms and constraint programming [13], [14],
integer programming and hybrid local search method [4], generalized quadratic assignment problem [15],
and ant colony optimization meta-heuristic model [16].
In this work, we present the CSP as multi-criteria based decision problem (MCDP). Gunadhi et al.,
[16] proposed a decision model for course advising system on studentโs need to know โwhat to doโ and โhow
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to do itโ. At the core of the system lies the curriculum generator which customizes the study plan to each
individual's needs and produces a schedule for the courses chosen. Customizing the study plan is depend on
the course selection criterion. Some systems allow students to request only courses for which they have
appropriate prerequisites and co-requisites [17]. In the others, the courses are suggested based on balancing
the course load, frequency of the course offering, shortening the path length to graduation, students'
preferences and their progress in the program [18], [19].
Current academic systems provide information about available courses and professors who will
teach them, sections, number of students in each section, and schedule. However, information about students'
previous progress from current/past enrollment is usually ignored even though such information are priceless
treasure in finding interdependent courses. In this direction, the educational data mining methods have been
successfully applied. Association rules e.g., are used as a way to seek dependency among courses of a
curriculum plan [20], [21], [22]. The course characteristics similarities of former students' study were used in
optimizing curricula of current students [7], [23].
4. INFLUENTIAL FACTORS on STUDENTS SELECTION
In the universities where students have a chance to select and enroll in a particular course, selecting
the optimum set of courses from the available courses pool is a high risk decision-making situation because
the cumulative impact will effect negatively/ positively on the students' performance progress, their expected
graduate date and the final GPA as well as their career direction and future employment opportunities. As
mentioned before, course selection process is influenced several factors. Analysis the research literature and
the conducted questionnaire, we summarize these factors into three main groups of concerns: (i) course
factors, (ii) social factors, and (iii) individual factors. Indeed, these groups is decomposed into sub-groups
which influence on the whole decision-making process. Since different courses are selected with different
preferences and objectives, the decision process must take all these factors concurrently (see Figure 1 below).
Figure 1. Infuential factors on students' course selection
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Next, we discuss the impact of these factors on students' decision-making process and show how
will they engage in the proposed approach. Table 1 gives a brief description of decision attributes that are
used for driving the selection process.
Table 1. Description of criteria and decision attributes used for selecting a course
Criteria Decision attributes Refers to:
Course factors
Course characteristics
Course credit hours, Distance between a course and
its prerequisites, Student competence for a given
course
Instructor characteristics
Personal instructor characteristics, Instructor
assessment approach, Instructor lecturing style
Teaching language Course teaching language
Social factors
Peer opinions Peers' feedback
Closed Friend Existing in the class a closed friend
Campus Location Location of the class room, Campus location
Individual factors
Course time scheduling Time when student attend the class
Student demands
Student's interest in a course, job opportunities, Local
labor.
Learning style
A way or an approach a student follows in the course
of learning.
4.1. Course Characteristics
- Course Characteristics
The questionnaire results show that students' choices regarding course characteristics are depend
mainly on the difficulty of the course, course weight ( course credit hour), distance between a course ๐๐ and
its prerequisites, and student competence for a given course.
Difficulty- refers to complexity level of a course taking in consideration the grades of every student
who passed that course successfully to the grades of all students who follow the same curriculum plan.
Logically, a course with high ๐๐๐๐(๐ถ๐) is considered as difficult course, otherwise it is easy.
๐๐๐๐(๐๐) = 1 โ (
โ โ ๐๐,๐
โฒ๐
๐=1
๐โ๐ถ ๐
โ โ ๐ ๐,๐
๐
๐=1
๐โ๐ถ ๐
โ
๐
๐
) (1)
where, ๐ถ๐- is the ๐th course in the curriculum plan, ๐๐,๐
โฒ
- is GPA of a student who passed a course ๐ถ๐
successfully from the first attempt, ๐๐,๐- is GPA of the student who take the course ๐ถ๐, ๐- number of student
who passed the course ๐ถ๐ from the first attempt, and ๐- is number of students who follow the same
curriculum plan and take the course ๐ถ๐.
Distance between two courses ๐ถ๐ and ๐ถ๐ taught by a student s is defined as the Euclidean distance of
the hierarchical level โ at where the courses ๐ถ๐ and ๐ถ๐are being taught.
๐๐๐ (๐ถ๐, ๐ถ๐) = {
โ(๐ถ๐
โ
โ ๐ถ๐
โ
)
2
+ (๐ถ๐ ๐
โ
โ ๐ถ๐ ๐
โ
)
22
, ๐ถ๐
๐๐๐๐๐๐๐ข๐๐ ๐๐ก๐
โ ๐ถ๐
1 , ๐๐กโ๐๐๐ค๐๐ ๐
(2)
where, ๐ถ๐
โ
and ๐ถ๐
โ
- is the hierarchical level โ at where the courses ๐ถ๐ and ๐ถ๐
โ
are being taught
respectively, ๐ถ๐ ๐
โ
and ๐ถ๐ ๐
โ
- is the academic semester where courses ๐ถ๐ and ๐ถ๐
โ
are being taught.
Competence represents student's ability to study a course based on the grades he has obtained in the
prerequisites.
๐ถ๐๐๐๐๐ก๐๐๐๐(๐๐
๐
) = {
1 , ๐ถ๐ โ๐๐ ๐๐๐ก ๐๐๐๐๐๐๐ข๐๐ ๐๐ก๐
โ ๐ ๐ถ ๐
๐
โ ๐๐๐๐(๐๐) โ ๐๐๐ (๐ถ๐, ๐ถ๐)๐
๐=1,๐โโ
๐๐ โ ๐๐
๐ โ , ๐๐กโ๐๐๐ค๐๐ ๐ (3)
where, ๐๐
๐
- is the current GPA grades of student ๐ , ๐๐๐๐(๐๐) - is difficulty of the course ๐๐,
๐๐๐ (๐ถ๐, ๐ถ๐)- is distance between a course ๐ถ๐(prerequisite course) and course ๐ถ๐, ๐๐- is credit hours of course
๐ถ๐and, ๐๐
๐
- is number of attempts student ๐ was enrolled in course ๐ถ๐.
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- Instructor Characteristics
Although, the course characteristics have a significant impact on students' enrollment decision,
practice shows that the instructor characteristics also play important role on the future decision to enroll in
those courses taught by this instructor [5], [24] and on how useful the course can be [25]. Nowadays,
majority of universities provide online system to collect students' feedback for all offered courses at the end
of academic semester. Often, feedback takes a form of questionnaire or survey which contain a series of
items that are ranked on a five-points Likert-scale. The questionnaire/survey items address the question about
personal instructor characteristics, course value presented by the instructor, instructor assessment approach,
and instructor lecturing style. Researchers, such as, [24], [26] noted that students prefer to take courses with
teachers who are enthusiastic, well spoken, knowledgeable, caring, and helpful. Beggs et al., [27] found
that the quality of a course presented by the instructor has a large affect on whether a student chooses to
enroll in a class. Although questionnaire results show beside the quality of the course, both the instructor
assessment approach [28], and instructor lecturing style [24], [26] are critical factors in course enrollment.
- Teaching Language
Several researchers considered language as a significant factor not only in learning process but also
in their motivation to learn [29], [30]. According to Coleman [31] the use of a common language allows, on
one hand, efficient exchange of ideas, on the other hand, facilitates communication skills.
Nowadays, major of universities present course contents in English even if it is not the official/
native language. The reason behind this choice is that English has a positive impact on modernization, and on
the quality of learners' experience [31]. However, students prefer to deal with instructors who share the same
native language or with course content that is written in the native language even if they speak and
understand English.
4.2. Social Factors
It is obvious that student's preferences are influenced directly or indirectly by peers and friends
opinions. Their influences are clear in shaping and molding the course of an individual life [32]. Peer
influence is more observable in friendship [33] which is represented as succumbing to the views and opinions
of the peers, making a decision based on peer's advice, or just listening to the peer before listening to their
teacher and advisors is a form of such influence [34]. Naz et al., [32] found that peer and friends have a
positive role in selection of subjects, selection of a class and laboratory.
Analysis the feedback of students of department of Information System at Taibah University (Table
2), the majority of students (57.1%) are agree that their selection is dependent on the received advice from
their peers or friends, (55.5%) prefer to enroll in a course if some of their friends are also enrolled in the
same course, and (74.6%) indicated that their opinion about instructors are influenced by peers' and friends'
opinions. Generally speak, majority of students are agree that their selection is influenced by advice of their
peers and friends.
Table 2. Students' preferences respect peer's/friend's opinion
Question
Percentage
Strongly Disagree Disagree Neutral Agree Strongly Agree
My choice of course is mainly depend on advice of
my peers/friends
3.2 11.1% 28.6% 31.7% 25.4%
I prefer to enroll in a course if some of my friends
are enrolled in it also
7.9% 9.5% 27% 22.2% 33.3%
Enrollment in a course which is taught by an
instructor is depend on peers' /friends' opinions
about the instructor
3.2% 3.2% 19% 39.7% 34.9%
Total Impact of peer's/friend's advice on course
selection
1.3% 4.2% 19.8% 33.2% 41.5%
4.3. Individual Factor
- Course Time schedule
Although student preference respect course time schedule does not play a role in selection process
of full-time student, students have made decisions to take a course, or to not take a course, based on the fact
of whether or not it fits into their schedule [35], [8].
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Table 3. Students' preferences course time schedule
Question
Percentage
Strongly Disagree Disagree Neutral Agree Strongly Agree
Choosing the scheduling times of courses have
helped me to pass them successfully.
7.9% 12.7% 31.7% 20.6% 27%
Engagement students in scheduling courses time
enhances their motivation to study
13.8% 12.7% 20% 33.8% 19.7%
Total Impact of course time schedule on
course selection
3.1% 7.2% 23.1% 31.7% 34.9%
Table 3 illustrates that 47.6% of students found that choosing the scheduling times of courses have a
positive impact on their study and lead them to pass the courses successfully, and 53.5% of students think
that engagement them in scheduling courses time enhances their motivation to study.
- Student Demands
Several studies have considered interest in a course topic or subject as a driving force behind
studentsโ enrollment in classes [24], [34], [35]. The interest impact is more evident when students should to
make decision to take a course from elective courses available by the collage.
According to [26], student's interest in a course is influenced by numerous factors such as subject
matter, topics, and career goals. Enjoyment, job opportunities, and local labor trend are other factors that
influences the course selection. Students are attracted to take a course that they think that will increase their
chances to get a job.
- Learning Style
Learning style is one of the individual differences that play an important role in learning [36]. In the
literature, several definitions can be found which share the same basic idea " the term learning style refers to
a way or an approach a student follows in the course of learningโ. According learning style theory, students'
interest in a course is influenced also by their preferred learning style. Adapting course content has been
applied intensively in e-learning systems where the learning styles and e-media are integrated together in the
design of their applications. Such integration showed a positive results in both learning styles detection and
e-learning application [37].
Table 4 presents how the learning style impacts on students' decision. It also presents students'
preferences regarding selecting courses. Statistical results emphasize on the fact that during making a
selection decision, beside the aforementioned factors, the learning style of a student should take in
consideration.
Table 4. Students' preferences respect to learning style
Question
Percentage
Strongly Disagree Disagree Neutral Agree Strongly Agree
I prefer to take a course with practical nature before
those with theoretical
6.3% 12.7% 36.5% 20.6% 23.8%
I prefer to enroll with maximum allowed workload in
an academic level
15.9% 19% 38.1% 11.1% 15.9%
I prefer to postponed university required course to the
latest level
23.8% 28.6% 28.6% 9.5% 9.5%
I prefer to finish early university required course as
possible as I can
3.2% 6.3% 17.5% 36.5% 36.5%
I prefer to take the course with lowest credit hours
firstly, then the highest and so on.
20.6% 20.6% 31.7% 11.1% 15.9%
I think that allowing to take a course from any level,
in case I take its prerequisite, help me to success.
7.9% 12.7% 22.2% 31.7% 25.4%
I prefer to follow courses' order as it is in the
curriculum plan.
1.6% 3.2% 31.7% 42.9% 20.6%
5. WORK METHODOLOGY
The core of this research is to build a decision model which aim at help and support the students
during the enrollment and registration process. The model is two-phased process (Figure 2). The first phase,
is similar to those presented in [23] where the most correlated courses are generated. At the second phase, the
student preferences are taking in consideration. This preferences are prioritized using multi-criteria analytic
hierarchy process (MC-AHP). To understand the research context and the used data, in the next sections, we
present a brief explanation of the used methods, the application domain, and the gathered data.
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Figure 2. The Research Methodology
5.1. The Used Methods
- Correlation Analysis
Observing relationship among variables is a classical data mining task. Broadly, there are four types
of relationship mining: association rule mining, correlation mining, sequential pattern mining, and causal data
mining [8]. To help student in making a decision of which course he /she should to take, it is helpful finding
positive or negative linear correlations between courses.
Often, to represent the correlation graphically, a scatter diagram is used where the pair of points/data
(x, y) is allocated on an orthogonal coordinate system.
The linear correlation coefficient measures the strength of the linear correlation between the two
variables; it reflects the consistency of the effect that a change in one variable has on the other. In educational
realm, the correlation between two courses Ci and Cj as follows:
corr(Ci , Cj) =
โ (gi
ciโgฬ ci)(gi
cj
โgฬ
cj)k
i=1
โโ (gi
ciโgฬ ci)2k
i=1
โโ (gi
cj
โgฬ
cj)2k
i=1
(4)
gi
ci
- is grade points for the ith course
gi
cj
- is grade points for the jth course
gฬ ci- is average grade point for all students who take the ith course
gฬ cj- is average grade point for all students who take the jth course
k- is number of students who take Ci and Cj.
The linear correlation coefficient takes value between โ1 and +1:
๏พ corr(Ci , Cj) = +1 reflects a perfect positive linear correlation between both courses Ci and Cj.
๏พ corr(Ci , Cj) = โ1 reflects a perfect negative linear correlation between both courses Ci and Cj.
๏พ corr(Ci , Cj) = 0 means that there is NO linear correlation.
if the calculated value is close to +1 or โ1, we then suppose that between the two variables there is a
linear correlation.
-Multi-criteria Analytic Hierarchy Process
AHP is a well-established decision making technique for dealing with multi-dimensional and often
contradictory preferences of individuals [5]. The AHP ranks alternatives in view of criteria and sub-criteria
(factors). In AHP, we start firstly with representing the problem with a hierarchal structure which is consists
of all factors and alternatives. The hierarchal structure mainly establishes the relationships between the levels
of the hierarchy order at which we place the objective (the Goal) at the top of the hierarchy, the criteria and
sub-criteria at intermediate levels, and finally the alternatives are placed at the lowest level of the order.
In the second step, a pair-wise comparison judgments are carried out, for each criterion, using a nine
points scale (1= equivalent,..., 9= extremely preferred to).
The result of each comparison is a matrix (n ร n), where the diagonal elements aii are equal to
one,i = 1,2, โฆ , n, and if aij = x, then aij = 1
xโ where x โ 0.
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A = [
a11
a12 โฏ a1n
a21
โฎ
a22 โฏ
โฎ โฑ
a2n
โฎ
an1
an2 โฏ ann
]
Next step of the AHP (scoring and weighting) is to compute eigenvectors uj=(u1, u2, โฆ , un) by
solving AW = ฮปmax. W, where ฮป- is an eigen-value and W- is eigenvector.
The final step of AHP is to perform a consistency check (consistency ratio CR) by dividing the
consistency index CI by the random index RI, where the consistency index CI is calculated as follows:CI =
(ฮปmax โ n)/(n โ 1), where n is the matrix size and the random index RI which is taken according Table 5.
Table 5. Average random consistency (RI) used in Saaty
Size of matrix 1 2 3 4 5 6 7 8 9 10
Random consistency 0 0 0.58 0.90 1.12 1.24 1.34 1.41 1.45 1.49
The CR is considered acceptable only if it is less than 0.1, otherwise the pair-wise comparison
judgments should be reviewed and improved.
5.2. Application Domain
To show how the decision model supports the students during the enrollment and registration
process, the experiential part of this work was developed in the context of department of Information System
at the Taibah University, Kingdom of Saudi Arabia. Generally, study at Taibah University, as all remains
universities in Saudi Arabia, are organized in two regular academic terms by year, plus a summer term which
is opened only if there is a quite number of students who failed pass a course in regular terms. The regular
terms are spanning four months, whilst the summer term is compressed into two months. Since 2004, the
academic program is changed three times. However, number of credit hours is still the same. Each program
consists of two parts:
- the preparation period where students spent one academic year at which they took a set of courses that
prepare them to their future studies
- the regular period is consists of four academic years. The program consists of 14 credit hours of
university requirement courses, 19 credit hours of faculty requirement courses, and 46 credit hours of
department requirement courses nine of them are elective courses.
In order to pass a course, the student has to obtain at least 60 points out of 100; otherwise he will be
required to attend the course again in the next academic year or in the summer term, in case the number of
those students who failed to pass the course is quite enough (the decision is made based on the opinion of the
vice dean of the academic affairs at each faculty). The maximum number of attempts to pass a course is
depends on the student's GPA. For the student whose the GPA is less than the cut-point (2.5 out of 5) for two
sequential academic terms, he will not be able to continue his/her studies. During the enrollment period,
students should to register the selected courses including the name of the preferred time and group using the
online enrollment system or by assistance the academic advisors. The students is eligible for enrollment a
course, only if they passed the prerequisites for the said course, otherwise they are deny to take it.
5.3. Data Description
Since the academic program is changed several times, the historical records contain data from three
different curricula, each of them with 42 courses separated through eight regular academic terms and four
summer terms, for further details about the total number of students and classes, see Table 6. Due to of
modification or changes in the curricula (sometimes, only the prerequisites of a course is changed), we focus
only the curricula from 2011 to 2015 namely "new curricula".
Table 6. The number of students in each academic year and average classes to graduate students.
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Academic Year
Enrolled Graduated Average Classes to graduate Curricula
Male Female Male Female Male Female
2010/2011 420 600 90 89 10.3 9 Old Curricula
2011/2012 600 676 95 126 10.4 9.8
New Curricula2012/2013 484 686 58 150 10.9 10.3
2013/2014 789 698 93 137 10.7 11
2014/2015 828 674 111 175 10.3 11.1 Developed
Curricula
The average classes to graduate students in Table 6 refers to the number of academic terms that
students spend to finish their study in case the fail to pass the course from the first attempt. Figure 3 shows
the increase in the required classes between both groups (male and female sections).
Figure 3. Average number of classes required to graduate students
The main goal of the current research is to give the student (who intends to register on a course) a
recommendation based on the gained grades at the previous terms. The correlation analysis is performed
based on the final grade of the students. The aim of this step is to link each course with the most correlated
courses that may be effected by the selected course. Table 7 shows the used attributes and give a brief
description for each of them, whilst Table 10 presents the data type of the attributes and a short statistical
summary for each of them. The "Period" attribute refers to the academic term in which student should take a
course. It discriminates as follows:
Period = {
x โ [1 โ 8], ๐ฅ โ ๐๐ ๐ ๐๐๐๐ข๐๐๐ ๐ก๐๐๐
x โ [9 โ 12], ๐ฅ โ ๐๐ ๐ ๐ ๐ข๐๐๐๐ ๐ก๐๐๐
Both "Registered Credit hours" and "Gained Credit hours" attributes are used to split the data set in
to training and testing set. The highest value of " Registered credit hours" denotes students has a difficulties
in finishing his study, whilst the highest value of "Gained credit hours" denotes that the student is near to
graduate.
Table 7. The used attributes
Attributes Description
Course name Identifier for each course the student is enrolled on
Course code Identifier for each course in the university system
Course credits Practical and theoretical workload for each course
Period Academic term in which student should take the course
Final grade Result obtained at the end of the term in each course
Student ID Identifier for each student
GPA Overview of the studentโs performance over time
Student gender The gender of student who took a course
Registered credit hours Amount of credit hours registered in the university system
Gained credit hours Amount of credit hours already student passed
10. IJECE ISSN: 2088-8708 ๏ฒ
Solving Course Selection Problem by a Combination of Correlation Analysis โฆ (Mohammed Al-Sarem)
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Table 8. Statistical summary of the used attributes
6. EXPERIMENTATION AND EVALUATION
Most course recommendation systems use students' personal data and social networking sites to find
out what they like or are interested in [38], e.g., proposed to use students' grades in developing a course
recommendation system. The system helps students find courses in which they can get high scores. For this
purpose, data about the courses which users learned, scores that students received, and the teachers of the
courses are collected.
Current work, as mentioned before, suggested a method with two-phased process. At the first phase,
the most correlated courses are generated. However, as we check correlation course by course, only those
courses (set of courses) that satisfy the following constrains are generated:
๏พ Only courses with highest correlation are generated.
๏พ The total number of credit hours of the generated courses (academic load of student ๐ด) should
not exceed the maximum course load ๐1.
The Pseudo-Code for generating courses based on correlation analysis is illustrated in Pseudo-Code 1.
Pseudo-Code I: Generating courses based on correlation analysis
๐บ๐๐ด ๐
๐กโ1
: GPA of a student ๐ at previous semester ๐ก โ 1
๐1 , ๐0: maximum and minimum course load for a student respectively
๐๐กโ1: course at semester ๐ก โ 1 that is passed successfully by the student, ๐๐กโ1 โ โ
๐๐ก+1: course at next semester that student can take, ๐๐ก+1 โ โ
๐: Final set of recommended courses
๐๐
๐ก+1
: Credit hours of courses at semester ๐ก + 1
1: |๐| โ โ
2: IF ๐บ๐๐ด ๐
๐กโ1
> ๐0 THEN
3: ๐๐๐๐(๐๐กโ1, ๐๐ก+1); # Calculate correlation
4: END IF
5: FOR โ๐ โ ๐ถ ๐กโ1
; # Loop for all courses that a student passed them successfully
6: ๐ด๐ฃ๐๐_๐๐๐๐[๐] โ
๐ถ๐๐๐๐๐ก๐๐๐๐( ๐ ๐
๐ก+1) โ ๐๐๐๐(๐ ๐
๐กโ1,๐ ๐
๐ก+1)
๐
๐=1
๐
; # Find average matrix
Attributes Data type Possible Value Statistical summary
Course name String Mathematics, Physic (1),
Programming (1), est.
42 courses
Course code String Math101, Phys101, CS102, est. 42 courses
Course credits Discrete 1,2,3,4 Course Credits Percentage
1 2%
2 12%
3 55%
4 31%
Period Discrete 1,2,..., 12. 12 terms
Final grade Continuous โฉ0,100โช Minimum 38
Maximum 100
Student ID String 31#####, 32#####, 33##### Student ID Percentage
31##### 8.4%
32#####, 43%
33#####, 48.6%
GPA Continuous โฉ0.000,5.000โช Mean 3.937297
Standard Deviation 0.552337
Sample Variance 0.305076
Minimum 2.06
Maximum 4.99
Student gender String Male, Female Student gender Percentage
Male 56%
Female 44%
Registered credit hours Discrete โฉ0,250โช Mean 173.0541
Standard Deviation 11.07285
Sample Variance 122.6081
Minimum 153
Maximum 209
Gained credit hours Discrete โฉ0,165โช Mean 102.797
Standard Deviation 40.88291
Sample Variance 1671.412
Minimum 14
Maximum 165
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7: ๐ถ ๐ก+1
โ ๐๐๐๐๐๐ก(๐, ๐๐๐ฅ(๐ด๐ฃ๐๐_๐๐๐๐(๐๐กโ1, ๐๐ก+1)); # Select ๐พ courses with maximum correlation
that are satisfy the university regulation
8: |๐| โ ๐๐๐๐๐๐๐(๐1, ๐ถ ๐ก+1
, ๐๐
๐ก+1
); # Generate the recommended course package
Pseudo-Code II: ๐๐๐๐๐๐๐(๐1, ๐ถ ๐ก+1
, ๐๐
๐ก+1
); Generate the recommended course sets
Input: ๐1 : allowed maximum course load for a student
๐ถ ๐ก+1
: Set of all available courses at semester ๐ก + 1
๐๐
๐ก+1
: Credit hours of a course ๐ โ ๐ถ ๐ก+1
Output: Set of the recommended courses
1: Set |๐ | โ โ , |๐| โ โ
2: ๐ โ ๐บ๐๐๐๐๐๐ก๐๐ด๐๐๐ถ๐๐๐๐๐๐๐ก๐๐๐ข๐๐๐๐ก๐ (๐ถ ๐ก+1
);
3: Add ๐ to |๐|
4: FOR ๐ โ 0 to ๐
5: ๐[๐] โ ๐ถ๐๐๐๐ข๐๐๐ก๐๐๐๐ก๐๐๐ถ๐๐๐๐๐ก๐ป๐๐ข๐๐ (๐๐
๐ก+1
);
6: Add ๐[๐] to |๐ |
7: END FOR
8: WHILE ๐, ๐[๐] โค ๐1
9: ๐ถ๐๐๐๐๐๐๐ก๐[๐] โ
โ (๐ ๐ ๐
๐ก+1๐ถ ๐ก+1
i=1,j=1,๐โ๐ถ ๐ก+1,
)โcorr(ci,cj)
๐[๐]
10: ๐ถ๐๐๐๐๐๐๐ก๐๐๐๐ก โ ๐๐๐๐๐๐ก(๐๐๐ฅ(๐ถ๐๐๐๐๐๐๐ก๐[๐])); # Sort the list
12: END WHILE
13: return ๐ถ๐๐๐๐๐๐๐ก๐๐๐๐ก
Since the recommended courses are generated based on the correlation analysis, the recorded grades
can be influenced, as mentioned above, by course content itself or experiencing a particular teacherโs style or
material of teaching [23]. Assuming that a student S passed a set of courses at semester t โ 1, and the grades
in the database are recorded taking in consideration the aforementioned influential factors.
Having samples of k students S = {s1, โฆ , sk}, who took a course Cj after a course Ci, and each
student si achieved gj
cj
GPA in course cj and gi
ci
GPA in course Cias shown in Table 9.
Table 9. Sample of students grades at a semester ๐ก
Student ID Course
ISLS101 ARAB101 MATH101 PHYS103 CS101 ENGL103
3151377 95 95 87 70 81 85
3151559 90 98 71 65 71 81
3182286 99 96 93 85 80 72
3182565 95 96 90 85 86 95
3182894 98 95 77 87 75 75
3200079 100 100 98 100 98 90
3200162 100 100 74 93 95 78
3200229 99 100 100 100 95 98
Let a student passed a set of courses as shown in Table 10, and the correlation among all curriculum
courses are already calculated.
Table 10. Student grades at current semester
Student ID Course
ISLS101 ARAB101 MATH101 PHYS103 CS101 ENGL103
3200237 95 95 87 70 60 85
Since Eq. (4) assumes that the grades of all courses are given, and because of absence the grades of
those courses of the next semester, we propose to use the regression analysis to predict the grades of each
courses based on the historical records of all its perquisite courses (see Table 11).
12. IJECE ISSN: 2088-8708 ๏ฒ
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Table 11. Predicted grades for the courses at the next semester
Student ID Curriculum Plan ๐
Grades at Semester ๐ก Predicted Grades
ISLS
101
ARAB
101
MATH
101
PHYS1
03
CS
101
ENGL
103
IS
201
CS
202
ARAB
201
MAT
H
102
IS
102
IS
221
CS
112
ENG
L 104
3200237 95 95 87 70 60 85 66 86 92 52 85 70 75 75
Table 12 presents the correlation among the already taken courses and those are offered at the next
semesters.
Table 12. Example for calculation demonstration.
Student ID Curriculum Plan ๐
Current Courses at Semester ๐ก
Next Semester ๐ก + 1
IS
201
CS
202
ARAB
201
MATH
102
IS
102
IS
221
CS
112
ENGL
104
3151377
ISLS101 95 0.04 0.25 0.604 0.331 0.441 0.244 0.221 0.09
ARAB101 95 0.00 0.27 1.000 0.346 0.210 0.279 0.297 -0.01
MATH101 87 0.15 0.63 0.526 1.000 0.536 0.447 0.489 0.47
PHYS103 70 0.29 0.37 0.483 0.625 0.342 0.260 0.358 0.20
CS101 60 0.34 0.46 0.486 0.640 0.286 0.574 1.00 0.49
ENGL103 85 0.26 0.49 0.603 0.416 0.379 0.151 0.321 1.00
Table 13 presents average correlation matrix of each generated course. Steps 7-10 allow us to sort
and select the top k courses based on its average correlation.
Table 13. Average correlation matrix of the courses
Student ID Curriculum Plan ๐
Current Courses at Semester ๐ก
Next Semester ๐ก + 1
IS
201
CS
202
ARAB
201
MATH
102
IS
102
IS
221
CS
112
ENGL
104
3151377
ISLS101 95 0.04 0.25 0.604 0.331 0.441 0.244 0.221 0.09
ARAB101 95 0.00 0.27 1.000 0.346 0.210 0.279 0.297 -0.01
MATH101 87 0.15 0.63 0.526 1.000 0.536 0.447 0.489 0.47
PHYS103 70 0.29 0.37 0.483 0.625 0.342 0.260 0.358 0.20
CS101 60 0.34 0.46 0.486 0.640 0.286 0.574 1.00 0.49
ENGL103 85 0.26 0.49 0.603 0.416 0.379 0.151 0.321 1.00
Average 0.18 0.41 0.617 0.56 0.366 0.33 0.45 0.37
To generate the set of the candidate courses package, first we should to follow the academic
regulation. In our case where this research is conducted, the academic regulation determines the maximum
course load ฮป1based on the student's GPA as follows:
ฮป1 = {
ฮป1 โ [17,21] , student is expected to graduate next semester
ฮป1 โ [12,17] , 5 โค GPA โค 2.8
ฮป1 = 12 , GPA โฅ 2.7
Let that the current GPA of a non-graduated student is 3.21 which means that the student has a right
to register courses with total credit hours up to 17. Table 14 shows the combination of all possible sets of the
eight courses. The set with the largest average correlation is suggested as a recommended package of courses
for the next phase.
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Table 14. Combination of all possible sets
Student ID Curriculum Plan ๐
ARAB
201
MATH
102
CS
112
CS
202
ENGL
104
IS
102
IS
221
IS
201
๐1
Overall Priority
Average
Rank
Credit
Hours
3 4 4 4 3 3 4 3
3200237
ARAB-201, MATH-102, ENGL-104, IS-221, IS-201 17 0.219992771 1
ARAB-201, MATH-102, ENGL-104, CS-112, IS-201 17 0.197847972 4
ARAB-201, MATH-102, ENGL-104, CS-202, IS-201 17 0.213518998 2
โฏ โฎ โฎ
ARAB-201, MATH-102, CS-112, CS-202 15 0.191914578 5
ARAB-201, MATH-102, CS-112, ENGL-104 14 0.207102686 3
โฏ โฎ โฎ
MATH-102, CS-112, CS-202 12 0.161333772 6
MATH-102, CS-112, IS-102 12 0.16062525 7
โฏ โฎ
Let the timetable of these courses are already predefined and each course (in this set) is linked with
one/ many instructors in different time (see Figure 4). The next step, as shown in Figure 2, is to prioritize
time, instructors, and sections of the courses based on the student preferences. As mentioned previously, the
preferences are prioritized using multi-criteria analytic hierarchy process (MC-AHP) (See Section 5.a).
Figure 4. Snapshot of IS-102 schedule
Following the procedure of MC-AHP, there are a total of six pair-wise comparison matrix tables:
1. The pair-wise comparison matrix of the criteria relating to the goal. This is illustrated in Table 15.
2. The pair-wise comparison matrices for the five options (set of the recommended courses) regarding all
the โcriteria concernedโ, where the criteria in all levels are connected to the options.
The consistency ratios of all comparisons were less than 0.1, which indicates that the weights used
are consistent.
Table 15. Pair-wise comparison matrix of the key criteria with regards to the goal
Criteria Course factors Social factors Individual factors
Global Priority
Vector
Course factors 1 9 3 0.67
Social factors 1/9 1 1/5 0.06
Individual factors 1/3 5 1 0.27
๐ถ๐ผ = 0.014606 ๐ถ๐ = 0.025182 โค 0.1
Tables 16 illustrates the pair-wise comparisons of the alternatives for the first offered sections of IS102
course in terms of aforementioned criteria.
14. IJECE ISSN: 2088-8708 ๏ฒ
Solving Course Selection Problem by a Combination of Correlation Analysis โฆ (Mohammed Al-Sarem)
3549
Table 16. Pair-wise comparison matrix with regards to the sub-criteria
Criteria
Course
characteristics
Instructor
characteristics
Teaching
language
Local Priority
Vector
Priority respect
Global Vector
Course characteristics 1 5 3 0.63 0.4221
Instructor characteristics 1/5 1 1/3 0.11 0.0739
Teaching language 1/3 3 1 0.26 0.1742
๐ถ๐ผ = 0.019357 ๐ถ๐ = 0.033375 โค 0.1
Criteria
Peer opinions Friendship Campus
Location
Priority Vector
Priority respect
Global Vector
Peer opinions 1 3 1/3 0.29 0.0174
Friendship 1/3 1 1/3 0.14 0.00084
Campus Location 3 3 1 0.57 0.0342
๐ถ๐ผ = 0.076 ๐ถ๐ = 0.1
Criteria Course time
scheduling
Student demands
Learning
style
Priority Vector Priority respect
Global Vector
Course time scheduling 1 7 1 0.51 0.1372
Student demands 1/7 1 1/3 0.10 0.027
Learning style 1 3 1 0.39 0.1053
๐๐ = ๐. ๐๐๐๐๐๐ ๐๐ = ๐. ๐๐๐๐๐๐
The output of the MC-AHP algorithm is summarized in the overall priority matrix as shown in Table 17.
Table 17. Overall priority matrix
Course Name IS-102 (Foundation of Information Systems)
Course factors Social factors Individual factors Overall Priority Rank
IA_4 0.6702 0.05244 0.2695 0.992 I
IB_4 0.4763 0.3661 0.109 0.9514 II
The procedure is continuing for all courses that are generated at the first phase. The aim of this step
is to prioritize the offered sections in the timetable. Thus, students are provided by a list of courses and its
sections taking in consideration their preferences.
7. CONCLUSION and FUTURE WORK
Course enrollment (CE) as administrative task is a repetitive process which faces students each
semester. Students, during the enrollment period, often, need to support. At a time not so long ago, students
were responsible for their own choices. Since the students aim to finish their study as soon as they can taking
as many courses as possible, their choices might affect negatively on their performance. From this end,
colleges and universities began to implement so-called academic advising affairs. Although, the faculty
academic advising has a significant impact on a studentโs academic success, several issues may lead to limit
this success specially when the ratio of the academic advisors to the students is high.
In this paper, we have presented the course enrollment task as a function of maximization of GPA.
For this purpose, we have proposed a two-phased process. The first phase, is similar to those presented in
[23] where the most correlated courses are generated. At the second phase, the courses are prioritized based
on the student preferences. The students selection is influenced several factors which have been categorized
into three main groups of concerns: (i) course factors, (ii) social factors, and (iii) individual factors.
Through this work, we have evaluated our decision model in the context of department of
Information System at the Taibah University, Kingdom of Saudi Arabia. Since the collected data were from
different curriculum plans, our concern was focused only on one curriculum plan because the change in
perquisite courses will cause different recommended courses. In the future, we intend to integrate with
timetable system of the admission and registration deanship, and build an unified model that can deal with
different curriculum plans. Further analysis should cover more factors that influence the course selection
itself. It will be more appropriate to shift the research towards collaborative recommended systems and
cluster students with same preferences and analyze their behaviors. The date mining approach would also be
very interesting.
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BIOGRAPHY OF AUTHOR
Mohammed Al-Sarem is an assistant professor of information science at the Taibah University,
Al Madinah Al Monawarah, KSA. He received the PhD in Informatics from Hassan II
University, Mohammadia, Morocco in 2014. His research interests center on E-learning,
educational data mining, Arabic text mining, and intelligent and adaptive systems. He published
several research papers and participated in several local/international conferences