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STEVENSON, DANIEL E. "IMAGE RELATED APPLICATIONS FOR A CORE ALGORITHMS COURSE." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 05 (August 2001): 845–57. http://dx.doi.org/10.1142/s0218001401001167.
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Image related computations are becoming mainstream in today's computing environment. However, Computer Science departments still offer image related courses mostly as electives or at the graduate level. As imaging's importance increases, one needs to consider its coverage at the core courses. Most departments cannot afford to add another course into their curriculum at this level, so the only remaining choice is to integrate image related applications into the existing core courses. This paper addresses this issue as it relates to an Algorithms course. The specific prerequisites and goals of our Algorithms course are described. The image related applications that have been adopted are presented and their impact on the course is discussed.
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Ülker, Ezgi Deniz. "The effect of applying 4-stages on learning analysis and design of algorithms." Cypriot Journal of Educational Sciences 15, no. 5 (October 29, 2020): 1238–48. http://dx.doi.org/10.18844/cjes.v15i5.4621.
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The ability of analysing and designing an algorithm is quite essential for computer science education. The students in the Analysis and Design of Algorithms (ADA) course are expected to be able to solve problems by choosing one of the proper design methods and to analyse the algorithm’s performance in terms of various aspects. Instead of using traditional teaching approaches, a new 4-stage method is proposed to improve the concept of learning and teaching of algorithms. The instructor applied 4 consecutive stages during the tutorials and practical implementations. The students were asked a series of questions for evaluating not only themselves but also the new method and the instructor capability of handling the course by applying the 4-stages. According to the evaluations, the 4-stage method met the course objectives with noticeable rates and the course learning outcomes were achieved with high success rates when compared with the previous semester. In order to attain a sustainable achievement in teaching ADA, it is recommended to apply the 4-stage method every academic year. The application of 4-stages allows the instructor to be more efficient in programming teaching and the student more confident in programming, therefore it can be suggested for other programming related courses.
Keywords: Computer science education; Concepts of learning and teaching; Course learning outcomes; Programming teaching; Teaching approaches
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Atef Yekta, Hoda, and Robert Day. "Optimization-based Mechanisms for the Course Allocation Problem." INFORMS Journal on Computing 32, no. 3 (July 2020): 641–60. http://dx.doi.org/10.1287/ijoc.2018.0849.
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In recent years, several universities have adopted an algorithmic approach to the allocation of seats in courses, for which students place bids (typically by ordering or scoring desirable courses), and then seats are awarded according to a predetermined procedure or mechanism. Designing the appropriate mechanism for translating bids into student schedules has received attention in the literature, but there is currently no consensus on the best mechanism in practice. In this paper, we introduce five new algorithms for this course-allocation problem, using various combinations of matching algorithms, second-price concepts, and optimization, and compare our new methods with the natural benchmarks from the literature: the (proxy) draft mechanism and the (greedy) bidding-point mechanism. Using simulation, we compare the algorithms on metrics of fairness, efficiency, and incentive compatibility, measuring their ability to encourage truth telling among boundedly rational agents. We find good results for all of our methods and that a two-stage, full-market optimization performs best in measures of fairness and efficiency but with slightly worse incentives to act strategically compared with the best of the mechanisms. We also find generally negative results for the bidding-point mechanism, which performs poorly in all categories. These results can help guide the decision of selecting a mechanism for course allocation or for similar assignment problems, such as project team assignments or sports drafts, for example, in which efficiency and fairness are of utmost importance but incentives must also be considered. Additional robustness checks and comparisons are provided in the online supplement.
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Shu, Hong Ping, Kui Li, and Liang Cao. "Research on Algorithm of Conflict Detection and Resolution in Three-Dimension Free Flight Based on Genetic Algorithms." Applied Mechanics and Materials 121-126 (October 2011): 4586–90. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4586.
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The research 3d environment of Free Flight is discussed, the substance of the conflict detection and resolution in 3d Free Flight based on genetic algorithms is studied, and a mathematical model is constructed in the paper. The emulation results of applications show that the genetic algorithms is suitable and positive to detect and resolute the conflict in 3d Free Flight. The flight courses of aircrafts, which are as close as possible to the theoretical direct flight course of the least-fuel-consumption, can be generated by genetic algorithms.
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Alsaffar, Ali Hasan. "Empirical study on the effect of using synthetic attributes on classification algorithms." International Journal of Intelligent Computing and Cybernetics 10, no. 2 (June 12, 2017): 111–29. http://dx.doi.org/10.1108/ijicc-08-2016-0029.
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Purpose The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course. Design/methodology/approach The paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines. Findings The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable. Originality/value This paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.
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Amrulloh, Arif, and Enny Itje Sela. "Course scheduling optimization using genetic algorithm and tabu search." Jurnal Teknologi dan Sistem Komputer 9, no. 3 (June 15, 2021): 157–66. http://dx.doi.org/10.14710/jtsiskom.2021.14137.
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Scheduling courses in higher education often face problems, such as the clashes of teachers' schedules, rooms, and students' schedules. This study proposes course scheduling optimization using genetic algorithms and taboo search. The genetic algorithm produces the best generation of chromosomes composed of lecturer, day, and hour genes. The Tabu search method is used for the lecture rooms division. Scheduling is carried out for the Informatics faculty with four study programs, 65 lecturers, 93 courses, 265 lecturer assignments, and 65 classes. The process of generating 265 schedules took 561 seconds without any scheduling clashes. The genetic algorithms and taboo searches can process quite many course schedules faster than the manual method.
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MITSUBORI, Kunihiko, Takeshi KAMIO, and Takahiro TANAKA. "Finding the Shortest Course of a Ship Based on Reinforcement Learning Algorithm." Journal of Japan Institute of Navigation 110 (2004): 9–18. http://dx.doi.org/10.9749/jin.110.9.
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SANCHEZ, ANGEL, JOSE F. VELEZ, ANA BELEN MORENO, and JOSE L. ESTEBAN. "INTRODUCING ALGORITHM DESIGN TECHNIQUES IN UNDERGRADUATE DIGITAL IMAGE PROCESSING COURSES." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 05 (August 2001): 789–803. http://dx.doi.org/10.1142/s0218001401001155.
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This paper documents the development and first offering of an undergraduate course in Digital Image Processing at the Rey Juan Carlos University, Madrid (Spain). The paper describes how the appropriate introduction of main Algorithm Design Techniques can successfully assist the students to achieve a comprehensive understanding of image operations and related algorithms. Image processing problems offer a natural way to present real world problems where the students can use their algorithmic knowledge. Furthermore, image processing solutions are needed from a methodological development and require efficient well-designed algorithms. This paper presents an effort in the integration of Algorithm Design Techniques in a Digital Image Processing course with a very practical scope.
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Premalatha, M., and V. Viswanathan. "Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms." Cybernetics and Information Technologies 19, no. 3 (September 1, 2019): 25–44. http://dx.doi.org/10.2478/cait-2019-0024.
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Abstract Choice Based Course Selection (CBCS) allows students to select courses based on their preferred sequence. This preference in selection is normally bounded by constraints set by a university like pre-requisite(s), minimum and maximum number of credits registered per semester. Unplanned course sequence selection affects the performance of the students and may prolong the time to complete the degree. Course Difficulty Index (DI) also contributes to the decline in the performance of the students. To overcome these difficulties, we propose a new Subset Sum Approximation Problem (SSAP) aims to distribute courses to each semester with approximately equal difficulty level using Maximum Prerequisite Weightage (MPW) Algorithm, Difficulty Approximation (DA) algorithm and Adaptive Genetic Algorithm (AGA). The three algorithms have been tested using our university academic dataset and DA algorithm outperforms with 98% accuracy than the MPW and AGA algorithm during course distribution.
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FINK, EUGENE, and MICHAEL HEATH. "IMAGE-PROCESSING PROJECTS FOR AN ALGORITHMS COURSE." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 05 (August 2001): 859–68. http://dx.doi.org/10.1142/s0218001401001192.
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Courses on algorithm analysis often include little programming, and do not emphasize application of advanced techniques to practical problems. On the other hand, students usually prefer hands-on learning, and may lack motivation to study theory. We augmented an algorithms course with a series of programming tasks, which involved application of the course material to image processing. These tasks motivated the students, and led to better understanding and retention of theoretical material. They also enabled the students to learn the basics of representing and manipulating images, along with the algorithm theory.
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Rahadi, Andi Pujo, Angelica Marcelia Simbolon, and Ernest Beardly Pani. "APPLICATION OF GRAPH COLORING IN THE COURSE SCHEDULING." Abstract Proceedings International Scholars Conference 7, no. 1 (December 18, 2019): 1399–410. http://dx.doi.org/10.35974/isc.v7i1.815.
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Introduction: Courses scheduling is a very important activity in the teaching and learning process in the Mathematics Education Department. The teaching and learning process is carried out by all students and lecturers so that the schedule of courses arranged must benefit the lecturers and students. If some of the provisions in schedule are not properly calculated, it will cause difficulty during the scheduling process. These provisions include courses held, number of classes available, amount of time available, number of students taking and availability of lecturers who teach. This study aims to create automation on the course scheduling using six stages of FAST Methodology which include Scope Definition, Problem Analysis, Requirement Analysis, Logical Design, Physical Design, and Evaluation.
Method: The Welsh-Powell algorithm has used which is one of the algorithms in the graph coloring method and implemented using C ++ programming.
Result: The results of the study show that the program is able to provide a quick and accurate solution for setting up course schedules. The results of this study can provide convenience in managing the right course scheduling so that it helps the course scheduling process to be even better.
Discussion: Further research can be done using other graph coloring algorithms or by performing an analysis of the ease of use of the resulting program.
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Han, Bing, Zhen Wang, and Zhilong Zhao. "Quality Evaluation Method of College Sports Long Jump Training Course Based on Genetic Algorithm." Mobile Information Systems 2022 (June 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/7905389.
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In order to improve the accuracy of the quality evaluation of college sports long jump training courses, this paper studies the quality evaluation method of college sports long jump training courses based on a genetic algorithm. According to the purpose of college long jump training course evaluation, basic principles of teaching quality evaluation and classroom teaching evaluation standards constructs the establishment process of evaluation index system and calculates the weight of long jump training course quality evaluation index based on AHP method, so as to provide index basis for subsequent quality evaluation; The quality evaluation model of the long jump training course is constructed based on the genetic algorithm. Through cross operation and mutation operation based on adaptive mutation probability, the high accuracy evaluation of college sports long jump training course quality is realized. The experimental results show that the evaluation accuracy of this design method is higher than 97.21%, and the evaluation satisfaction is higher than 97.15%, which significantly improves the accuracy of the university sports long jump training courses and has practical significance in the course evaluation.
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Et. al., Wilda Susanti,. "An Overviuw Of The Teaching And Learning Process Basic Programming In Algorithm And Programming Courses." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 2934–44. http://dx.doi.org/10.17762/turcomat.v12i2.2332.
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In this paper, we review the literature related to computer programming learning, where Algorithms and Programming are the topic domains of the Informatics and Computer science clusters. There are 4 competencies in learning outcomes, such as: 1) understand algorithmic concepts; 2) master algorithm concepts and principles; 3) master programming language concepts; and 4) master programming languages and algorithms. The main focus of this review is on beginner programming and topics related to student difficulties in learning programming. Various problems experienced by beginners were identified from the literature to some of the solutions offered by researchers.
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Ren, Xinwei, Wei Yang, Xianliang Jiang, Guang Jin, and Yan Yu. "A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention." Sustainability 14, no. 5 (March 2, 2022): 2907. http://dx.doi.org/10.3390/su14052907.
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With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.
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Nugroho, Arief Kelik, Ipung Permadi, and Ana Romadhona Yasifa. "OPTIMIZING COURSE SCHEDULING FACULTY OF ENGINEERING UNSOED USING GENETIC ALGORITHMS." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 7, no. 2 (February 18, 2022): 91–98. http://dx.doi.org/10.33480/jitk.v7i2.2262.
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In carrying out an activity regularly and smoothly, it is necessary to make an activity schedule that can manage the time of one activity with another so that unwanted things do not happen such as the same time, the same place, and others. Making a schedule of activities is quite easy to do if there are not too many entities involved and if the entities are not tied to each other, but for larger cases, creating a schedule of activities manually will take quite a lot of time and can result in errors in the schedule or shortages. effectiveness in the resulting schedule. This is commonly experienced in making course schedules at universities because there are a lot of course data and lecturers can teach several courses at once and at different times, therefore in making course schedules can be done by applying genetic algorithms so that the time required needed in making the course schedule shorter and the results obtained can be more optimal than the results of making the course schedule manually. In this study, the optimal course schedule was obtained in the 31st generation using data on rooms, courses, study time, lecturers, and departments so that one chromosome has 154 gen, then the population length is made up to 9 individuals or chromosomes, the mutation rate is set at 0.1, and the method used in the individual selection stage is the tournament selection method where the tournament size is set at 3. The fitness value is taken so that a schedule is said to be optimal, i.e. if the fitness value is equal to 1 because then it shows that there are no errors or problems (such as time, lecturers, conflicting rooms) that occur in the schedule.
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Krivoplyasova, E. V., V. Yu Nefyodova, and A. V. Prilepina. "Methods of teaching the basics of programming in Python." Informatics in school, no. 3 (May 14, 2020): 24–30. http://dx.doi.org/10.32517/2221-1993-2020-19-3-24-30.
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The article discusses the main trends in the study of programming languages in the school course of informatics. The programming languages (Basic, Pascal, C, Python, algorithmic language) that are acceptable for use at the Unified State Exam in informatics and ICT and their main features are listed. The analysis of the use of the proposed programming languages in educational and methodological kits on informatics is carried out. The program of the elective course for teaching the basics of programming in Python is proposed. It contains an explanatory note, goals and objectives of the course, thematic planning, course content, expected learning outcomes. The methodical material on the theme "Programming of branching algorithms" in Python is considered.
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Moglan, V. D. "Didactic potential of using systems for visualization of algorithms in the process of teaching programming." Open Education 23, no. 2 (May 14, 2019): 31–41. http://dx.doi.org/10.21686/1818-4243-2019-2-31-41.
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The article is devoted to the didactic potential on the use of the visualizer of algorithms as software, which graphically demonstrates the work of algorithms for processing input data. There are described the difficulties arising during the study of the fundamentals of algorithmization and programming. The author proposed to use the algorithm visualizer, as an auxiliary visual means of teaching algorithmization, for more successful mastering the mechanism of the algorithms at lectures. The article discusses the functional requirements for the visualizer of algorithms, describes its main elements and guidelines for the use of visualizers of algorithms in the educational process. In order to increase the effectiveness of the educational process, it is proposed to use the visualizer of algorithms for teaching algorithms and programming for students of the “Applied Informatics” direction as future specialists in the information technologies.The purpose of the study is to increase the efficiency of the development of algorithmic thinking among students of the specialty “Computer Science”, due to the support and active use of the algorithm visualizer as an additional visual tool for learning when studying the course “Fundamentals of Programming”.Materials and research methods include the analysis of scientific and methodological literature on the subject under study, the analysis of the problem of developing algorithmic thinking by students, the practical implementation of the learning process of the basics of algorithmization and programming using the developed algorithms visualizer, the analysis of students’ survey results.Results. The research conducted by the author allowed the creation of an instrumental environment an algorithm visualizer that supports the formation of problem solving skills, enhancing the effectiveness of the learning process in the fundamentals of algorithmization and programming and the development of algorithmic thinking. Have been implemented visualizers, that allowing online demonstration of the work of algorithms for processing static data structures (arrays), which contributes to the comprehension and consolidation of theoretical material. Visualizers are introduced into the educational process at Balti State University A. Russo and are used in studying the course “Fundamentals of Programming”.Conclusion. The author comes to the conclusions about the expediency of the visual description of the algorithm, proposes methods for using the algorithms visualizer in the educational process, and highlights positive factors that affect the understanding the mechanism of algorithm execution when organizing training using visualizers.
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Stokes, Thomas A., Douglas J. Gillan, and Jeffery P. Braden. "Establishing the Link Between Usability and Student Satisfaction in Adaptive Online Learning." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 1976–80. http://dx.doi.org/10.1177/1541931213601450.
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Online courses present a new element to learners in college courses. Interfaces (web pages) take the place of an instructor as the primary information delivery system. In other words, a student’s learning experience is now tied to the quality of a course’s human- computer interaction. One emerging method of online course delivery is an adaptive course that tailors to individual students needs, abilities, or preferences. There has been much work done on the algorithms that allow the course to adapt to individual students, but there seems to be a lack of research into the usability of these interfaces and how their quality affects student performance and satisfaction. This paper presents some of the data that was collected in a larger, grant-supported project and establishes relationships between usability metrics (ease of use and perceived usefulness) and student satisfaction and outcome measures in adaptive-online courses.
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Djordjevic-Kajan, S., Dragan Stojanovic, and Aleksandar Stanimirovic. "Advanced System Software curricula." Facta universitatis - series: Electronics and Energetics 18, no. 2 (2005): 309–17. http://dx.doi.org/10.2298/fuee0502309d.
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An advanced System Software curricula at the Faculty of Electronic Engineering in Nis is presented in this paper. The system software track consists of two important themes of Computer Science and Computing in General organized now as two separated courses: Operating Systems course and System Software Development and System Programming course. Both courses offer extensive teaching of foundational concepts and principles of Operating Systems and System Programming along with design and implementation of presented topics in real operating systems and system software, such as Unix, Linux and Windows 2000/XP. Laboratory environments and exercises for both courses offer both examination of main algorithms and structures within operating systems and system software through simulation, and what is more important, hands-on experience with operating system internals and code.
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Fang, Yuan, and Jingning Li. "Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course." Computational Intelligence and Neuroscience 2021 (December 16, 2021): 1–11. http://dx.doi.org/10.1155/2021/8249625.
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This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.
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Huang, Tong Cheng, and Xin Liang Liu. "Research and Implementation of Dynamic Synchronous Visual Teaching System for Data Structure Algorithms." Advanced Materials Research 433-440 (January 2012): 4114–19. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4114.
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Data Structure is the specialized core course for students majoring in Information Science. It is very important for students to study succesor courses and enhance their programming level. This paper presented the authors’ actual achievement in research and practice of dynamic synchronous visual teaching system for Data Structure algorithms.
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Puspitasari, Fani, and Parwadi Moengin. "Penerapan Metode Hybrid Genetic Algorithm (GA) dan Pattern Search (PS) untuk Penjadwalan Mata Kuliah Universitas." Jurnal Rekayasa Sistem Industri 9, no. 3 (October 27, 2020): 201–12. http://dx.doi.org/10.26593/jrsi.v9i3.4093.201-212.
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The problem of university course scheduling is a complicated job to do because of the many constraints that must be considered, such as the number of courses, the number of rooms available, the number of students, lecturer preferences, and time slots. The more courses that will be scheduled, the scheduling problem becomes more complex to solve. Therefore, it is necessary to set an automatic course schedule based on optimization method. The aim of this research is to gain an optimal solution in the form of schedule in order to decrease the number of clashed courses, optimize room utilization and consider the preferences of lecturer-course. In this research, a hybridization method of Genetic Algorithm (GA) and Pattern Search (PS) is investigated for solving university course scheduling problems. The main algorithm is GA to find the global optimum solution, while the PS algorithm is used to find the local optimum solution that is difficult to obtain by the GA method. The simulation results with 93 courses show that the Hybrid GA-PS method works better than does the GA method without hybrid, as evidenced by the better fitness value of the hybrid GA-PS method which is -3528.62 and 99.24% of the solutions achieved. While the GA method without hybrid is only able to reach a solution of around 65% and has an average fitness value of -3100.76.
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Wei, Pengxu, Ruixue Bao, and Yubo Fan. "Comparing the reliability of different ICA algorithms for fMRI analysis." PLOS ONE 17, no. 6 (June 27, 2022): e0270556. http://dx.doi.org/10.1371/journal.pone.0270556.
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Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.
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Mone, Ferdinandus, and Justin Eduardo Simarmata. "APLIKASI ALGORITMA GENETIKA DALAM PENJADWALAN MATA KULIAH." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 4 (December 1, 2021): 615–28. http://dx.doi.org/10.30598/barekengvol15iss4pp615-628.
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Making a class schedule becomes problem and takes a long time because of several obstacles such as the lack of lecture rooms, the lack of teaching staff, and the high of courses available in one semester. This study aims to apply genetic algorithms in making class schedules to facilitate the process of making class schedules. The method used is the waterfall method with the stages of the Software Development Life Cycle. The results of the application of genetics application show that the process of making course schedules can overcome the constraints of 1) space and time clashes, 2) lecturer conflicts, 3) Friday prayer times clashing, 4) there is a time when the lecturer wants for certain reasons, and 5) practicum in the laboratory room. By passing these constraints, the application of genetic algorithms in course scheduling is categorized as effective. Based on the results of running on 51 lecturers (51 chromosomes), the average running time 30 times in a row is 25.86 minutes so that the use of genetic algorithm applications in scheduling courses is efficient.
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Chinn, Donald. "Peer assessment in the algorithms course." ACM SIGCSE Bulletin 37, no. 3 (September 2005): 69–73. http://dx.doi.org/10.1145/1151954.1067468.
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D'Antonio, Lawrence. "Incorporating bioinformatics in an algorithms course." ACM SIGCSE Bulletin 35, no. 3 (September 2003): 211–14. http://dx.doi.org/10.1145/961290.961569.
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Papalaskari, Mary-Angela. "Peer evaluation in an algorithms course." ACM SIGCSE Bulletin 35, no. 3 (September 2003): 274. http://dx.doi.org/10.1145/961290.961639.
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Li, Zian, and Cui Zhao. "KNN algorithm of early warning system for applied research course." E3S Web of Conferences 218 (2020): 01030. http://dx.doi.org/10.1051/e3sconf/202021801030.
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This paper designed a small course early warning system based on KNN algorithm, aimed at students haven’t finished the course of time can be completed by yourself some predict their courses by chance. In this paper, the basic principle of KNN algorithm is briefly introduced, and the course warning system is modified by Manhattan distance with added weights. This paper briefly describes the basic framework of this model and introduces the application of KNN algorithm in this model. Through a large number of basic experimental data to test the training, using figures to show, finally get the curriculum early warning system model, to achieve the effect of curriculum early warning.
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Malik, Sohail Iqbal, Ragad Tawafak, Ghaliya Alfarsi, Mohammed Waseem Ashfaque, and Roy Mathew. "A Model for Enhancing Algorithmic Thinking in Programming Education using PAAM." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 09 (May 4, 2021): 37. http://dx.doi.org/10.3991/ijim.v15i09.20617.
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Most novice programmers consider learning to program as a difficult and challenging field of study for them. As a result, high dropout and failure rates in programming one courses reported. One of the reasons is that most programming (1) courses don’t give equal attention to syntax and semantics of programming language and algorithmic thinking. In this study, a web application based on Problem Analysis and Algorithmic Model (PAAM) was prepared and acquainted in the programming (1) course. The application focuses on problem analysis and algorithmic thinking. The influence on genders' opinion after offering the PAAM model in the programming (1) course determined by organizing a survey. The mean values of the male and female survey respondents compared by performing the T-test. The purpose is to determine if there is any significant difference between the mean values. Results show that students appreciated the web application in the programming (1) course. Male students discerned more positive responses in the survey questions compared to female students. The T-test result shows a significant difference between the values of respondents because the p-value for equal variances assumed is (.000), which is less than p = 0.05. The application encourages a new approach based on four steps (problem statement → problem analysis → problem-solving skills → code) for novices. The application helps students to understand programming structures, program design and comprehension.
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Pilotti, Maura A. E., Emaan Nazeeruddin, Mohammad Nazeeruddin, Ibtisam Daqqa, Hanadi Abdelsalam, and Maryam Abdullah. "Is Initial Performance in a Course Informative? Machine Learning Algorithms as Aids for the Early Detection of At-Risk Students." Electronics 11, no. 13 (June 30, 2022): 2057. http://dx.doi.org/10.3390/electronics11132057.
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The extent to which grades in the first few weeks of a course can predict overall performance can be quite valuable in identifying at-risk students, informing interventions for such students, and offering valuable feedback to educators on the impact of instruction on learning. Yet, research on the validity of such predictions that are made by machine learning algorithms is scarce at best. The present research examined two interrelated questions: To what extent can educators rely on early performance to predict students’ poor course grades at the end of the semester? Are predictions sensitive to the mode of instruction adopted (online versus face-to-face) and the course taught by the educator? In our research, we selected a sample of courses that were representative of the general education curriculum to ensure the inclusion of students from a variety of academic majors. The grades on the first test and assignment (early formative assessment measures) were used to identify students whose course performance at the end of the semester would be considered poor. Overall, the predictive validity of the early assessment measures was found to be meager, particularly so for online courses. However, exceptions were uncovered, each reflecting a particular combination of instructional mode and course. These findings suggest that changes to some of the currently used formative assessment measures are warranted to enhance their sensitivity to course demands and thus their usefulness to both students and instructors as feedback tools. The feasibility of a grade prediction application in general education courses, which critically depends on the accuracy of such tools, is discussed, including the challenges and potential benefits.
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Hussain, Mushtaq, Wenhao Zhu, Wu Zhang, and Syed Muhammad Raza Abidi. "Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores." Computational Intelligence and Neuroscience 2018 (October 2, 2018): 1–21. http://dx.doi.org/10.1155/2018/6347186.
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Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study includedhighest education level,final results,score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which includeddataplus,forumng,glossary,oucollaborate,oucontent,resources,subpages,homepage,and URLduring the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.
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Paul, Dimple V., and Chitra S. Nayagam. "Use of Subspace Clustering Algorithm for Students' Competency and Subject Knowledge Assessment." International Journal of Knowledge and Systems Science 9, no. 2 (April 2018): 70–83. http://dx.doi.org/10.4018/ijkss.2018040104.
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Student performance studies are the primary challenge for any course with continuous assessment. The challenge lies in performing validation tests of whether course objectives are being met and also in identifying areas of the course structure that needs improvement. This article identifies whether objectives of the course are being achieved or not, by analyzing the student performance in different courses using competencies as the criteria for assessment. Performance evaluation includes diverse types of competency components such as presentation, assignment, group discussion, etc., along with written examination in order to assess the knowledge of students, as well as their interest in the subject. A PROCLUS algorithm has been chosen for experimentation, as the algorithm identifies similarities among data sets and forms clusters of disjoint sets. The algorithm not only considers random sample points, but also successfully scans entire data sets to identify meaningful dimensions that are needed to form actual clusters. Experimental results have identified the similarities of the students' performance across the subjects that are similar in nature and their competency parameters were also found to be similar. A majority of the students have performed alike in certain subjects that involved practical components or in other ways, similar performance is achieved during the assessment of courses on competencies like presentations skills, group discussions, writing skills, etc., rather than mere theoretical components. This study could help to modify the evaluation and assessment pattern for the theory subjects and/or to fine tune the course structure and objectives of such course, and also to find some alternate techniques to improve the other competencies.
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Shamaeva, E. F., E. M. Bryukhova, A. V. Lyubimova, and E. R. Tolmacheva. "Algorithm for developing and implementing online educational programs using the example of open video lessons of the GIS INTEGRO course." Geoinformatika, no. 3 (2020): 10–19. http://dx.doi.org/10.47148/1609-364x-2020-3-10-19.
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This article focuses on how to develop educational courses. As an example illustrating the proposed approach, an algorithm for implementing the applied on-line course «Fundamentals of Using GIS Technologies» is proposed, which can be included in the educational program of any university whose activities are related to Earth sciences, as well as used in advanced training programs for specialists. As a tool for preparing the course, open video lessons on GIS INTEGRO published on the developer's website are used. The article discloses the content of training materials, considers the main aspects of organizing the educational process: planning the educational schedule, overseeing and informational support of participants in the course of training, verification of knowledge, gaming techniques, as well as a set of necessary technical programs. The methods described in the article could help not only to develop the course, but also to launch an educational program on it. Keywords: geoinformation technologies, video-lessons of GIS INTEGRO, efficiency of mass open educational courses (MOOC), architecture of online courses, ecosystem of MOOC.
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Hoshino, Richard, and Caleb Raible-Clark. "The Quest Draft: an Automated Course Allocation Algorithm." Proceedings of the AAAI Conference on Artificial Intelligence 28, no. 2 (July 27, 2014): 2906–13. http://dx.doi.org/10.1609/aaai.v28i2.19025.
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Course allocation is one of the most complex issues facing any university, due to the sensitive nature of deciding which subset of students should be granted seats in highly-popular (market-scarce) courses. In recent years, researchers have proposed numerous solutions, using techniques in integer programming, combinatorial auction design, and matching theory. In this paper, we present a four-part AI-based course allocation algorithm that was conceived by an undergraduate student, and recently implemented at a small Canadian liberal arts university. This new allocation process, which builds upon the Harvard Business School Draft, has received overwhelming support from students and faculty for its transparency, impartiality, and effectiveness.
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ÇAM, Emre, and Muhammet Esat ÖZDAĞ. "Discovery of Course Success Using Unsupervised Machine Learning Algorithms." Malaysian Online Journal of Educational Technology 9, no. 1 (December 28, 2020): 26–47. http://dx.doi.org/10.17220/mojet.2021.9.1.242.
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This study aims at finding out students’ course success in vocational courses of computer and instructional technologies department by means of machine learning algorithms. In the scope of the study, a dataset was formed with demographic information and exam scores obtained from the students studying in the Department of Computer Education and Instructional Technology at Gaziosmanpasa University. 127 students, who took the courses of Programming Languages I and Programming Languages II, participated in the study. Model that was suggested in the study was implemented using open source coded Keras library. Students were split into clusters by K-means and Deep Embedded Clustering algorithms which are unsupervised machine learning algorithms. Effect of the attributes that enabled clustering was identified by Kruskal Wallis test. With this study, a model that helps educators and instructional designers build skills for predicting, assures discovering success patterns through data mining and facilitates assisting in the stages of lesson planning was proposed.
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Chen, Haijian, Dongmei Han, Yonghui Dai, and Lina Zhao. "Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs." Computational Intelligence and Neuroscience 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/123028.
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In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of “C programming language” are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate.
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Başar, Mehmet Sinan, and Sinan Kul. "A student-based central exam scheduling model using A* algorithm." Open Computer Science 12, no. 1 (January 1, 2022): 181–90. http://dx.doi.org/10.1515/comp-2022-0237.
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Abstract In this study, a student-based placement model using the A* algorithm is proposed and applied to solve the problem of placing the courses in exam sessions. The application area of the model is midterm and final exams, conducted by the Open Education Faculty. The reason for choosing open education exams for the practice is that the exams are applied across the country and more than 100,000 students participate. The main problem is to obtain a suitable distribution that can satisfy many constraints simultaneously. In the current system, the lessons in the sessions were placed once using the curriculum knowledge. This placement plan is applied in all exams. When the placement is done according to the curriculum information, the courses in the sessions cannot be placed effectively and efficiently due to a large number of common courses and the large number of students taking the exam. This makes the booklets more expensive and the organization more prone to errors. Both the opening of new programs and the increase in the number of students regularly lead to the necessity of placing the classes in sessions dynamically each semester. In addition, to prevent conflicts with the calendars of other central exams, it is necessary to conduct all exams in three sessions. A better solution was obtained by using a different model than the currently used model in the study. With this solution, distribution of the courses of successful students with few courses to all sessions is provided, and difficult courses of unsuccessful students who have a large number of courses were gathered in the same session. This study can support future studies on two issues: the first issue is the approach of using the course that will be taken by most students instead of the courses taught in most departments in the selection of the course to be placed in the booklet. The second issue is to try to find the most suitable solution by performing performance tests on many algorithms whose performance has been determined by many academic studies.
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Ignatjeva, Olga A. "Algocracy in the Political System: Pro & Contra." Socialʹnye i gumanitarnye znania 8, no. 2 (June 18, 2022): 138. http://dx.doi.org/10.18255/2412-6519-2022-2-138-145.
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The development of information and communication technologies leads to new technological phenomena, such as artificial intelligence, which makes it possible to increase the capacity of the human brain to process information and make decisions. Over the last twenty years, the information collection system has become more complex, its volume is growing exponentially, and new ways of processing information, such as neural networks and machine learning, are appearing. Technologies are rapidly changing and developing, and of course the public administration system takes them to improve the accuracy of forecasts and the quality of decision-making in various policy sectors. Algorithmic governance is referred to by the term algocracy. However, not all of the public administration system works with the use of machine learning algorithms remain areas where traditional decision-making methods persist. Despite the progressiveness of algorithmic governance there are still a number of problematic aspects that require ethical and legislative regulation of algorithmic governance. The purpose of this article is to analyze the essence of governance based on the use of big data algorithms and to present a classification of the advantages and risks of an automated data analysis system for public administration.
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Liu, Guan-Chen, and Chih-Hsiang Ko. "Exploring Multiple Application Scenarios of Visual Communication Course Using Deep Learning Under the Digital Twins." Computational Intelligence and Neuroscience 2022 (February 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/5844290.
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The emergence of intelligent technology has brought a particular impact and allows for virtuality-reality interaction in the educational field. In particular, digital twins (DTs) feature virtuality-reality symbiosis, solid virtual simulation, and high real-time interaction. It has also seen extended applications to the field of education. This study optimizes the design of the visual communication (Viscom) course based on the deep learning (DL) algorithm. Firstly, the theory of DL is analyzed following the relevant literature, and the typical DL networks, network structures, and related algorithms are introduced. Secondly, Viscom technology is expounded, and DL technology is applied to the Viscom course. Then, the applicability and feasibility of DL in the Viscom course are analyzed through a questionnaire survey (QS) design by collecting students’ attitudes towards Viscom courses before and after the experiment. After introducing DL into the Viscom course, the results show that students’ learning interest and satisfaction with the practical knowledge mastery have increased. However, the satisfaction with theoretical knowledge mastery before practical courses has decreased; overall, the teaching effect of the Viscom course has been improved. Therefore, the introduction of DL into the DT-enabled Viscom can provide a reference for developing the Viscom course. The research content offers technical support (TS) for integrating DT technology and modern education.
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Singh, Anuj Kumar, Sandeep Kumar, Shashi Bhushan, Pramod Kumar, and Arun Vashishtha. "A Proportional Sentiment Analysis of MOOCs Course Reviews Using Supervised Learning Algorithms." Ingénierie des systèmes d information 26, no. 5 (October 31, 2021): 501–6. http://dx.doi.org/10.18280/isi.260510.
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When anyone is looking to enroll for a freely available online course so the first and famous name comes in front of the searcher is MOOC courses. So here in this article our focus is to collect the comments by enrolled users for the specified MOOC course and apply sentiment analysis over that data. The significance of our article is to introduce a proficient sentiment analysis algorithm with high perceptive execution in MOOC courses, by seeking after the standards of gathering various supervised learning methods where the performance of various supervised machine learning algorithms in performing sentiment analysis of MOOC data. Some research questions have been addressed on sentiment analysis of MOOC data. For the assessment task, we have investigated a large no of MOOC courses, with the different Supervised Learning methods and calculated accuracy of the data by using parameters such as Precision, Recall and F1 Score. From the results we can conclude that when the bigram model was applied to the logistic regression, the Multilayer Perceptron (MLP) overcomes the accuracy by other algorithms as SVM, Naive Bayes and achieved an accuracy of 92.44 percent. To determine the sentiment polarity of a sentence, the suggested method use term frequency (No of Positive, Negative terms in the text) to calculate the sentiment polarity of the text. We use a logistic regression Function to predict the sentiment classification accuracy of positive and negative comments from the data.
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Yang, Haiying. "Mining and Evaluation of Teaching Civics Courses Based on Apriori-TIDS Algorithm." Journal of Sensors 2021 (December 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/5956429.
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The thinking course is an innovative move to implement the fundamental task of moral education and realize the whole process of education and all-round education in universities. The Apriori-TIDS algorithm proposed in this paper adopts the TID list of transaction identifiers to calculate the support count and generate the frequent item set of the Hou option set, and the whole frequency set generation process only needs to scan the transaction database once, which greatly improves the operation efficiency of the mining algorithm. The course is based on the three focus points of ideological and political education, such as “matters, times, and situations”, to explore the elements of ideological and political education hidden in the course, and to give the principles and criteria for evaluating the effectiveness of ideological and political teaching in the course, in order to make the professional degree course become the main channel to lead the ideological and political education of postgraduate courses and improve the effectiveness of the course in educating people.
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Narayanasamy, Senthil Kumar, and Atilla Elçi. "An Effective Prediction Model for Online Course Dropout Rate." International Journal of Distance Education Technologies 18, no. 4 (October 2020): 94–110. http://dx.doi.org/10.4018/ijdet.2020100106.
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Due to tremendous reception on digital learning platforms, many online users tend to register for online courses in MOOC offered by many prestigious universities all over the world and gain a lot on cutting edge technologies in niche courses. As the reception of online courses is increasing on one side, there have been huge dropouts of participants in the online courses causing serious problems for the course owners and other MOOC administrators. Hence, it is deemed necessary to find out the root causes of course dropouts and need to prepare a workable solution to prevent that outcome in the future. In this connection, the authors made use of three machine learning algorithms such as support vector machine, random forest, and conditional random fields. The huge samples of datasets were downloaded from the Open University of China, that is, almost 7K student profiles were extracted for the empirical analysis. The datasets were loaded into a confusion matrix and analyzed for the accuracy, precision, recall, and f-score of the model.
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Silapachote, Piyanuch, and Ananta Srisuphab. "Engineering Courses on Computational Thinking Through Solving Problems in Artificial Intelligence." International Journal of Engineering Pedagogy (iJEP) 7, no. 3 (September 29, 2017): 34. http://dx.doi.org/10.3991/ijep.v7i3.6951.
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Computational thinking sits at the core of every engineering and computing related discipline. It has increasingly emerged as its own subject in all levels of education. It is a powerful cornerstone for cognitive development, creative problem solving, algorithmic thinking and designs, and programming. How to effectively teach computational thinking skills poses real challenges and creates opportunities. Targeting entering computer science and engineering undergraduates, we resourcefully integrate elements from artificial intelligence (AI) into introductory computing courses. In addition to comprehension of the essence of computational thinking, practical exercises in AI enable inspirations of collaborative problem solving beyond abstraction, logical reasoning, critical and analytical thinking. Problems in machine intelligence systems intrinsically connect students to algorithmic oriented computing and essential mathematical foundations. Beyond knowledge representation, AI fosters a gentle introduction to data structures and algorithms. Focused on engaging mental tool, a computer is never a necessity. Neither coding nor programming is ever required. Instead, students enjoy constructivist classrooms designed to always be active, flexible, and highly dynamic. Learning to learn and reflecting on cognitive experiences, they rigorously construct knowledge from collectively solving exciting puzzles, competing in strategic games, and participating in intellectual discussions.
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Hundhausen, Christopher D. "Integrating algorithm visualization technology into an undergraduate algorithms course: ethnographic studies of a social constructivist approach." Computers & Education 39, no. 3 (November 2002): 237–60. http://dx.doi.org/10.1016/s0360-1315(02)00044-1.
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Luo, Yong, Guochang Zhou, Jianping Li, and Xiao Xiao. "A MOOC Video Viewing Behavior Analysis Algorithm." Mathematical Problems in Engineering 2018 (October 16, 2018): 1–7. http://dx.doi.org/10.1155/2018/7560805.
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MOOCs (massive open online courses) are developing rapidly, but they also face many problems. As the MOOC’s most important resource, the course videos have a very important influence on the learning. This article defines the ratio R (R=Average viewing duration/Video length), which reflects the popularity of the video. By analyzing the relationship between the video length, release time, and R, we found a significant negative linear correlation between video length and R and video release time and R. However, when the number of videos is less than the threshold, the release time has less influence on R. This paper presents a video viewing behavior analysis algorithm based on multiple linear regression. The residual independence test proved that the algorithm has a good approximation to the data. It can predict the popularity of similar course videos to help producers optimize video design.
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Zhu, Qianyao. "Network Course Recommendation System Based on Double-Layer Attention Mechanism." Scientific Programming 2021 (December 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/7613511.
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In view of the lack of accurate recommendation and selection of courses on the network teaching platform in the new form of higher education, a network course recommendation system based on the double-layer attention mechanism is proposed. First of all, the collected data are preprocessed, while the data of students and course information are normalized and classified. Then, the dual attention mechanism is introduced into the parallel neural network recommendation model so as to improve the model’s ability to mine important features. TF-IDF (term frequency-inverse document frequency) based on the student score and course category is improved. The recommendation results are classified according to the weight of course categories, so as to construct different types of course groups and complete the recommendation. The experimental results show that the proposed algorithm can effectively improve the model recommendation accuracy compared with other algorithms.
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Ramaswami, Gomathy, Teo Susnjak, and Anuradha Mathrani. "On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining." Big Data and Cognitive Computing 6, no. 1 (January 7, 2022): 6. http://dx.doi.org/10.3390/bdcc6010006.
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Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for supporting these students in overcoming their learning difficulties. However, the majority of studies have taken the approach of developing individual models that target a single course while developing prediction models. These models are tailored to specific attributes of each course amongst a very diverse set of possibilities. While this approach can yield accurate models in some instances, this strategy is associated with limitations. In many cases, overfitting can take place when course data is small or when new courses are devised. Additionally, maintaining a large suite of models per course is a significant overhead. This issue can be tackled by developing a generic and course-agnostic predictive model that captures more abstract patterns and is able to operate across all courses, irrespective of their differences. This study demonstrates how a generic predictive model can be developed that identifies at-risk students across a wide variety of courses. Experiments were conducted using a range of algorithms, with the generic model producing an effective accuracy. The findings showed that the CatBoost algorithm performed the best on our dataset across the F-measure, ROC (receiver operating characteristic) curve and AUC scores; therefore, it is an excellent candidate algorithm for providing solutions on this domain given its capabilities to seamlessly handle categorical and missing data, which is frequently a feature in educational datasets.
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YAPICI, Mutlu, and Ömer Faruk BAY. "Genetic Algorithm Based Timetabling Program." Artificial Intelligence Studies 2, no. 2 (December 26, 2020): 20–31. http://dx.doi.org/10.30855/ais.2019.02.02.01.
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Course Timetabling Problem is concerned with assigning a number of courses and instructors to classrooms by taking the constraints into consideration. Generally, this problem is typically resolved manually; and due to the large variety of constraints, resource limitations and complicated human factors involved, it takes a lot of time and manpower. It is considered as one of the most time-consuming problems faced by universities and colleges today. In this study, we aimed to develop a genetic algorithm-based timetabling software to bring a solution to course timetabling problem, which is a real world problem. This software allows constraints to be entered easily and allows that optimal solutions are found. To find the most suitable solution for optimization, two different solution methods, a full-genetic algorithm and a partial-genetic algorithm, were tested. Test results showed that when we start the full genetic algorithms from randomly generated initial population, it takes quite some time to obtain the appropriate solution. With the partial-genetic algorithm, an optimal solution was achieved much more quickly than the full genetic algorithm.
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Kafandaris, Stelios, and Saul I. Gass. "Decision Making and Algorithms - A First Course." Journal of the Operational Research Society 38, no. 3 (March 1987): 297. http://dx.doi.org/10.2307/2581952.
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Kafandaris, Stelios. "Decision Making and Algorithms - A First Course." Journal of the Operational Research Society 38, no. 3 (March 1987): 297. http://dx.doi.org/10.1057/jors.1987.48.
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