Applying Genetic Algorithms To Improve Students ...

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Applying Genetic Algorithms To Improve Students’ Academic Performance By Group Formation 1

N. Devasenathipathi, 2Dr. Nilesh K. Modi 1 Assistant Professor, Sardar Vallabhbhai Patel Institute of Technology, Vasad, Gujarat and Ph.D. student, R & D Centre, Bharathiar University, Coimbatore. 2 Professor and Head, S.V. Institute of Computer Studies, Kadi, Gujarat ABSTRACT Group work is highly synchronous as good way of learning and performing work. In many firms, the reason for their success is effective team work. It is highly necessary that students be taught to learn and perform in a group. Yet, creating groups which has correct coupling cohesion is a difficult task. Student groups are created having some constraints in mind. However, when groups are created by individuals (teachers), many times either the constraints are relaxed, forgotten or some other form of bias is done in the creation of student groups. In connection to this, a student‟s academic performance, while being in a group environment, depends upon two factors. The first factor is the grasping level of the given student and the second one is the contribution level of other students in that particular group. With these two factors, a formula to predict the future academic score of a student among a given set of students as his group members was found out. This paper presents a method to form optimal groups of students using genetic algorithms with the above constraints. Keywords: Group formation, Students‟ academic performance, Genetic algorithms, Group learning, Constraints satisfaction, Academic performance prediction. 1. INTRODUCTION No one is perfect in as aspects of work. But industry demands perfection. This gave rise to the specialization of fields. Professionals specialize in particular disciplines (fields) and form groups among them to complete a project assignment with perfection. Moreover, group work helps in decreasing individuals‟ workload, thereby aiding in the variety

of roles in an organization. With this, organizations have started to look for students (future professionals) who are able to share skills and knowledge, communicate their ideas effectively and collaborate with other members of their team. This, searching of students, lead to design the curriculum (syllabus) such that incorporates the use of team projects to help students in developing these basic skills which are the need for success in any business organization [10]. Educational organizations view group work as a model for making students to get into the industry (i.e. seeking placements). Moreover, they believe that group work helps in managing and solving problems more efficiently. The result is that academic institutions have started to incorporate subjects/courses which require group formation among students. This helps the students in sharing their knowledge and skills, boosting problem solving skills and finally improving the communication skills[10]. But it is often noticed that not all student groups succeed because of its non-systematic formation. To make group development and performance a success proper group formation method is very essential. This research paper is framed into six sections with introduction being the first section. The second section provides a glimpse of some of the work done previously in association to the method of group formation and the success of group work in education. In section three, we discuss the working of genetic algorithms and application of genetic algorithms in students‟ group formation. In section four (implementation section), we discuss the actual experiment undertaken and arrived results. The next

IFRSA International Journal of Data Warehousing & Mining |Vol1|issue 2|Nov 2011

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N. Devasenathipathi, N K. Modi | Applying Genetic Algorithms To Improve Students’ Academic Performance By Group Formation section (section 5) deals with the interpretation of the experimental results, followed by scope for future work. Finally a conclusion is presented which gives a very brief summary of the overall research work. 2. BACKGROUND WORK Lalita Agashe in her paper [7] “Sustainable Development and Cooperative Learning in the Formal Education System in India” at International Conference on Education for a Sustainable Future in Ahmedabad, India, in January 2005, presented an account of the action research where group learning was found effective in teaching science in a school. Also she put forth the case of intentional and systematic inclusion of group learning strategies in the formal education system for the better implementation of Education for Sustainable Development in India. László, Illyés in their research paper titled [8] “Balanced Student Groups Forming for Univeristy Projects using Genetic Algorithm”, discussed two ways to generate student groups. First using genetic algorithms and second using the game theory in which he used professors as the authority and students as players to formulate their options. In the research paper titled [3], “A Framework for Semantic Group Formation in Education”, the authors Asma Ounnas, Hugh C Davis and David E Millard proposed a framework for student group formation based upon satisfying the constraints of the person forming the groups by reasoning over semantic data about the potential participants. Also they concluded that the use of semantic web technologies and logic programming increased the satisfaction of the constraints. Authors, Agustin-Blas, L.E.; Salcedo-Sanz, S.; Ortiz-Garcia, E.; Perez-Bellido, A.; PortillaFigueras, A., in their research paper titled [2], “Assignment of Students to Preferred Laboratory Groups Using a Hybrid Grouping Genetic Algorithm”, tried to show the application of grouping genetic algorithm in solving the problem of assigning students to laboratory groups. Their problem included a capacity as a constraint. Two cases were considered where students preferences were asked to form the groups and lecturers‟ preferences were also asked for forming the students‟ groups. Finally the performance of the approach was shown in many test instances of the problem and the results of the same were compared with the results of an existing heuristic algorithm. In the research paper titled [5], “An algorithm to form balanced and diverse groups of students”,

authors Hak Koon Yeoh, Mohamad Iskandr Mohamad Nor presented an algorithm which helps in grouping the students based on diversity in gender and race in addition to keeping the group average CGPA almost equal. The algorithm gave multiplefeasible solutions with a maximum absolute deviation in group average CGPA of 0.03/4.00 or even lower. Christodoulopoulos, C.E.; Papanikolaou, K.A. of University of Edinburgh, in their research paper titled [4], “A Group Formation Tool in an E-Learning Context”, presented a web-based tool for group formation that supported the instructor to automatically create both homogeneous and heterogeneous groups based on three criteria (factors). Their tool used fuzzy c-means algorithm for creating homogenous grouping that provided for each student the probability of belonging to different groups. Finally their research results indicated a good efficiency of their proposed approach in forming homogenous and heterogeneous groups. 3. GENETIC ALGORITHMS AND ITS APPLICATIONS IN STUDENTS’ GROUP FORMATION Genetic algorithms are search algorithms and optimization method which tries to copy the evolution process of nature. Many real-world problems have been solved by genetic algorithms. In the initial stage, genetic algorithm starts with a set of random solutions (called chromosomes). Then the fitness value (using fitness function) of each chromosome is calculated which shows the goodness of fit of each chromosome. In the next stage, some of the fitter initial chromosomes are chosen to become parent chromosomes for the crossover operation. The choosing of initial chromosomes to become parent chromosomes is based on the Charles Darwin‟s theory of evolution i.e. Survival of the fittest. Only chromosomes (solutions) with high fitness value compared to fitness value of other initial chromosomes are selected to become parent chromosomes (which are given the chance to reproduce the next generation of better chromosomes). In the next stage, genetic operators called crossover and mutation are used to create (reproduce) the next set of better chromosomes. Again better chromosomes (fitter solutions) are selected from the second generation of chromosomes and are allowed to reproduce better chromosomes. This cycle continues till some level of better solutions are achieved or some fixed number of iterations (cycle) have passed. Some difficulties faced by people while applying genetic algorithms to solve


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N. Devasenathipathi, N K. Modi | Applying Genetic Algorithms To Improve Students’ Academic Performance By Group Formation problems are the way chromosomes are encoded, the way fitness function is defined, choosing of appropriate crossover and mutation operators, the way parent chromosomes are chosen from the initial population of chromosomes. We now describe the seven steps in any Standard Genetic Algorithm (According to Eric Krevice Prebys, “The Genetic Algorithm in Computer Science”, MIT Undergraduate Journal of Mathematics, 2007): 1. Start with a population of n random individuals each with l-bit chromosomes. 2. Calculate the fitness f(x) of each individual. 3. Choose, based on fitness, two individuals and call them parents. Remove the parents from the population. 4. Use a random process to determine whether to perform crossover. If so, refer to the output of the crossover as the children. If not, simply refer to the parents as the children. 5. Mutate the children with probability pm of mutation for each bit. 6. Put the two children into an empty set called the new generation. 7. Return to Step 2 until the new generation contains n individuals. Delete one child at random if n is odd. Then replace the old population with the new generation. Return to Step 1. We consider the case of solving the travelling salesman problem using genetic algorithm. First, randomly some valid paths (say 1000 valid paths based on the number of cities) are formed. Then the fitness (in terms of cost or distance) is evaluated for every path. The lesser the cost/distance, the fitter is the path (chromosome). The paths become chromosomes and cities form the genes. Fitter paths among the initial valid paths are selected to generate the next set of more fit paths (chromosomes). These selected paths are made to undergo an operator called as crossover operator and another operator called mutation operator to reproduce the next set of valid fitter paths. Crossover operator is used among two chromosomes (in our case two paths). But traditional crossover techniques like single point crossover, two point crossover cannot be applied here. So crossover techniques like PMX (Partially Mapped Crossover), OX (Order Crossover), CX (Cycle Crossover), ERX (Edge Recombination Crossover) are used in this case. Similarly, traditional mutation techniques cannot be applied to chromosomes trying to solve Travelling Salesman Problem. So mutation techniques like displacement mutation, inversion

mutation, exchange mutation, insertion mutation are used here. As in the case of solving Travelling Salesman Problem where the order of cities is important to obtain the best solution, order of students is important for the formation of student groups. Every chromosome is a list of students with their current scores and calculated score (if the student was in that group), where every student becomes a gene. Figure 1 depicts the chromosome of Travelling Salesman Problem for 6 given cities and chromosome of student group formation for 56 students. The technique to calculate the calculated score of any student, using his/her previous score is discussed in the implementation section.

4.

IMPLEMENTATION OF THE RESEARCH WORK

It was earlier proved that a student‟s academic result, working in a group environment depended upon two factors. First factor is the grasping level of the student and second factor is the contribution level of his group members. Using these two factors, a formula was formulated to calculate the score of any given student, with respect to the academic scores (contribution level) of other students in that particular group and his/her grasping level. The sum of the scores of all the five students in a group were calculated. The student whose future score to be calculated is subtracted from this sum. Then the sum was divided by 4 (since it was knowledge of 4 students). Then this value is multiplied by the previous score (grasping level) of the given student. The percentage of one fifth (since the group consists of five students) of this score is finally then added with the previous score of the given student.


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N. Devasenathipathi, N K. Modi | Applying Genetic Algorithms To Improve Students’ Academic Performance By Group Formation The formula (devised to be implemented in C language) to calculate the future score of a student is given below: ((((∑xi – xi)/4) * xi)/100)/5 + xi It was a class of 56 students of post graduate course in computer applications and according to the formula every group should consist of 5 students. 11 groups of students were formed in which there were 10 groups with 5 students in each group and one group with six students. The summation of the future marks (obtained by the formula) of all students was kept as the fitness value. High summation value meant more fit. In our case, a chromosome would mean an arrangement of 56 students (like that of Travelling Salesman Problem). A „C - language‟ program was coded to compute the future marks of students (in accordance to the score of other group members) and find its summation. The initial population consisted of 1000 chromosomes. Based on the above fitness function, 500 fitter chromosomes were selected. According to genetic algorithms theory, these 500 fitter chromosomes were cloned and sent for crossover. We implemented (by writing a C program) Partially Mapped Crossover (PMX) for generating the next generation of chromosomes (student groups) of fitter chromosomes. After this, displacement mutation of those crossover chromosomes was implemented by writing a c program. The process was iterated till a good level of success was achieved. We coded functions in C - language for the following activities 1. Population initialization (Create random valid 1000 chromosomes) 2. Calculation of fitness value 3. Choosing the top 500 fitter chromosomes 4. Perform Crossover (Partially Mapped Crossover) 5. Perform Mutation (Displacement Mutation) 5.

RESULTS AND ITS INTERPRETATION

We had the following data for every student after the result of the semester examination (and after the experiment). Name Calculated Actual of the semester semester student score score (according to formula) 1. 25 students out of 56 students matched the predicted score with their actual semester score (with an error level of 5 percentage).

Difference in No. Of predicted score and students actual score (in percentage) One 3 Two 3 Three 5 Four 3 Five 11 2. Linear Correlation between the Predicted score and final score was 0.637119. 3. 47 students scored better than their previous semester scores. The above results show that students did better in group environment. Also the students groups generated by our genetic algorithm was balanced with good score students and relatively low score students. More number of students (of other classes) were also now willing to be formed into groups which they felt was more productive than individual study. Although all the students scores were calculated (predicted) with the help of a formula which relied only on the previous scores of the students, focus into other parameters were considered as part of future work. 6.

CONCLUSION

We exhibited an innovative way of using genetic algorithms for improving the academic performance of students by forming optimal groups. The algorithm produced worthy outcomes for different chromosome sizes. But the execution time rose higher as the number of students increased. Also in order to obtain better quality solutions a compromise has to be made between the solution aspect and the execution time. REFERENCES [1] Adewole Philip, Akinwale Adio Taofiki, Otunbanowo Kehinde, “A Genetic Algorithm for Solving Travelling Salesman Problem”, International Journal of Advanced Computer Sciences and Applications, Volume 2, No. 1, January 2011. [2] Agustin-Blas, L.E.; Salcedo-Sanz, S.; OrtizGarcia, E.; Perez-Bellido, A.; Portilla-Figueras, A.; Dept. of Signal Theor. & Commun., Univ. de Alcala, Madrid, “Assignment of Students to Preferred Laboratory Groups Using a Hybrid Grouping Genetic Algorithm”, Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference. [3] Asma Ounnas, Hugh C Davis, David E Millard, “A Framework for Semantic Group Formation in


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Education”, Educational Technology and Society, 12 (4), 43-55. Christodoulopoulos, C.E.; Papanikolaou, K.A., “A Group Formation Tool in an E-Learning Context”, Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference. Hak Koon Yeoh, Mohamad Iskandr Mohamad Nor, “An algorithm to form balanced and diverse groups of students”, 2009 Wiley Periodicals, Inc. Comput Application Engineering Education 19: 582–590, 2011. Kusum Deep, Hadush Mebrahtu, “Combined Mutation Operators of Genetic Algorithm for the Travelling Salesman problem”, International Journal of Combinatorial Optimization Problems and Informatics, Vol. 2, No.3, Sep. - Dec. 2011, pp. 1-23. Lalita Agashe, "Sustainable Development and Cooperative Learning in the Formal Education System in India", International Conference on

Education for a Sustainable Future in Ahmedabad, India, in January 2005. [8] László, Illyés, “Balanced Student Groups Forming for University Projects Using Genetic Algorithm”, In Proceedings of the 8th International Conference on Informatics in Economy, pages 554-559, Academy of Economic Studies, Bucharest, 2007. [9] Swati Mehrotra, Ritesh Khunyakari, Sugra Chunawala and Chitra Natarajan, “Evidences of learning through collaboration in Design and Technology Tasks in Indian Classrooms". [10] S.E. Kruck, “Assessing Individual Student Performance in Collaborative Projects: A Case Study”, Information Technology, Learning and Performance Journal, Volume 19, No. 2, Fall 2001. [11] Zhamri Che Ani, Azman Yasin, Mohd Zabidin Husin, Zauridah Abdul Hamid, “A Method for Group Formation using Genetic Algorithm”, International Journal of Computer Science and Engineering, Volume 02, No. 09, 2010, 3060 – 3064.


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