The determination and analysis of factors affecting to student learning by artificial intelligence in higher education

At the present time, with the improvement of educational work in the education field life, and is intended to facilitate the organization. Many methods are used to accomplish this goal. Artificial intelligence is one of these methods. With artificial intelligence applications to problems encountered in the educational life solutions are the way. In this study, an analysis of the factors affecting student learning and the identification process is carried out by an artificial intelligence-based method. An optimization method for determining the factors affecting the learning process has been proposed. Because many factors that are effective for student learning. For this reason the optimization process must be performed of these factors. An optimization process using fuzzy logic and genetic algorithm is proposed to perform this operation. Then, the classification process of these factors is performed using K-means algorithm. Factors that affect the learning process in order to perform the analysis of the factors affecting student learning students, teachers, the curriculum and namely social life is divided into four main sections. Artificial intelligence method was used to perform the analysis of the impact of these factors on the learning process. Thus, the determination of the factors that affect student learning and analysis will be done in an easier way. Also easily cause remedy the problems which occur in the training using the results obtained from this method is produced. Thereby more accurately and quickly determine the problems to occur in the training satisfies faster and more effective solutions can be realized.

[1]  Mohd Tariq,et al.  Fuzzy logic control of buck converter for photo voltaic emulator , 2016, 2016 4th International Conference on the Development in the in Renewable Energy Technology (ICDRET).

[2]  R. Gnanadass,et al.  Optimization of process parameters through fuzzy logic and genetic algorithm - A case study in a process industry , 2015, Appl. Soft Comput..

[3]  Qishan Zhang,et al.  Grey Kmeans algorithm and its application to the analysis of regional competitive ability , 2014, 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference.

[4]  Varadraj P. Gurupur,et al.  Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic , 2014, IEEE Transactions on Learning Technologies.

[5]  Mohamed Akil,et al.  A new hybrid binarization method based on Kmeans , 2014, 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[6]  Mehmet Karakose,et al.  Reinforcement Learning Based Artificial Immune Classifier , 2013, TheScientificWorldJournal.

[7]  Rahat Iqbal,et al.  An intelligent framework for monitoring student performance using fuzzy rule-based Linguistic Summarisation , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[8]  N. Buniyamin,et al.  The impact of environment on engineering students' academic performance: A pilot study , 2011, 2011 3rd International Congress on Engineering Education (ICEED).

[9]  K. Vimala A study of Artificial Intelligence in behavioural education , 2011 .

[10]  Chin-pin Chen,et al.  The Influencing Factors of Student Teachers' Entrepreneurial Learning Behavior , 2011, 2011 Fourth International Joint Conference on Computational Sciences and Optimization.

[11]  Dimple Malik,et al.  Evolving limitations in K-means algorithm in data mining and their removal , 2011 .

[12]  Liu Xian,et al.  Artificial intelligence and modern sports education technology , 2010, 2010 International Conference on Artificial Intelligence and Education (ICAIE).

[13]  HongLiu,et al.  Web user clustering analysis based on KMeans algorithm , 2010, ICOIN 2010.

[14]  Nadire Cavus,et al.  The evaluation of Learning Management Systems using an artificial intelligence fuzzy logic algorithm , 2010, Adv. Eng. Softw..

[15]  JinHua Xu,et al.  Web user clustering analysis based on KMeans algorithm , 2010, 2010 International Conference on Information, Networking and Automation (ICINA).

[16]  Umi Kalthum Ngah,et al.  Adaptive fuzzy moving K-means clustering algorithm for image segmentation , 2009, IEEE Transactions on Consumer Electronics.

[17]  Mahdi Hassani Goodarzi,et al.  Evaluating Students' Learning Progress by Using Fuzzy Inference System , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[18]  Tengku Amina Munira,et al.  Applications of Artificial Intelligence in an Open and Distance Learning institution , 2008, 2008 International Symposium on Information Technology.

[19]  Surendra Kumar,et al.  Optimization of Fuzzy Logic Controller using Genetic Algorithms , 2006 .

[20]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .