BİDEMAT - ZEKİ ÖĞRETİM SİSTEMİ

Bu çalışmada pedagojik alan bilgisi, bilişim teknolojileri ve yapay zekâ teknolojilerini bir araya getiren BİDEMAT adında zeki bir öğretim sistemi geliştirilmiştir. Bu sistem öğrencilere zaman ve mekân yani sınıf ortamından bağımsız uzaktan eğitimin mümkün olduğu etkin bir bireysel öğrenme ortamı sunar. Öğretmenlere ise öğrencilerine her zaman ulaşabilecekleri, onların öğrenme performanslarını takip edebilecekleri, yeni öğrenilecek konularla ilgili materyalleri ve ölçme değerlendirme araçlarını ekleyebilecekleri dinamik uyarlanabilir bir ortam sunar. BİDEMAT ZÖS sistemi öğrenciyi öğretim süreci boyunca adım adım izleyip değerlendirirken ve akıllı yönlendirme bileşeni sayesinde yönlendirirken ayırabilmektedir. Çünkü ZÖS öğretmenin belirlediği kurallara göre öğrencileri bireysel olarak yönlendirebilmekte ve öğretmen gerekli gördüğü durumlarda öğrencisine sistem üzerinden ayrıca müdahale edebilmektedir. Bu sayede öğretmen, hem öğretim ve ölçme değerlendirme sürecinde daha aktif rol almakta hem de önemli derecede zaman kazanmaktadır. Böylece öğreticiler rutin öğretim sürecinden çıkarak daha faklı öğretim materyalleri ve içerikler geliştirmek üzerine yoğunlaşabilir yeni kaynaklar oluşturarak öğrenmeyi daha zengin, çekici ve eğlenceli hale getirebilirler. Dünyada ve ülkemizde giderek önemi artan uzaktan eğitim sistemleri dikkate alındığında BİDEMAT ZÖS sistemi, FATİH projesi kapsamında bir türlü tam olarak uygulamaya koyulamayan ders yazılımları için önemli bir örnek oluşturmaktadır

BIDEMAT – INTELLIGENT TUTORING SYSTEM

In this work, an Intelligent Tutoring System (ITS) called BIDEMAT, which brings together pedagogical field knowledge, information technology, and artificial intelligence technologies have been developed. This system provides students with an effective individual learning environment in which distance learning is possible regardless of time and space, in other words, in the classroom environment. It also provides teachers dynamic environment in which they can always get to their students, follow their learning performance, add materials about new topics and measurement assessment tools. While The BIDEMAT ITS system follows and assesses students step by step throughout the teaching process and guides it through intelligence guidance, teachers will be able to allocate more time to their students. Because ITS can direct students individually according to the rules set by the teacher and the teacher can also interfere his / her students through the system. Thanks to this system, the teacher will be more active in the teaching and assessment process and will gain considerable time. Thus, the tutors can concentrate on developing more teaching materials and content by being out of the routine teaching process, and they can make learning richer, more attractive and more fun by creating new resources. Considering the increasing importance of distance education systems in the world and our country, the BIDEMAT ITS system constitutes an important example for course software which cannot be fully implemented within the scope of FATIH project.It is very important to consider learner characteristics and needs while designing learning environments with a learner-centered teaching approach. Because individual differences of learners influence their learning styles, learning histories, learning speeds, expectations, and information. However, the fact that the content and teaching programs offered to learners are the same causes the learners to become squeezed and become disconnected from the learning process. The best way to overcome this problem is to include the Adaptive Learning Environments (ALEs) which make it possible to design flexible learning environments and support different learning styles by taking individual needs into in the teaching process. ALEs provide individualized learning opportunities that enable users to experience an effective learning experience thanks to the use of new web technologies and artificial intelligence algorithms. These environments manage the learning process by providing to learners the right content that they need with the right strategy in time and by following theirs learning improvement and giving to them the necessary recommendations. These systems are named as Intelligent Tutoring Systems. The number of studies carried out in the ITS field in our country is limited. One of these is the application of agent-based learning system developed by Ozdemir (2000) in the Middle East University. Another study in this field is the development of intelligent tutoring system component with Prolog (Dağ 2003). In an another a study, the system called ZOSMAT, which emphasizes cognitive activities, is designed to be a virtual classroom environment as well as to meet the needs of real classrooms (Keles et al., 2007). It is a student-centered system. Improvement in the learning process depends on the student’s own effort. All the facilities of the technology were utilized in order to make learning easier and permanent. In this work, an intelligent teaching system called BIDEMAT, which brings together pedagogical field knowledge, information technology, and artificial intelligence technologies have been developed. This system provides students with an effective individual learning environment in which distance learning is possible regardless of time and space, in other words, in the classroom environment. It also provides teachers dynamic environment in which they can always get to their students, follow their learning performance, add materials about new topics and measurement assessment tools. While The BIDEMAT ITS system follows and assesses students step by step throughout the teaching process and guides it through intelligence guidance, teachers will be able to allocate more time to their students. Because ITS can direct students individually according to the rules set by the teacher and the teacher can also interfere his / her students through the system. In this system, the teacher is only a guide. He/she follows learning situations of students on the system and sends a message via e-mail or system to guide the student. The content of the BIDEMAT sytem can be updated dynamically for each course and topic. In this study, the issues of the “Factors and Multiples” were used as a content of learning. BIDEMAT system was developed with BAP 2012/EĞİTİM-02 project support at Agri Ibrahim Cecen Universty. The system consists of four component. These components are a tutorial module, Knowledge module, student model and evaluation and recommendation module. The tutorial module of the system is the manage panel.Thanks to this module, the tutor can check his/her students’ the learning performances and exam results, lead them on the system, communicate with them via chat and ban their some activities on the system. The information module provides content management of the system. Instructional content and measurement evaluation questions related to the subject that the teacher wants to teach can be entered into the system through this module. These may include videos, analytical examples and animations, additional questions to the pool of questions, and flash games that will create motivational rewards. This module provides a dynamic structure to the BIDEMAT system by keeping the contents and measurement evaluation questions up to date with the file management feature. This module allows the Tutor to view all content and questions related to the lesson or topic and plan the teaching process. The student module is the module where the students are presented with subject-oriented videos, question solutions and tests that measure learning performance. This module provides students with instructional content taking into consideration the concept map and individual learning performance. It also allows students to keep individual notes during the learning process so that the student has the opportunity to go over it again using his/her own notes at the end of the study. The assessment and advice module presents it to the students by analyzing the learner's progress in the learning process and his performance in the assessment process. Thus, the students have detailed knowledge about individual learning performance and are guided by the system to re-learn the subjects and concepts that they lack. The system rewards the students that get 70% and over learning performance with games to increase their motivation at subject-based examinations. In order to learn about the performance and usage of the BIDEMAT system developed in this study, the application was made with a group of students of Alpaslan Middle School in Agri city studying in the fall semester of 2016-2017 who had never learned the subject “Factors and Multiples” before. A group of 40 students selected as an experimental group participated in the application of 4-hour BIDEMAT Intelligent Teaching System at the Computer and Teaching Technology Laboratories at Agri Ibrahim Cecen University. The students who constitute the experiment group learned the topic of Mathematics- 1st unit chapter 2- "Factors and Multiples" with BIDEMAT ITS system and the control group learned it in the classroom environment at school. At the end of the training period, in order to determine whether the students had positive effects on the learning outcomes of the BIDEMAT ITS system, a test consisting of 20 questions prepared jointly was applied to both groups. The average of the group of learners with the traditional method (experimental) was Xort = 58,250, and the average of the group who learned with the BIDEMAT intelligent Teaching System (control) was found as Xort = 79,13. It has been found out that the average learning performance of students learning with BIDEMAT is 21% higher than that of the experimental group. To determine whether the difference between the two groups was meaningful or not independent group t-test was applied and analyzed. According to the findings obtained from the analysis results (t = -6,256 and p

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  • Arıcı N., Karacı A., (2013). Türkçe Öğrenimi için Web Tabanlı Zeki Öğretim Sistemi (Türkzös) ve Değerlendirmesi. Turkish Studies - International Periodical For The Languages, Literature And History Of Turkish Or Turkic Volume 8/8 Summer 2013, www.turkishstudies.net, p. 65- 87, DOI Number: http://dx.doi.org/10.7827/TurkishStudies.4878, Ankara-Turkey.
  • Brown, J.S. and Burton R.R., (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–192.
  • Brusilovsky, P., (1998). Methods and techniques of adaptive hypermedia. In P. Brusilovsky, A. Kobsa, & J. Vassileva (Eds.), Adaptive hypertext and hypermedia, Dordrecht: Kluwer Academic Publishers,1–43.
  • Büğrü, E.Ö. (2003). Web-Tabanlı Akıllı Eğitimde Uyarlanır İçerik Sunumu Sisteminin Bayes Ağı Yaklaşımı İle Tasarımı ve Gerçekleştirilmesi. Yüksek Lisans Tezi, Hacettepe Üniversitesi.
  • Carbonell, J.R., (1970). AI in CAI: An artificial intelligence approach to computer-aided instruction. IEEE Transactions on Man–Machine System, MMS -11 (4), 190–202.
  • Clancey, W.J., (1982). Tutoring rules for guiding a case method dialogue. In D.H. Sleeman et & J.S. Brown (Eds.), Intelligent tutoring systems, 201–225, London: Academic Press.
  • Collins, A.M., (1976). Processing in acquiring knowledge. In R.C. Anderson (Ed.), Schooling and the acquisition of knowledge. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Conati, C., and Maclare H., (2004). Evaluating a probabilistic model of student effect. In Proceedings of the seventh international conference ITS 2004, Maceio, Brasil, 55–66.
  • Dağ F., Erkan, K. (2004). Prolog Tabanlı Zeki Öğretim Sistemi. Denizli: Mühendislik Bilimleri Dergisi, II. Bilgi Teknolojileri Kongresi. 47-55.
  • Dağ F., (2003). Zeki Öğretim Sistemi Bileşenlerinin Prolog ile Gerçekleştirilmesi, Yüksek Lisans Tezi,Kocaeli Üniversitesi, Fen Bilimleri Enstitüsü, Kocaeli.
  • Davidovic, A., Warren J., and Trichina E., (2003). Learning benefits of structural example based adaptive tutoring systems. IEEE Transactions on Education, 46(2), 241–251.
  • Dillenbourg, P., and Mendelsohn P., (1991). Le Developpement de l’enseignement Intelligemment Assiste par Ordinateur. Natural and Artificial Intelligence Symposium. Rome, September, 23– 25.
  • Erol H.F., Gülcü İ., (2016). Yabancı Dil Olarak Türkçenin Bilgisayar Destekli Öğretimi İle İlgili Yabancı Öğrenci Görüşleri, Turkish Studies - International Periodical for the Languages, Literature and History of Turkish or Turkic Volume 11/3 Winter 2016, p.1115-1130, ISSN: 1308-2140, www.turkishstudies.net, DOI Number: http://dx.doi.org/10.7827/TurkishStudies.929, ANKARA-TURKEY
  • Keleş A, Ocak R., Keleş A, Gülcü A., (2009). ZOSMAT: Web-based intelligent tutoring system for teaching-learning process, Expert Systems with Applications, 36,1229-1239 pp., Mart 2009, DOI: 10.1016/j.eswa.2007.11.064
  • Kelly, D., and Tangney B., (2004). Predicting learning characteristics in a multiple intelligence beased tutoring system. In Proceedings of the seventh international conference ITS 2004, Maceio, Brasil, 678–688.
  • Kim, J., Lee, A. & Ryu, H. (2013). Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model. International Journal of Industrial Ergonomics 43, 450-461.
  • Önder, H. H. (2003). Uzaktan Eğitimde Bilgisayar Kullanımı ve Uzman Sistemler. The Turkish Online Journal of Educational Techonology. 2 (3): 142-146.
  • Özdemir B., (2000). Development of an Intelligent Agent for Distance Learning, Computer Engineering Department of ODTU (MS Degree), Ankara, Türkiye.
  • Shute, V.J., Postka J., (1996). ITS: Past, Present and Future. 19th Chapter of Handbook of Research Educational Communications and Technology, 1270 p, AECT Publication, Bloomington.
  • Suebnukarn, S., and Haddawy P., (2004). A collaborative intelligent tutoring system for medical problem based learning. In IUI_04, Madeira, Funchal, Portugal.
  • Tamer, T. (2002). Yapay Zeka Programlama Tekniklerinin Bilgisayar Destekli Eğitimde Kullanımına İlişkin Bir Model. Yükseklisans Tezi. Ankara: Gazi Üniversitesi Fen Bilimleri Enstitüsü. 28-102.
  • Vandewaetere, M., Desmet, P., & Clarebout, G. (2011). The contribution of learner characteristics in the development of computer-based adaptive learning environments. Computers in Human Behavior, 27(1), 118-130.