scholarly journals Fuzzy Logic for Refining the Evaluation of Learners’ Performance in Online Engineering Education

2019 ◽  
Vol 4 (6) ◽  
pp. 50-56 ◽  
Author(s):  
Akrivi Krouska ◽  
Christos Troussas ◽  
Cleo Sgouropoulou

Intelligent tutoring systems have been widely used for optimizing the educational process by creating a student-centered learning environment. As a matter of fact, an integral part of intelligent tutoring systems is the evaluation of the learners’ performance. In traditional learning, the instructors calculate the grade of the students derived from the assessment units and other factors, such as the difficulty of the exercises or their effort, in order to produce the final students’ score in the course. However, in most cases, the evaluation of learners’ performance in intelligent tutoring systems takes place by calculating an average grade of students without taking into account the aforementioned factors. In view of the above, this paper presents a novel way for refining the evaluation of students’ performance using fuzzy logic. As a testbed for our research, we have designed and implemented an intelligent tutoring system holding social networking characteristic for teaching the engineering course of “Compilers”. More specifically, the system is responsible for acquiring information about students such as their grades, the kinds of misconceptions, the level of tests’ difficulty as well as their effort including their social interaction, i.e. participation in forums, making comments in posts and posting regarding the educational process. Taking these into consideration, fuzzy logic model diagnoses the accuracy of students’ grade and the system suggests that the instructor redefine students’ grade properly. Our system was evaluated using t-test and the results show high accuracy and objectivity in the evaluation of students’ performance.

2017 ◽  
Vol 26 (4) ◽  
pp. 717-727 ◽  
Author(s):  
Vladimír Bradáč ◽  
Kateřina Kostolányová

AbstractThe importance of intelligent tutoring systems has rapidly increased in past decades. There has been an exponential growth in the number of ends users that can be addressed as well as in technological development of the environments, which makes it more sophisticated and easily implementable. In the introduction, the paper offers a brief overview of intelligent tutoring systems. It then focuses on two types that have been designed for education of students in the tertiary sector. The systems use elements of adaptivity in order to accommodate as many users as possible. They serve both as a support of presence lessons and, primarily, as the main educational environment for students in the distance form of studies – e-learning. The systems are described from the point of view of their functionalities and typical features that show their differences. The authors conclude with an attempt to choose the best features of each system, which would lead to creation of an even more sophisticated intelligent tutoring system for e-learning.


Author(s):  
Rashmi Khazanchi ◽  
Pankaj Khazanchi

Current educational developments in theories and practices advocate a more personalized, student-centered approach to teach 21st-century skills. However, the existing pedagogical practices cannot provide optimal student engagement as they follow a ‘one size fits all' approach. How can we provide high-quality adaptive instructions at a personalized level? Intelligent tutoring systems with embedded artificial intelligence can assist both students and teachers in providing personalized support. This chapter highlights the role of artificial intelligence in the development of intelligent tutoring systems and how these are providing personalized instructions to students with and without disabilities. This chapter gives insight into the challenges and barriers posed by the integration of intelligent tutoring systems in K-12 classrooms.


2010 ◽  
Vol 6 (1) ◽  
pp. 46-70 ◽  
Author(s):  
Kiran Mishra ◽  
R.B. Mishra

Intelligent tutoring systems (ITS) aim at development of two main interconnected modules: pedagogical module and student module .The pedagogical module concerns with the design of a teaching strategy which combines the interest of the student, tutor’s capability and characteristics of subject. Very few effective models have been developed which combine the cognitive, psychological and behavioral components of tutor, student and the characteristics of a subject in ITS. We have developed a tutor-subject-student (TSS) paradigm for the selection of a tutor for a particular subject. A selection index of a tutor is calculated based upon his performance profile, preference, desire, intention, capability and trust. An aptitude of a student is determined based upon his answering to the seven types of subject topic categories such as Analytical, Reasoning, Descriptive, Analytical Reasoning, Analytical Descriptive, Reasoning Descriptive and Analytical Reasoning Descriptive. The selection of a tutor is performed for a particular type of topic in the subject on the basis of a student’s aptitude.


Author(s):  
Yong Se Kim ◽  
Hyun Jin Cha ◽  
Tae Bok Yoon ◽  
Jee-Hyoung Lee

Motivation is a paramount factor to student success. Although it is well known that the learner’s motivation and emotional state in educational contexts are very important, they have not been fully addressed in intelligent tutoring systems (ITS). In this paper, a method for integrated motivation diagnosis and motivational planning is described in a manner applied to an operable system. For the motivational diagnosis rules, three different channels of data (performance from interaction with the system, verbal communication, and feedbacks) are combined. For the motivational planning rules, four different strategies (different learning process, helps, different teaching strategies, and arousal questions or feedbacks) are combined. By applying the mechanisms, a tutoring system for the topic of perspective projection with motivation diagnosis and motivational planning on a multiagent system with fuzzy logic has been implemented.


Author(s):  
Igor Jugo ◽  
Božidar Kovačić ◽  
Vanja Slavuj

Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our systems architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.


1995 ◽  
Vol 10 (1) ◽  
pp. 52-62
Author(s):  
Marios C. Angelides ◽  
Amelia K.Y. Tong

Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutoring system can be developed to support multiple tutoring strategies during the course of interaction. SONATA has been implemented using a hypertext tool, HyperCard II. 1.


Author(s):  
Mingyu Feng ◽  
Neil Heffernan ◽  
Kenneth Koedinger

Student modeling and cognitively diagnostic assessment are important issues that need to be addressed for the development and successful application of intelligent tutoring systems (its). Its needs the construction of complex models to represent the skills that students are using and their knowledge states, and practitioners want cognitively diagnostic information at a finer grained level. This chapter reviews our effort on modeling student’s knowledge in the ASSISTment project. Intelligent tutors have been mainly used to teach students. In the ASSISTment project, we have emphasized using the intelligent tutoring system as an assessment system that provides instructional assistance during the test. Usually it is believed that assessment get harder if students are allowed to learn during the test, as its then like try to hit a moving target. So our results are surprising that by providing tutoring to students while they are assessed we actually prove the assessment of students’ knowledge. Additionally, in this article, we present encouraging results about a fine-grained skill model with that system that is able to predict state test scores. We conclude that using intelligent tutoring systems to do assessment seems like a reasonable way of dealing with the dilemma that every minute spent testing students takes time away from instruction.


Author(s):  
Pauline K. Cushman

Intelligent Tutoring Systems have been designed for a variety of purposes. Much of the design effort has been aimed at the actual subject matter. Often ignored has been the critical nature of the interface. If the way people interact with computers is directly related to their personality, then systems should respond differently to different people. This paper describes the design of an interface for an Intelligent Tutoring System that, given the student's personality, will make adjustments in the style of interaction.


2014 ◽  
Vol 6 (2) ◽  
pp. 138-146 ◽  
Author(s):  
Sintija Petrovica

Since 1970-ties the research is being carried out for the development of intelligent tutoring systems (ITS) that aretrying to imitate human-teachers and their teaching methods. However, over the last decade researchers inspired by the closerelationship between emotions and learning have been working on the addition of an emotional component to human-computerinteraction. This has led to creation of a new generation of intelligent tutoring systems – emotionally intelligent tutoring systems(EITS). Despite the research carried out so far, a problem how to adapt tutoring not only to a student’s knowledge state butalso to his/her emotional state has been disregarded. The paper presents study on how to use the determined student’s emotionalstate further in order to change behaviour of the intelligent tutoring system looking from the pedagogical point of view and toimplement this as a part of the pedagogical module. The architecture of the planned tutoring system that adapts the tutoring bothto student’s emotions and knowledge is also described in the paper. Straipsnyje nagrinėjami klausimai, susiję su informacijos apienustatytą studento emocinę būklę taikymu sumaniosios mokymosistemos elgsenai keisti, taip pat emocinės būklės poveikis mokymoprocesui pedagoginiu požiūriu. Siūlomas pedagoginiamsaspektams įgyvendinti specializuotas informacinės sistemosmodulis. Parodoma pedagoginio modulio vieta sumaniosiosmokymo sistemos, pritaikančios mokymo procesą konkretausstudento žinių ir emociniam lygmenims, architektūroje.


2011 ◽  
Vol 26 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Fred Phillips ◽  
Benny G. Johnson

ABSTRACT: Prior research demonstrates that students learn more from homework practice when using online homework or intelligent tutoring systems than a paper-and-pencil format. However, no accounting education research directly compares the learning effects of online homework systems with the learning effects of intelligent tutoring systems. This paper presents a quasi-experiment that compares the two systems and finds that students’ transaction analysis performance increased at a significantly faster rate when they used an intelligent tutoring system rather than an online homework system. Implications for accounting instructors and researchers are discussed.


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