Effects of Intelligent Tutoring Systems (ITS) on Personalized Learning (PL) (original) (raw)
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Comparative Analysis of Intelligent Tutoring System Approaches
Journal 4 Research - J4R Journal, 2018
With the huge advancement in technology there is a need of advancement in education. Lots of work have been done in education system to enhance the learning ability of students, Intelligent Tutoring System (ITS) is one of the outcome of those advancements. It provides a platform which reduce the dependency on human tutor as it focuses on providing smart (intelligent) system to students for learning. ITS is a system which is implemented on computers that uses Artificial Intelligence(AI) techniques for advanced learning experience where human tutor is replaced by a virtual tutor. It presents education and other study related information in a flexible and personalized way. An ITS mainly consists of three models Domain Model, Student Model, Tutor Model.
Intelligent tutoring systems and learning performance
Online Information Review, 2019
PurposeIntelligent tutoring systems (ITS) are a supplemental educational tool that offers great benefits to students and teachers. The systems are designed to focus on an individual’s characteristics, needs and preferences in an effort to improve student outcomes. Despite the potential benefits of such systems, little work has been done to investigate the impact of ITS on users. To provide a more nuanced understanding of the effectiveness of ITS, the purpose of this paper is to explore the role of several ITS parameters (i.e. knowledge, system, service quality and task–technology fit (TTF)) in motivating, satisfying and helping students to improve their learning performance.Design/methodology/approachData were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data.FindingsData analysis revealed that the quality of knowledge, system and service directly impacted satisfaction and improved TTF for ITS. It was found that TTF and student ...
Utilizing Educational Data Mining Techniques for Improved Learning, 2020
With digitization, a rapid growth is seen in educational technology. Different formal and informal learning contents are available on the internet. Intelligent tutoring system provides personalized e-learning to the learners. Different attributes like historical data, real-time data, behavioral, and cognitive are usually used for personalization. Based on the personalization, the intelligent tutoring system aims to provide easy and effective understanding. Recent research highlights the effect of learner's behavior and emotions on effective teaching-learning process. This chapter provides a brief description of the intelligent tutoring system, current developments, instructional techniques, proposed solution, and future recommendations. The emphasis of the study is to provide insights on self-regulated learning.
2018
With the rapid growth of technology, computer learning has become increasingly integrated with artificial intelligence techniques in order to develop more personalized educational systems. These systems are known as Intelligent Tutoring systems (ITSs). This paper focused on the variant characteristics of ITSs developed across different educational fields. The original studies from 2007 to 2017 were extracted from the PubMed, ProQuest, Scopus, Google scholar, Embase, Cochrane, and Web of Science databases. Finally, 53 papers were included in the study based on inclusion criteria. The educational fields in the ITSs were mainly computer sciences (37.73%). Action-condition rule-based reasoning, data mining, and Bayesian network with 33.96%, 22.64%, and 20.75% frequency respectively, were the most frequent artificial intelligent techniques applied in the ITSs. These techniques enable ITSs to deliver adaptive guidance and instruction, evaluate learners, define and update the learner's model, and classify or cluster learners. Specifically, the performance of the system, learner's performance, and experiences were used for evaluation of ITSs. Most ITSs were designed for web user interfaces. Although these systems could facilitate reasoning in the learning process, these systems have rarely been applied in experimental courses including problem-solving, decision-making in physics, chemistry, and clinical fields. Due to the important role of a cell phone in facilitating personalized learning and given the low rate of using mobile-based ITSs, this study has recommended the development and evaluation of mobile-based ITSs. ARTICLE HISTORY
Design Perspectives of Intelligent Tutoring System
Intelligent Tutoring Systems (ITSs) have come a long way, since their inception decades ago. Its prospects have revolutionized e-Learning, curriculum instructions and workplace training. The field has witnessed significant developments towards many possible directions and as a result, numerous ITSs have been developed to date. Recent tutoring systems have moved from research labs to classrooms [1]. However, it is still a costly affair and lacks established standards. Human learning phenomena are very complex and itself is an ongoing research activity right through the history of mankind. This paper attempts to identify some key instructional/learning aspects that must be addressed while designing a successful tutoring system. In this regard, we have reviewed some of the well-known ITS design principles and report an analysis of their success in modelling the learning/instructional ingredients.
A Comparative Literature Review of Intelligent Tutoring Systems from 1992-2015
2017
A Comparative Literature Review of Intelligent Tutoring Systems from 1990-2015 Brice Robert Colby Department of Instructional Psychology and Technology, BYU Master of Science This paper sought to accomplish three goals. First, it provided a systematic, comparative review of several intelligent tutoring systems (ITS). Second, it summarized problems and solutions presented and solved by developers of ITS by consolidating the knowledge of the field into a single review. Third, it provided a unified language from which ITS can be reviewed and understood in the same context. The findings of this review centered on the 5-Component Framework. The first component, the domain model, showed that most ITS are focused on science, technology, and mathematics. Within these fields, ITS generally have mastery learning as the desired level of understanding. The second component, the tutor model, showed that constructivism is the theoretical strategy that informs most ITS. The tutoring tactics employ...
A review of intelligent tutoring systems in e-learning
—An ITS (Intelligent Tutoring System) is a complex, integrated software system that applies the principles and methods of artificial intelligence (AI) to the problems and needs of teaching and learning. They allow searching the student level of knowledge and learning strategies used to increase or correct the students' knowledge. They are intended to support and improve the teaching and learning process in a selected area of knowledge while respecting the individuality of the learner. In the paper a review of intelligent tutoring systems (ITS) is given from the aspect of their application and usability in modern learning concepts. How to cite this article: Dašić, P.; Dašić, J.; Crvenković, B. & Šerifi, V.: A review of intelligent tutoring systems in e-learning. Annals of the Oradea University – Fascicle of Management and Technological Engineering, Vol. 15 (XXV), No. 3 (December 2016), pp. 85-90. ISSN 1583-0691. doi: 10.15660/AUOFMTE.2016-3.3276.
Intelligent tutoring system to improve learning outcomes
AI Communications, 2019
Nowadays, society is in constant evolution, which allows constant production of new knowledge. In this way, citizens are constantly pressured to obtain new qualifications through training/requalification. The need for qualified people has been growing exponentially, which means that resources for education/training are limited to being used more efficiently. In this paper we will focus in the design the user model, so, we propose an innovative approach to design a user model that monitors the user's biometric behaviour by measuring their level of attention during e-learning activities. In addition, a machine learning categorization model is presented that oversees user activity during the session. We intend to use non-invasive methods of intelligent tutoring systems, observing the interaction of users during the session. Furthermore, this article highlights the main biometric behavioural variations for each activity and bases the set of attributes relevant to the development of machine learning classifiers to predict users' learning preference. The results show that there are still mechanisms that can be explored and improved to better understand the complex relationship between human behaviour, attention and evaluation that could be used to implement better learning strategies. These results can be decisive in improving ITS in e-learning environments and to predict user behaviour based on their interaction with technology devices.
With the rapid growth of technology, computer learning has become increasingly integrated with artificial intelligence techniques in order to develop more personalized educational systems. These systems are known as Intelligent Tutoring systems (ITSs). This paper focused on the variant characteristics of ITSs developed across different educational fields. The original studies from 2007 to 2017 were extracted from the PubMed, ProQuest, Scopus, Google scholar, Embase, Cochrane, and Web of Science databases. Finally, 53 papers were included in the study based on inclusion criteria. The educational fields in the ITSs were mainly computer sciences (37.73%). Action-condition rule-based reasoning, data mining, and Bayesian network with 33.96%, 22.64%, and 20.75% frequency respectively, were the most frequent artificial intelligent techniques applied in the ITSs. These techniques enable ITSs to deliver adaptive guidance and instruction, evaluate learners, define and update the learner's model, and classify or cluster learners. Specifically, the performance of the system, learner's performance, and experiences were used for evaluation of ITSs. Most ITSs were designed for web user interfaces. Although these systems could facilitate reasoning in the learning process, these systems have rarely been applied in experimental courses including problem-solving, decisionmaking in physics, chemistry, and clinical fields. Due to the important role of a cell phone in facilitating personalized learning and given the low rate of using mobile-based ITSs, this study has recommended the development and evaluation of mobile-based ITSs.