Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods

Mousavinasab, Elham; Zarifsanaiey, Nahid; Kalhori, Sharareh R. Niakan; Rakhshan, Mahnaz; Keikha, Leila; Saeedi, Marjan Ghazi

doi:10.1080/10494820.2018.1558257

Cited by 256 publications

(118 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Systematic reviews of ITSs [21], [22], [23] have shown that ITS research has successfully provided techniques and systems to help students acquire specific cognitive and metacognitive knowledge in different areas (e.g., medicine, mathematics, science, database design). ITS research has also proposed different strategies for tutoring, such as the identification and correction of errors [24] and the construction of self-explanations [25], [26].…”

Section: A Intelligent Tutoring Systemsmentioning

confidence: 99%

A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

Paladines¹,

Ramírez²

2020

IEEE Access

View full text Add to dashboard Cite

Intelligent tutoring systems (ITSs) are computer programs that provide instruction adapted to the needs of individual students. Dialog systems are computer programs that communicate with human users by using natural language. This paper presents a systematic literature review to address ITSs that incorporate dialog systems and have been implemented in the last twenty years. The review found 33 ITSs and focused on answering the following five research questions. a) What ITSs with natural language dialogue have been developed? b) What is the main purpose of the tutoring dialogue in each system? c) What are the pedagogical features of the teaching process performed by the ITSs with natural language dialogue? d) What natural language understanding approach does each system employ to understand students' utterances? e) What evidence exists related to the evaluation of ITSs with natural language dialogue? The results of this review reveal that most ITSs are directed toward science, technology, engineering, and mathematics (STEM) domains at the university level, and the majority of the selected ITSs implement the expectations and misconceptions tailored approach. Furthermore, most ITSs use dialog to help students learn how to solve a problem by applying rules, laws, etc. (the apply level in Bloom's taxonomy). With regard to the instructional approach, the selected ITSs help students write correct explanations or answers for deep questions; assist students in problem solving; or support a reflective dialogue motivated by either previously provided content or the result of a simulation. Additionally, we found empirical evaluations for 90.91% of the selected ITSs that measure the learning gains and/or assess the impacts of different tutoring strategies.

show abstract

Section: A Intelligent Tutoring Systemsmentioning

confidence: 99%

A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

Paladines¹,

Ramírez²

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This approach does not seek to model the mechanisms of human learning but aims to identify, based on the actions of the learner, the new knowledge to bring to it. Bayesian networks are one of the techniques that allow this type of tutoring to be carried out (Desmarais & Baker, 2012), (Ramírez-NoriegaA & Al., 2017) and (Mousavinasab & Al., 2018).…”

Section: Its Architecturementioning

confidence: 99%

Collaborative Tutoring: A Multi-Tutor Approach

Ennaji

Boukachour

Machkour

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. An Intelligent Tutorial System (ITS) is a learning computer environment. Many ITSs do not integrate human tutor since they are designed to use in autonomy by the learner. One of the reasons to increase the rate of desertion in a distance training framework compared to that of a face-to-face course is the absence of the human killer. Besides, the existing ITSs are dedicated to a single learning object based on domain-dependent modelling. Our contribution consists in proposing an ITS, independent of the learning domain, capable of initiating learning, of managing an articulation between machine tutoring and human tutoring (teaching and peers) to offer an individualized and personalized follow-up, and ensure certification of the learner’s assessment.

show abstract

“…As the levels of learners' precognition differ, feedback should be tailored to individuals, instead of providing exactly the same learning experiences for everyone (Nazemi, Breyer, Stab, Burkhardt, & Fellner, 2010). To achieve this, artificial intelligence techniques drawn from Intelligent Tutoring Systems (ITSs) (Mousavinasab et al, 2018) have sometimes been employed (e.g., Rummel et al, 2016). In addition, researchers have realised that it is important to shift from measuring the learning outcomes of, in the case of this study, fraction knowledge, to interpreting the learning process where learning occurs (Kong, 2008;Mavrikis, Holmes, Zhang, & Ma, 2018).…”

Section: The Strengths and Challenges Of Exploratory Learning Environmentioning

confidence: 99%

Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools

Zhang

Gao

Holmes

et al. 2019

Interactive Learning Environments

View full text Add to dashboard Cite

Feedback in intelligent tutoring systems has been depicted as an important contributor to encourage exploration. However, few studies have explored learners' interaction patterns associated with feedback and the use of external representations in exploratory learning environments. This study used Fractions Lab, an exploratory learning environment for mathematics, to facilitate children's conceptual understanding of fractions in three Chinese schools. Students (n = 189) from six different classes were invited to use Fractions Lab, and 260,000 event logs were collected. Beyond demonstrating the overall efficacy of the approach, lag sequential analysis supported us in approaching a deeper understanding of patterns of interaction. The findings highlight that the design of three-levels of feedback (Socratic, guidance, and didactic-procedural feedback) played different roles in supporting students to use external representations to perform mathematical tasks in an exploratory learning environment. This study sheds light on how these interaction patterns might be applied to the Fractions Lab system in order to provide increasingly tailored support, based on cultural differences, to enhance students' technology-mediated learning experiences.

show abstract

Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods

Cited by 256 publications

References 50 publications

A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

Collaborative Tutoring: A Multi-Tutor Approach

Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools

Contact Info

Product

Resources

About