scholarly journals Data-Driven Hint Generation in Intelligent Tutoring Systems for Learning Microeconomics

2019 ◽  
Vol 4 (9) ◽  
pp. 37-40
Author(s):  
Bui Trong Hieu ◽  
Bui Thi Kim Uyen
Keyword(s):  
Problem Solving ◽  
Intelligent Tutoring Systems ◽  
Scientific Inquiry ◽  
Student Behavior ◽  
Intelligent Tutoring ◽  
Data Driven ◽  
Tutoring Systems ◽  
Inquiry Skills ◽  
Scientific Inquiry Skills ◽  
Problem Solving Behavior

Scientific inquiry skills is used in all educational areas. In the context of microeconomics, it is widely known that the development of scientific inquiry skills is central to the construction of ideas that enable understanding. Intelligent tutoring systems (ITSs) have been shown to be effective in supporting students' domain-level learning through guided problem solving practice. ITSs provide personalized feedback (in the form of hints) to students and improve learning at effect sizes approaching that of human tutors. However, creating an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain’s curriculum. Creating an ITS requires time, resources, and multidisciplinary skills. Because of the large possible range of problem solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem solving domains. Data-driven ITSs have shown much promise in increasing effectiveness by analyzing past data in order to quickly generate hints to individual students. However, the fundamental long term goal was to develop “better, faster, and cheaper” ITSs. The main goal of this paper is to: 1) presents ITSs used in the microeconomics education; and 2) introduce data-driven ITSs for microeconomics education.  

scholarly journals Data-Driven Intelligent Tutoring Systems for STEM+C Learning and Teaching

2019 ◽  
Vol 4 (9) ◽  
pp. 202-206
Author(s):  
Hieu Trong Bui
Keyword(s):  
Problem Solving ◽  
Intelligent Tutoring Systems ◽  
Student Behavior ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Data Driven ◽  
Tutoring Systems ◽  
Learning And Teaching ◽  
Past Data ◽  
Problem Solving Behavior

It is wide known that one of the most effective ways to learn is through problem solving. In recent years, it is widely known that problem solving is a central subject and fundamental ability in the teaching and learning. Besides, problem solving is integrated in the STEM+C (Science, Technology, Engineering, and Math plus Computing, Coding or Computer Science) fields. Intelligent tutoring systems (ITSs) have been shown to be effective in supporting students' domain-level learning through guided problem solving practice. Intelligent tutoring systems provide personalized feedback (in the form of hints) to students and improve learning at effect sizes approaching that of human tutors. However, creating an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain’s curriculum. Creating an ITS requires time, resources, and multidisciplinary skills. Because of the large possible range of problem solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem solving domains. Data-driven ITSs have shown much promise in increasing effectiveness by analyzing past data in order to quickly generate hints to individual students. However, the fundamental long term goal was to develop “better, faster, and cheaper” ITSs. In this work, the main goal of this paper is to: 1) present ITSs used in the STEM+C education; and 2) introduce data-driven ITSs for STEM+C education.

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

scholarly journals Advances in Intelligent Tutoring Systems: Problem-solving Modes and Model of Hints

2007 ◽  
Vol 2 (1) ◽  
pp. 48 ◽  
Author(s):  
Alla Anohina
Keyword(s):  
Problem Solving ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Layer Model ◽  
Tutoring Systems ◽  
Adaptive Support ◽  
Two Layer Model ◽  
Adaptive Abilities

The paper focuses on the issues of providing an adaptive support for learners in intelligent tutoring systems when learners solve practical problems. The results of the analysis of support policies of learners in the existing intelligent tutoring systems are given and the revealed problems are emphasized. The concept and the architectural parts of an intelligent tutoring system are defined. The approach which provides greater adaptive abilities of systems of such kind offering two modes of problem-solving and using a two-layer model of hints is described. It is being implemented in the intelligent tutoring system for the Minimax algorithm at present. In accordance with the proposed approach the learner solves problems in the mode which is the most appropriate for him/her and receives the most suitable hint.

Sign in / Sign up

Export Citation Format

Share Document