A Machine Learning Approach to Pronominal Anaphora Resolution in Dialogue Based Intelligent Tutoring Systems

2014 ◽  
pp. 307-318 ◽  
Author(s):  
Nobal B. Niraula ◽  
Vasile Rus
Keyword(s):  
Machine Learning ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Learning Approach ◽  
Anaphora Resolution ◽  
Tutoring Systems ◽  
Machine Learning Approach ◽  
Pronominal Anaphora

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 A tutorial on machine learning for 
interactive pedagogical systems

2018 ◽  
Vol 5 (3) ◽  
pp. 79-112
Author(s):  
Francisco S Melo ◽  
Samuel Mascarenhas ◽  
Ana Paiva
Keyword(s):  
Machine Learning ◽  
Intelligent Tutoring Systems ◽  
Serious Games ◽  
Intelligent Tutoring ◽  
Short Introduction ◽  
Tutoring Systems ◽  
Key Concepts

This paper provides a short introduction to the field of machine learning for interactive pedagogical systems. Departing from different examples encountered in interactive pedagogical systems—such as intelligent tutoring systems or serious games—we go over several representative families of methods in machine learning, introducing key concepts in this field. We discuss common challenges in machine learning and how current methods address such challenges. Conversely, by anchoring our presentation on actual interactive pedagogical systems, highlight how machine learning can benefit the development of such systems.

"A hybrid machine learning approach to Automated Scenario Generation (ASG) to support adaptive instruction in virtual simulations and games"

2018 ◽  
Author(s):  
Robert A. Sottilare
Keyword(s):  
Machine Learning ◽  
Adaptive Systems ◽  
Learning Objectives ◽  
Learning Needs ◽  
Learning Approach ◽  
Adaptive Instruction ◽  
Tutoring Systems ◽  
Machine Learning Approach ◽  
Virtual Simulations ◽  
Hybrid Machine

"This paper examines machine learning methods to automatically generate a large number of child scenarios from a small number of parent scenarios in support of adaptive instruction conducted in virtual simulations and game-based platforms. Adaptive instructional systems (AISs) include Intelligent Tutoring Systems (ITSs), intelligent mentors, recommender systems, personal assistants, and intelligent instructional media. AISs attempt to tailor instruction for individuals and teams based on their learning needs (e.g., knowledge or skill deficiencies), goals, and preferences. This often requires much more content than current non-adaptive systems which provide one or a very limited set of training scenarios to address a given set of learning objectives. The goal of the research described in this paper is to reduce the authoring burden for developing a large number of unique and relevant training scenarios. The methodology presented also ranks the resulting scenarios with respect to a set of author-specified learning objectives and learner/team competency in the domain of instruction. The unique contributions of this paper are tied to its hybrid machine learning approach, and consideration for both learning objectives and learner/team competency in automatically ranking generated scenarios."

A Machine Learning Approach to Anaphora Resolution in Nepali Language

2020 ◽  
Author(s):  
Apurbalal Senapati ◽  
Arun Poudyal ◽  
Prithwiraj Adhikary ◽  
Sahana Kaushar ◽  
Anmol Mahajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Anaphora Resolution ◽  
Machine Learning Approach

A multi‐strategy machine learning student modeling for intelligent tutoring systems

2013 ◽  
Vol 31 (2) ◽  
pp. 274-293 ◽  
Author(s):  
Mu‐Jung Huang ◽  
Heien‐Kun Chiang ◽  
Pei‐Fen Wu ◽  
Yu‐Jung Hsieh
Keyword(s):  
Machine Learning ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Student Modeling ◽  
Tutoring Systems
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