Emotional Impact for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

Recent studies have shown that Matrix Factorization (MF) method, deriving from recommendation systems, can predict student performance as part of Intelligent Tutoring Systems (ITS). In order to improve the accuracy of this method, we hypothesize that taking into account the mutual influence effect in the relations of student groups would be a major asset. This criterion, coupled with those of the different relationships between the students, the tasks and the skills, would thus be essential elements for a better performance prediction in order to make personalized recommendations in the ITS. This paper proposes an approach for Predicting Student Performance (PSP) that integrates not only friendship relationships such as workgroup relationships, but also mutual influence values into the Weighted Multi-Relational Matrix Factorization method. By applying the Root Mean Squared Error (RMSE) metric to our model, experimental results from KDD Challenge 2010 database show that this approach allows to refine student performance prediction accuracy.

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

International Journal of Artificial Intelligence in Education ◽

10.1007/s40593-021-00267-x ◽

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.

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A Meta-Analytic Estimation of a Common Effect Size from a Series of Experiments Related To an E-Learning System Effectiveness Evaluation

Intelligent Tutoring Systems in E-Learning Environments ◽

10.4018/978-1-61692-008-1.ch016 ◽

2011 ◽

pp. 327-341

Author(s):

Ani Grubišic

Keyword(s):

Student Performance ◽

Effect Size ◽

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Meta Analysis ◽

Learning System ◽

Tutoring Systems ◽

Learning And Teaching ◽

E Learning ◽

Series Of Experiments

As the acquisition of knowledge is often an expensive and time-consuming process, it is important to know whether it actually improves the student performance. The e-learning is a revolutionary paradigm that has lately been significantly evolving and it is closely related to the intelligent tutoring systems. Methodology for evaluating the educational influence of learning and teaching process, questions whether and in what amount, students learn effectively. Our contribution to this compulsive field of research is a meta-analysis of a series of experiments based on the same two-group methodology that reveals a more precise effect size of one particular e-learning system - eXtended Tutor-Expert System, a representative of web-based authoring shells for building intelligent tutoring systems.

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A New Approach to Modelling Students’ Socio-Emotional Attributes to Predict Their Performance in Intelligent Tutoring Systems

Journal of Education and e-Learning Research ◽

10.20448/journal.509.2021.83.340.348 ◽

2021 ◽

Vol 8 (3) ◽

pp. 340-348

Author(s):

Kouamé Abel ASSIELOU ◽

Cissé Théodore HABA ◽

Tanon Lambert KADJO ◽

Bi Tra GOORE ◽

Kouakou Daniel YAO

Keyword(s):

Student Performance ◽

Performance Prediction ◽

Intelligent Tutoring Systems ◽

Cognitive Abilities ◽

Mean Squared Error ◽

Intelligent Tutoring ◽

Tutoring Systems ◽

Social And Emotional ◽

Computer Based Learning ◽

The Impact

Intelligent Tutoring Systems (ITS) are computer-based learning environments that aim to imitate to the greatest possible extent the behavior of a human tutor in their capacity as a pedagogical and subject expert. One of the major challenges of these systems is to know how to adapt the training both to changing requirements of all kinds and to student knowledge and reactions. The activities recommended by these systems mainly involve active student performance prediction that, nowadays, becomes problematic in the face of the expectations of the present world. In the associated literature, several approaches, using various attributes, have been proposed to solve the problem of performance prediction. However, these approaches have failed to take advantage of the synergistic effect of students' social and emotional factors as better prediction attributes. This paper proposes an approach to predict student performance called SoEmo-WMRMF that exploits not only cognitive abilities, but also group work relationships between students and the impact of their emotions. More precisely, this approach models five types of domain relations through a Weighted Multi-Relational Matrix Factorization (WMRMF) model. An evaluation carried out on a data sample extracted from a survey carried out in a general secondary school showed that the proposed approach gives better performance in terms of reduction of the Root Mean Squared Error (RMSE) compared to other models simulated in this paper.

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An Intelligent framework for Blended Learning

Revista Colombiana de Computación ◽

10.29375/25392115.3441 ◽

2018 ◽

Vol 19 (2) ◽

pp. 37-45

Author(s):

Armando Ordóñez ◽

Martha Giraldo G. ◽

Freddy Muñoz ◽

Hugo Ordoñez ◽

Yeni Rosero

Keyword(s):

Student Performance ◽

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Online Course ◽

Tutoring Systems ◽

Personalized Education ◽

Open Source Framework ◽

Implementation Costs

Personalized education contributes to the motivation of the students and improves student performance. Some tools such as the Intelligent Tutoring Systems have been proposed to this purpose with excellent results. However, most of the existing works have given little attention to the role of the teachers. In this article, an open source framework based on a standard intelligent tutoring system is presented. The framework aims at reducing the implementation costs and the complexity of the interfaces, in addition, the framework considers the participation of teachers. The framework was used to create a math course for an elementary school student, and will be used as a basis for the personalization of a Small Private Online Course.

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