Pedagogical Agent Gestures to Improve Learner Comprehension of Abstract Concepts in Hints

In most Intelligent Tutoring Systems, the help messages (hints) are not very clear for students as they are only presented textually and have little connection with the task elements. This can lead to students' undesired behaviors, like gaming the system, associated with low performance. In this paper, the authors aim at evaluating if the gestures of an animated pedagogical agent to explain hints related to equation solving improves the students' understanding of these help messages. With this goal, they developed an animated pedagogical agent that uses gestures coupled with messages to explain hints in an algebra tutor. The authors performed a qualitative pilot study with four students to verify the impact of using gestures by the animated pedagogical agent on the comprehension of the hints, using two different versions of the system. The difference between these versions was the availability of gestures by the agent. The results showed that students understood the hints provided by the agent more correctly when they were coupled with agent's gesture. Furthermore, they also preferred using the tutor version with gestures.

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Building a Social Conversational Pedagogical Agent

Conversational Agents and Natural Language Interaction ◽

10.4018/978-1-60960-617-6.ch006 ◽

2011 ◽

pp. 128-155 ◽

Cited By ~ 20

Author(s):

Agneta Gulz ◽

Magnus Haake ◽

Annika Silvervarg ◽

Björn Sjödén ◽

George Veletsianos

Keyword(s):

Intelligent Tutoring Systems ◽

Social Relationship ◽

Intelligent Tutoring ◽

Development Project ◽

Pedagogical Agents ◽

Pedagogical Agent ◽

Ongoing Research ◽

Tutoring Systems ◽

Potential Abuse ◽

Design Challenges

This chapter discusses design challenges encountered when developing a conversational pedagogical agent. By tracing the historical roots of pedagogical agents in Intelligent Tutoring Systems (ITS), we discern central developments in creating an agent that is both knowledgeable and fosters a social relationship with the learner. Main challenges faced when attempting to develop a pedagogical agent of this kind relate to: i) learners’ expectations on the agent’s knowledge and social profile, ii) dealing with learners’ engagement in off-task conversation and iii) managing potential abuse of the agent. We discuss these challenges and possible ways to address them, with reference to an ongoing Research & Development project, and with a focus on the design of a pedagogical agent’s visual embodiment and its conversational capabilities.

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Intelligent tutoring systems and learning performance

Online Information Review ◽

10.1108/oir-11-2017-0340 ◽

2019 ◽

Vol 43 (4) ◽

pp. 600-616 ◽

Cited By ~ 2

Author(s):

Ali Yuce ◽

A. Mohammed Abubakar ◽

Mustafa Ilkan

Keyword(s):

Student Satisfaction ◽

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Knowledge System ◽

Learning Performance ◽

Equation Modeling ◽

Content Type ◽

Tutoring Systems ◽

Constructive Feedback ◽

The Impact

Purpose Intelligent 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/approach Data were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data. Findings Data 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 satisfaction with ITS did not generate higher learning performance. However, student satisfaction with ITS did improve learning motivation and resulted in superior learning performance. Data suggest this is due to students receiving constant and constructive feedback while simultaneously collaborating with their peers and teachers. Originality/value This study verifies that there was a need to assess the benefits of ITS. Based on the study’s findings, theoretical and practical implications are proposed.

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DÓRIS — Pedagogical Agent in Intelligent Tutoring Systems

Intelligent Tutoring Systems - Lecture Notes in Computer Science ◽

10.1007/3-540-47987-2_14 ◽

2002 ◽

pp. 91-104 ◽

Cited By ~ 3

Author(s):

Cássia Trojahn dos Santos ◽

Rejane Frozza ◽

Alessandra Dhamer ◽

Luciano Paschoal Gaspary

Keyword(s):

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Pedagogical Agent ◽

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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