How to Integrate Emotions in Dialogues With Pedagogic Conversational Agents to Teach Programming to Children

2020 ◽  
pp. 66-91
Author(s):  
Elizabeth Katalina Morales-Urrutia ◽  
Jose Miguel Ocaña ◽  
Diana Pérez-Marín
Keyword(s):  
Primary Education ◽  
Interactive Systems ◽  
Conversational Agents ◽  
Teacher Student ◽  
Multiple Domains

Pedagogic conversational agents are interactive systems that allow students to dialogue with them about a certain domain to learn. PCAs have been used in multiple domains from pre-primary education to university, in roles such as teacher, student, or companion. In this chapter, Alcody, a PCA to teach programming to children, is enhanced with a new proposal to manage emotions in the dialogue with students. The goal is that when children are learning to program, Alcody can help them with the emotions associated to the learning. Six emotions have been integrated into Alcody: happiness, anger, sadness, fear, surprise, and disgust. A description of how a PCA to teach programming can modify its face and verbal expressions according to the emotion detected in the student. This is given for any other researcher that would like to incorporate emotions in dialogues between PCAs and students.

How to Integrate Emotions in Dialogues With Pedagogic Conversational Agents to Teach Programming to Children

2022 ◽  
pp. 876-899
Author(s):  
Elizabeth Katalina Morales-Urrutia ◽  
Jose Miguel Ocaña ◽  
Diana Pérez-Marín
Keyword(s):  
Primary Education ◽  
Interactive Systems ◽  
Conversational Agents ◽  
Teacher Student ◽  
Multiple Domains

Pedagogic conversational agents are interactive systems that allow students to dialogue with them about a certain domain to learn. PCAs have been used in multiple domains from pre-primary education to university, in roles such as teacher, student, or companion. In this chapter, Alcody, a PCA to teach programming to children, is enhanced with a new proposal to manage emotions in the dialogue with students. The goal is that when children are learning to program, Alcody can help them with the emotions associated to the learning. Six emotions have been integrated into Alcody: happiness, anger, sadness, fear, surprise, and disgust. A description of how a PCA to teach programming can modify its face and verbal expressions according to the emotion detected in the student. This is given for any other researcher that would like to incorporate emotions in dialogues between PCAs and students.

scholarly journals Prof. Watson: A Pedagogic Conversational Agent to Teach Programming in Primary Education

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 84
Author(s):  
Yeves-Martínez ◽  
Pérez-Marín
Keyword(s):  
Primary Education ◽  
Research Interest ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Global Trend ◽  
Alternative Procedures

Teaching programming in Primary Education has recently attracted a great deal of research interest. One global trend is using multimedia languages such as Scratch. However, it was our belief that by using Pedagogic Conversational Agents that dialog with the students, they have to think how to solve given problems and to write the code to solve them. In particular, the MECOPROG methodology was applied to design the student-agent dialog in Prof. Watson. An experiment with 19 students (11–12 years old) was carried out proving the viability of the approach, which shed some light into alternative procedures to teach programming in Primary Education.

scholarly journals Comparative Study of Kuttab Islamic Education System and Madrasah Ibtidayah Education System

2021 ◽  
Vol 9 (1) ◽  
pp. 1-19
Author(s):  
Fathurrahman Muhtar
Keyword(s):  
Primary Education ◽  
Education System ◽  
Educational Institution ◽  
Education Institution ◽  
Islamic Education ◽  
Empirical Content ◽  
13Th Century ◽  
Reference Analysis ◽  
Literature Research ◽  
Teacher Student

This article shows the numerous benefits created in the 7th-13th century A.D. by a primary education institution named Kuttab. Kuttab is an educational institution that teaches the Qur'an to be read and memorized, history to be studied, and Arabic verses. This article utilizes an approach to literature research, gathering different references related to Kuttab. The empirical content process then evaluates the relation, and conclusions are drawn from the reference analysis. This paper concludes that with education in Madrasah Ibtidaiyah, Kuttab institutions have a similar curriculum. The excellence of Kuttab focuses more on memorizing the Qur'an so that in the golden age (7-12 years), Kuttab students will remember the entire material of the Qur'an, in addition to teachers who have excelled and skills. Unlike Ibtidaiyah madrasahs, which do not focus students on memorizing the Qur'an, and as teachers in Kuttab, teachers are also far from professionalism. In comparison to the current madrasah Ibtidaiyah curriculum, the learning materials in Kuttab are thin.Keywords : curriculum, kuttab, madrasah, teacher, student.

scholarly journals Analysis of Teacher-Student Interaction in the Joint Solving of Non-Routine Problems in Primary Education Classrooms

2020 ◽  
Vol 12 (24) ◽  
pp. 10428
Author(s):  
Beatriz Sánchez-Barbero ◽  
José María Chamoso ◽  
Santiago Vicente ◽  
Javier Rosales
Keyword(s):  
Problem Solving ◽  
Primary Education ◽  
Mathematics Classroom ◽  
Student Interaction ◽  
Process Selection ◽  
Teacher Student ◽  
Cognitive Difficulty ◽  
Teacher Student Interaction ◽  
Teachers And Students ◽  
Problem Solving Process

The analysis of teacher–student interaction when jointly solving routine problems in the primary education mathematics classroom has revealed that there is scarce reasoning and little participation on students’ part. To analyze whether this fact is due to the routine nature of the problems, a sample of teachers who solved, together with their students, a routine problem involving three questions with different cognitive difficulty levels (task 1) was analyzed, describing on which part of the problem-solving process (selection of information or reasoning) they focused their interaction. Results showed that they barely focused the interaction on reasoning, and participation of students was scarce, regardless of the cognitive difficulty of the question to be answered. To check whether these results could be due to the routine nature of the problem, a nonroutine problem (task 2) was solved by the same sample of teachers and students. The results revealed an increase in both reasoning and participation of students in processes that required complex reasoning. This being so, the main conclusion of the present study is that including nonroutine problem solving in the primary education classroom as a challenging task is a reasonable way to increase students’ ability to use their own reasoning to solve problems, and to promote greater teacher–student collaboration. These two aspects are relevant for students to become creative, critical, and reflective citizens.

A Proposal to Model Interaction from the Analysis of Student - Pedagogic Conversational Agent Logs

2013 ◽  
pp. 148-162
Author(s):  
Diana Pérez-Marín ◽  
Ismael Pascual-Nieto
Keyword(s):  
Human Computer Interaction ◽  
Interactive Systems ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Practical Application ◽  
Model Interaction ◽  
Multiple Personality ◽  
Evaluation Techniques ◽  
Computer Interaction

According to User-Centered Design, computer interactive systems should be implemented taking into account the users’ preferences. However, in some cases, it is not easy to apply conventional Human-Computer interaction evaluation techniques to identify the users’ needs and improve the user-system interaction. Therefore, this chapter proposes a procedure to model the interaction behaviour from the analysis of conversational agent dialog logs. A case study in which the procedure has been applied to model the behaviour of 20 children when interacting with multiple personality Pedagogic Conversational Agents is described as an illustrative sample of the goodness and practical application of the procedure.

БАШТАЛГЫЧ КЛАССТАРДА МАТЕМАТИКА САБАГЫН ОКУТУУДА МААЛЫМАТТЫК ТЕХНОЛОГИЯЛАРДЫ КОЛДОНУУ

2020 ◽  
pp. 132-134
Author(s):  
Насыйкат Картанбаева
Keyword(s):  
Information Technology ◽  
Primary Education ◽  
Teaching And Learning ◽  
Information Technologies ◽  
Educational Material ◽  
Educational Process ◽  
Educational Tool ◽  
Teacher Student ◽  
The Subject ◽  
Self Knowledge

Аннотация. Азыркы күндө билим берүү процессин жүргүзүүдө эл аралык стандартка шайкеш келүүчү заманбап маалыматтык технологияларды киргизүү жана мугалимдин ийгиликтүү иш алып баруусу үчүн зарыл болгон шарттарды камсыз кылуу – бул азыркы мезгилдин негизги талаптарынан болуп саналат. Себеби маалыматтык технологиялар окутуучуларга окуу процессин сапаттуу, жаңы деңгээлге алып чыгууга шарт түзөт. Бул макалада билим берүү процессинде башталгыч класстарда математика сабагын окутууда маалыматтык технологияларды колдонуунун жолдору каралды. Математика сабагын окутууда компьютердик жаңы технологияларды пайдалануу, окутуу процессин өркүндөтүүнүн бирден-бир каражаты катары саналып, окуучулардын математика сабагына кызыгуусун жана өз алдынча билимге ээ болуу ишмердигин өркүндөтүүнүн мүмкүнчүлүгүн арттырат. Түйүндүү сөздөр: маалыматтык технологиялар, окуу процесси, окутуу каражаты, билим берүү, математика сабагы, мугалим, окуучу. Аннотация: В этой статье рассматривается процесс обучения в системе начального образования. Изучаются способы использования информационных технологий в школе по предмету математика. Уроки математики с использованием новых компьютерных технологий в преподавании и обучении дает возможность совершенствования деятельности самопознания во время ученого процесса, что приводит к повышению интереса учащихся к математике и увеличивает возможность улучшения восприятия учебного материала. Ключевые слова: информационные технологии, образовательный процесс, средство обучения, образование, математика, предмет, учитель, ученик. Abstract: This article discusses the learning process in primary education. The ways of using information technologies in school on the subject of mathematics are studied. Mathematics lessons with the use of new computer technologies in teaching and learning makes it possible to improve the activity of self-knowledge during the academic process, which leads to an increase in the interest of students in mathematics and increases the possibility of improving the perception of educational material. Key words: Information technology, educational process, educational tool, education, mathematics subject, teacher, student.

How to Create a Pedagogic Conversational Agent for Teaching Computer Science

2019 ◽  
pp. 114-134
Author(s):  
José Miguel Ocaña ◽  
Elizabeth K. Morales-Urrutia ◽  
Diana Pérez-Marín ◽  
Silvia Tamayo-Moreno
Keyword(s):  
Natural Language ◽  
Computer Science ◽  
Primary Education ◽  
Computational Thinking ◽  
Computer Applications ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Teacher Team

Pedagogic conversational agents are computer applications that can interact with students in natural language. They have been used with satisfactory results on the instruction of several domains. The authors believe that they could also be useful for the instruction of computer science programming. Therefore, in this chapter, the MEDIE methodology is described to explain how to create an agent to teach programming to primary education children and develop their computational thinking. The main steps are to communicate with the teacher team, to validate the interface, and to validate the functionality, practical sessions, and evaluation. The first two steps are covered in this chapter.

scholarly journals Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset

2020 ◽  
Vol 34 (05) ◽  
pp. 8689-8696
Author(s):  
Abhinav Rastogi ◽  
Xiaoxue Zang ◽  
Srinivas Sunkara ◽  
Raghav Gupta ◽  
Pranav Khaitan
Keyword(s):  
Large Scale ◽  
Training Data ◽  
Conversational Agents ◽  
Dialogue System ◽  
Conversational Interface ◽  
State Tracking ◽  
Public Datasets ◽  
Multiple Domains ◽  
Task Oriented ◽  
Slot Filling

Virtual assistants such as Google Assistant, Alexa and Siri provide a conversational interface to a large number of services and APIs spanning multiple domains. Such systems need to support an ever-increasing number of services with possibly overlapping functionality. Furthermore, some of these services have little to no training data available. Existing public datasets for task-oriented dialogue do not sufficiently capture these challenges since they cover few domains and assume a single static ontology per domain. In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains. Our dataset exceeds the existing task-oriented dialogue corpora in scale, while also highlighting the challenges associated with building large-scale virtual assistants. It provides a challenging testbed for a number of tasks including language understanding, slot filling, dialogue state tracking and response generation. Along the same lines, we present a schema-guided paradigm for task-oriented dialogue, in which predictions are made over a dynamic set of intents and slots, provided as input, using their natural language descriptions. This allows a single dialogue system to easily support a large number of services and facilitates simple integration of new services without requiring additional training data. Building upon the proposed paradigm, we release a model for dialogue state tracking capable of zero-shot generalization to new APIs, while remaining competitive in the regular setting.

Learning a Generalizable Model of Team Conflict from Multiparty Dialogues

2021 ◽  
Vol 15 (04) ◽  
pp. 441-460
Author(s):  
Ayesha Enayet ◽  
Gita Sukthankar
Keyword(s):  
Task Conflict ◽  
Communication Styles ◽  
Semantic Features ◽  
Conversational Agents ◽  
Good Communication ◽  
Team Conflict ◽  
Dialogue Acts ◽  
Multiple Domains ◽  
Machine Learning Models ◽  
Better Than

Good communication is indubitably the foundation of effective teamwork. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomena in which humans synchronize their linguistic choices. Conversely, teams may experience conflict due to either personal incompatibility or differing viewpoints. We tackle the problem of predicting team conflict from embeddings learned from multiparty dialogues such that teams with similar post-task conflict scores lie close to one another in vector space. Embeddings were extracted from three types of features: (1) dialogue acts, (2) sentiment polarity, and (3) syntactic entrainment. Machine learning models often suffer domain shift; one advantage of encoding the semantic features is their adaptability across multiple domains. To provide intuition on the generalizability of different embeddings to other goal-oriented teamwork dialogues, we test the effectiveness of learned models trained on the Teams corpus on two other datasets. Unlike syntactic entrainment, both dialogue act and sentiment embeddings are effective for identifying team conflict. Our results show that dialogue act-based embeddings have the potential to generalize better than sentiment and entrainment-based embeddings. These findings have potential ramifications for the development of conversational agents that facilitate teaming.

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