ABSTRACT
Smart devices have become an integral part of the everyday lives of children. Today, children can even use voice-based interactions to interact with devices for a wide range of activities. Previous research has shown that voice-driven interfaces have the potential to offer a potent new mechanism for teaching, engaging, and supporting children in daily life. Our paper, therefore, argues that it is critical not only to investigate how children use voice-based interactions to communicate with devices (e.g., smart speakers) but also the nature of relationships that children form with these devices, the influence such use has on children’s learning and behavior, and the role that parents or guardians play in deciding the norms of use for children. We also propose to explicitly and intricately investigate complexities in use and its impact relative to entangled identities (conveyed through overlapping attributes of gender, ethnicity, race, class) and larger social systems. To this end, we propose to use Social Learning Theory to understand how children learn through observing and interacting with smart devices, specifically using voice-based commands. Methodologically, we will conduct participatory design sessions and follow-up interviews to get a nuanced understanding of how children mentally contextualize voice-enabled smart devices and how social influence (e.g., parental expectation/norms), the social function of identification (e.g., children’s emotional connection with technology), and learning goals impact their usage patterns.
CCS CONCEPTS
Human-centered computing→User studies; Empirical studies in HCI; Scenario-based design; Participatory design.
KEYWORDS
Voice-based interactions, Children’s behavior and learning practices, Social learning theory, Parasocial relationships
INTRODUCTION
Today, various types of smart devices are deeply integrated into our day to day lives. The use of technology has increased not only for adults but also for children, be it as a source of entertainment or as a learning aid. So much so, that the exposure and use of technology have been considered as a crucial influence on the process of learning and development of children [5]. Bower and Sturman demonstrated that wearable devices offer a range of pedagogical uses (in-situ contextual information, recording, simulation, communication, first-person view, in-situ guidance, feedback, distribution, and gamification), afford benefits to educational quality (engagement, efficiency, and presence), and provide logistical advantages (handsfree access and free up space) in a classroom setting [2]. More recently, smart devices (e.g., smartphones, tablets, smart speakers) have started to offer conversational assistants (e.g., Amazon Alexa, Siri, and Google Now) that lend flexible means of interacting with the device. Due to the presence of such voice assistants, children no longer need to read or write to be able to interact with the devices [9]. As the amount of background information a child needs to use these devices has reduced, it can have an impact in the information seeking, behavioral (e.g., children might imitate and emulate certain characteristics of these devices), and learning practices pursued by children and the factors that affect these practices. Hence, in this paper, we argue that it is critical to investigate how and why children are using these devices (e.g., voice-connected speakers), and the influence voice-based interactions with devices has on children’s behavior and learning practices.
We propose to investigate this issue through the lens of Bandura’s Social Learning Theory (SLT). It explains ‘observational learning’ in terms of how people learn through observing others behavior, attitudes, and the outcomes (penalty or reward) one might incur due to such a behavior [1]. However, SLT is a complex and subjective concept with many different facets to it, exploring all of which is beyond the scope of this study. Therefore, to understand ‘observational learning’ our study centers around the three social factors provided by Over et al [10]. These three factors are social function of identification (parasocial relationships), the type of role that children associate with voice assistants; learning goals, the type of learning tasks that children use voice assistants for; social norms and customs, particularly focussing on the role that parents/guardians play in regulating children’s use of technology. Therefore, we will investigate three fundamental research questions in this paper:
RQ1: What are the type of parasocial relationships that child form with the voice-enabled smart devices?
RQ2: What are the type of learning objectives or tasks that children are interested to use voice-enabled smart devices for and how parasocial relationships may or may not impact those?
RQ3: How do social norms and customs (especially those instilled/followed by parents and guardians) affect the way children use voice-connected smart devices?
To answer these questions we aim to conduct Participatory Design (PD) sessions and a set of interviews with design groups consisting of children from different age groups: 7- 9 years, 10-12 years, and 13-17 years. This will enable us to investigate the role age and gender of children within the context of our research questions. For example, we will explore if the nature of parasocial relations/role that younger children associate with voice-enabled smart devices differ from those that older children associated with these devices.
CONCLUSION
Our position paper proposes to investigate how voice-based interactions with smart devices are affecting or can affect the learning practices of children. Particularly, we use the three factors by Oven et al. to operationalize the use of SLT as a tool to answer our research questions on how children mentally contextualize voice-enabled smart devices and how social influence (e.g., parental expectation/norms), the social function of identification (e.g., children’s emotional connection with technology), and learning goals impact their usage patterns. To this end, we propose to employ both historical log analysis and participatory design sessions.
About KSRA
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
FULL Paper PDF file:
Impact of Voice-based Interaction on Learning Practices and Behavior of ChildrenBibliography
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Year
2019
Title
Impact of Voice-based Interaction on Learning Practices and Behavior of Children
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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
