ABSTRACT
Feature-rich applications such as word processors and spreadsheets are not only being used by adults but increasingly by children and older adults as well. Learning these applications is challenging as they offer hundreds of commands throughout the interface. We investigate how newcomers from different age groups explore the user interface of a feature-rich application to determine, locate, and use relevant features. We conducted an in-lab observational study with 10 children (10-12), 10 adults (20-35) and 10 older adults (60-75) who were first-time users of Microsoft OneNote. Our results illustrate key exploration differences across age groups, including that children were careful and performed as efficiently as the adults, whereas older adults spent a long time and repeated sequences of failed selections. Further, their exploration style was negatively influenced by their past knowledge of similar applications. We discuss design interventions to accommodate these exploration differences and to improve software onboarding for newcomers.
Author Keywords
Age-related differences, lab study, desktop/laptop GUI, newcomers’ exploration strategy
CCS Concepts
Human-centered computing → Graphical user interfaces, Empirical studies in HCI, Social and professional topics
INTRODUCTION
Modern feature-rich applications such as word processors, spreadsheets, and 3D modeling packages offer hundreds of commands organized under various menus, toolbars, and navigation structures. These applications are powerful and highly flexible but can be overwhelming and difficult to learn [25, 30, 40]. One common way for users to learn a new application is to explore the functionality displayed on the user interface [5, 47]. However, exploring the interface of a feature-rich application can be challenging because users must determine which features are needed to accomplish their tasks, understand how individual features work (in isolation and together), and locate relevant features in the interface [23, 45].
Increasingly, newcomers to feature-rich software include a diverse group of users. For example, children are using various productivity and learning applications in digital classrooms [41, 58]. With greater flexibility in retirement age, older adults (65+) are working longer and learning to use new applications for knowledge work [3, 22, 53]. Prior work has shown that children can be more eager to explore software than adults [9, 29] whereas older adults can be fearful to explore new applications and can have lower confidence levels [10, 36, 59]. Other work has also shed light on how different age groups approach new technologies [3, 27, 51]. However, little is known about differences and similarities in how users explore the interface of a feature-rich application when learning to use it for the first-time.
Tackling the problem of interface exploration for feature-rich software is more important than ever before: by some estimates, at least 25% of users are abandoning an application after just a single-use [44]. Application onboarding can be a crucial part of a user’s journey [4], but there are few insights from the HCI literature about how to design such onboarding experiences, particularly those that support the natural interface exploration styles of the different age groups and keep them motivated to learn the application.
With this issue in mind, the core research questions that we explore in this paper are: What are the age-related differences in users’ exploration styles when using a feature-rich application for the first time? In particular, how do different user groups determine, locate, and use relevant features within the application? How do they deal with performance breakdowns? Characterizing these differences in exploration styles of different age groups could help designers make more inclusive design decisions.
We conducted a structured observational study with 30 newcomers to a feature-rich application, Microsoft OneNote 10 children (10-12), 10 adults (20-35), and 10 older adults (60- 75). Our goals were to identify and characterize the exploration styles of the different age groups. We captured detailed interaction data (via software logs) to quantitatively compare exploration styles. We also supplemented our quantitative analysis with brief post-session interviews to obtain participants’ retrospective assessments of what made exploration easy or difficult. Based on both our quantitative and qualitative findings, we propose design implications for applications seeking to foster efficient exploration and onboarding experiences for the different age groups.
Our work contributes (1) simultaneous investigation of the interface exploration styles of three age cohorts – children, adults, and older adults; (2) identification of the challenges that each age group faces when exploring the interface of a feature-rich application to accomplish a goal; (3) identification of the different strategies that each age group uses to deal with hurdles during interface exploration; (4) design implications to support efficient interface exploration for the different age groups; and (5) a codebook that other researchers can leverage to investigate exploratory learning in GUIs.
CONCLUSION
When learning a feature-rich application for the first time, users often explore different menus and features to accomplish their desired tasks. Today, these applications are being used by children, adults, and older adults alike. Our study contributes insights into the interface exploration styles of the three age groups, the challenges that they face, and the strategies that they use to deal with breakdowns. We found, among other things, that children explore the interface carefully but struggle to locate contextual menus (because of lack of mouse exposure), whereas older adults have difficulties determining the relevant sequence of features and repeat failed selections. Our work is an important step towards understanding the diversity in users’ approaches to learning through exploration which has implications for improving their application onboarding experiences through design.
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:
Learning Through Exploration: How Children, Adults, and Older Adults Interact with a New Feature-Rich ApplicationBibliography
author
Year
2020
Title
Learning Through Exploration: How Children, Adults, and Older Adults Interact with a New Feature-Rich Application
Publish in
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Doi
https://doi.org/10.1145/3313831.3376414
PDF reference and original file: Click here
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.
