ICoME 2022

Cultivating Road User Safety Attitudes to Differing Levels of
Vehicle Automation: An Online Collaborative Learning Approach
Yoshiko Goda
Kumamoto University, Japanygoda@kumamoto-u.ac.jp
Maki Arame
Polytechnic University of Japan, Japan
arame@uitec.ac.jp
Junko Handa
Polytechnic University of Japan, Japanj-handa@uitec.ac.jp
Masashi Toda
Kumamoto University, Japan
toda@cc.kumamoto-u.ac.jp
Makoto Itoh
University of Tsukuba, Japanitoh.makoto.ge@u.tsukuba.ac.jp
Satoshi Kitazaki
National Institute of Advanced Industrial Science and Technology, Tsukuba-city, Japansatoshi-kitazaki@aist.go.jp
Most safety education is designed for individual or whole group knowledge and skill acquisition and may not adequately
cultivate preferred safety attitudes. The study addresses the design of online collaborative learning from two perspectives: (1)online community-based self- and peer reflection and (2) integration of online collaborative tools and technologies. To develop
the three components of safety attitude (cognitive, affective, and behavioral), the proposed teaching approach drew on
Community of Inquiry theory to support reflection and learner engagement. The Zoom video conferencing system (Zoom)
and Miro were utilized for online collaborative learning, and short educational movies provided a more concrete sense of
automated driving. For the 21 undergraduates who participated in the study, the learning outcomes indicate that the video
material enhanced their overview of the system and functions of these innovative cars.

Keywords: safety education, safety attitude, online collaborative learning, automated car, s

Background

Introduction

Cars are currently assigned to one of six levels of automation, ranging from level 0 (no automation) to level 5 (fullautomation) (SAE J3016, 2021). At present, level 3 cars are generally the highest level sharing the road with othertraffic. Most road users recognize the importance of safety education; among the available types of education andtraining, most are either lecture-based (where an instructor talks to multiple participants in a classroom) or involve
self-paced e-learning. However, recent safety education initiatives have emphasized the interaction between instructor
and participants. In particular, it is recognized that improving safety attitude requires self-reflection through social
learning, and some researchers have tried to develop more effective, efficient, and attractive approaches based oncollaborative learning (e.g., Koivisto & Mikkonen, 1997). The present study addresses the design of online collaborative
learning from two perspectives: (1) online community-based self- and peer reflection and (2) integration of online
collaborative tools and technologies.

Automated Car Usage and Education

Related Literature

Up to level 2 (partial automation), the driver remains largely responsible for driving. At level 3 or higher, the driver
can do things other than driving when the car is operating in automated mode, with some restrictions (e.g., no sleeping

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or drinking). At level 3 (conditional automated driving), the driver must still be careful even in automated mode, andthey must be ready to take over at the car’s request. Automated cars may help to reduce deaths or injuries in traffic
accidents and increase transport options for the elderly and disabled. Some of these vehicles are now in experimental
use as public transportation in towns with declining populations or where bus drivers are difficult to recruit or retain.
Level 2 cars are now becoming commonplace, and there are also some level 3 cars on the market. The driver’s roles
and responsibilities are different at levels 2 and 3. For example, level 3 requires the driver to return to intervene (RtI)
when the automated function is no longer available. Cars of different levels now coexist on the road, withcorresponding differences in vehicle behavior. For example, level 3 cars may reduce speed and stop or execute
emergency maneuvers as a result of RtL. As Zhou et al. (2018) reported, the RtI response is shorter and smootherwhen the driver has some knowledge of the automated car, but knowledge and skill alone may not suffice for safe
driving. Zhou et al. (2019) suggested that knowledge acquired from a short lecture may not be retained and is likely to
be forgotten after some period of time. To enhance learning retention, then, there is a need for more effective
education and learning interventions.

Safety Attitude
Hakatta et al. (2002) proposed a hierarchical model of driver behavior based on four layers adopted from Keskinen(1996): (1) vehicle maneuvering; (2) mastering traffic situations; (3) driving goals and context; and (4) life skills andgoals. The highest layer (4) places cars and driving in the context of personal development and skills for self-control,
including safety attitude, as critical factors in safe and secure transportation. Eagly and Chaiken (1993, p.1) defined
attitudes as “tendencies to evaluate an entity with some degree of favour or disfavour, ordinarily expressed in cognitive,
affective and behavioural responses.” Attitudes are important predictors of behavior (Kraus, 1995), and individual
differences in risk-taking extend beyond unsafe behaviors to general attitudes and behavioral tendencies (Haga, 1999),
including pedestrians’ attitudes and behaviors (Dinh et al., 2020).

Online Collaborative Learning
The Community of Inquiry (CoI) framework (Garrison, 2011) is frequently applied to the design of online collaborative
learning. The CoI framework comprises three components: teaching presence, social presence, and cognitive presence.
It is important to ensure that these three presences are balanced (Goda, in print) and appropriately integrated in
education and learning experiences. Fiock (2020) described an instructional approach to increase learner engagement
and interaction quality based on Sorensen and Baylen’s (2009) seven principles of good practice and CoI. Increased
learner engagement enhances the learning experience (Parrish, Wilson, & Dunlap, 2011), which is the ultimate goal of
CoI-based learning (Garrison, 2011). Fiock summarized good practices and collaborative tools for effective online
collaboration.

Research Design and Methods
Design of Online Collaborative Learning Intervention
Self- and peer-reflection are known to be effective methods for cultivating attitudes (Koivisto & Mikkonen, 1997). In
Gagné’s Taxonomy of Learning, attitude is categorized as an affective domain (Gagné et al., 1992). According to Gagné(1984), attitude is unlikely to be influenced by a lecture-based approach, and vicarious reinforcement based on
observation and reflection on human models may be more effective. Attitudes are also known to be difficult tomeasure, and self-report questionnaires are often used. In shaping desired attitudes, it is important first to support
knowledge acquisition about the relevant facts and their impacts. In the present case, the workshop was designed to
impart knowledge about automated cars and the coexistence of different levels. For the purposes of motivation andsubject knowledge acquisition, instructional videos (Arame et al., 2022) were used, along with a movie exploring theeffects of external communication from an automated car on pedestrians’ behavior (Arame et al., 2021). The video
material helped participants to gain a more concrete sense of the functions and impacts of these vehicles.
In accordance with study restrictions, the workshop was delivered entirely online and lasted for 90 minutes. The
collaborative work was carefully designed to take account of the three presences described in the CoI framework. The
online seminar was implemented using the Zoom video conferencing system and Miro, a tool for online collaboration.
The design specified when and how these ICT tools were to be integrated into online learning (see Table 1). Participants
were required to complete a prequestionnaire and pretest before the workshop began.
During the workshop, participants were required to connect using Zoom. The main Zoom room was used for thewhole-class lecture and discussion, and the breakout rooms were used for small group discussions. Miro frames were2

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preset for learning activities (see Figure 1), and participants used the frames one by one with accompanying instruction.
As an opening activity set for the whole class, participants were instructed to share their experiences of accidents ornear misses as drivers and road users. This activity served to activate existing knowledge and increased attention andsocial presence in CoI mode.
Table 1
Design of Workshop for Safety Attitudes: Activities and ICT Tools/Learning Materials
Time(min.)
5
10

15

10
10
15

Activity

Description

ICT Tools and Learning Materials

Prequestionnaire andpretestOpening

Complete prequestionnaire and pretest

Online forms (prequestionnaire andpretest)ZOOM(Mainroom &
・Opening video with cases
breakfor initial attention (2 min.)rooms)/ ・ Video: Self-driving car
ppt file(5 min.)・Video: Outward
communication (2 min.)Miro[Lecture] Overview ofself-driving cars;
implications for society
when different levelsof self-driving cars
coexist.
[Discussion 1(D1)] Asa traffic user(pedestrian, etc.)[Lecture] Review of
D1 deliverables[Lecture] Coexistenceof self-driving cars at
different levels[Discussion 2 (D2)] Asa driver10[Lecture] Let’s checkthe other groups’
results on Miro

10

[Lecture] Review of
D2 deliverables[Lecture] Example ofscale for safe driving

105

Wrap-up andpostquestionnaire
Postquestionnaire andposttest

Briefly introduce self to group; share traffic
safety experience.
Learn about self-driving cars!
Summary of self-driving cars. How does society
look when different levels of self-driving cars
coexist? What are the three components ofattitude?
What attitudes should we adopt to ensure
traffic safety where different levels of
autonomous vehicles coexist?
As a traffic user, organize these attitudes interms of knowledge, emotions, and actions.
Review outcomes on Miro.
Confirm Miro activities. Thinking about safe
attitudes as a driver
Where autonomous vehicles of different levels
coexist, what attitudes should we adopt toensure safe traffic use?
As a driver, organize these attitudes in terms ofknowledge, emotions, and actions.
Look at other groups’ outputs and review yourown group’s output. Place a red-blue sticker onanother team’s board on Miro to indicate
consent/disagreement. Think about how you
can contribute to safe, secure, and smoothtransportation.
Review outcomes on Miro.
Let’s look at an example of a scale for safe
driving! Let’s think about a safe attitude interms of the scale items.
Interval for reflection on the seminar.

・Opening video with story
for increasing motivation (2
min.)
Miro

Miro

Online forms: postquestionnaire andposttest

Two group discussions encouraged participants to think about safe behaviors from the dual perspectives of driver andtraffic user. To support self- and peer- reflection, the three components of attitude were introduced, and participants
were instructed to organize their near-miss experiences in terms of these three components. To make the processeasier for participants who were unfamiliar with automated cars, video footage of the cars and the 3D environment
were presented during the lectures along with the research results. As well as posting their own group’s comments in
Miro, participants were asked to check and evaluate other groups’ posts in order to engage with a range of views andopinions related to safety attitudes.
Once the group discussions had finished, some of the scale items s related to unsafe behavior were introduced tostimulate further views on safety. Preliminary assessment of the procedure in the form of expert reviews and small
group assessment was conducted two weeks before the workshop to implement any necessary revisions.

Participants

For the purposes of this study, 21 sophomore and senior undergraduate business majors were recruited as participants.

Data Collection
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The study questionnaire included one item about holding a driver’s license, nine items (SA1 to SA9 in Table 2) about
general safety attitudes (Oya & Haga, 2016), and three open-ended questions about the merits and demerits ofautomated cars and opinions about the workshop. The safety-related items from Oya and Haga were general and did
not refer specifically to driving. In the exercises asking participants to consider road safety from the perspectives ofdrivers and road users, the cognitive test used 20 multiple-choice questions to assess their knowledge of automatedcars, based on the content of the instructional videos shown during the workshop. The questionnaire and test wereadministered as Google forms before and after the workshop.

Figure 1 Screenshot: Using Miro for collaborative learning.

Results
Of the 21 participants recruited for the workshop, only 17 completed all of the questionnaires and tests, and thesewere included in the analysis. Table 2 summarizes the results of paired t-testing of safety attitude items. The results
were statistically significant for the test (t(16)=3.755, p = 0.002) and for SA4 (“When doing something, I reflect on
accidents and problems that occurred in the past” (t(16)= 2.729, p = 0.015) and tended toward significance for SA3(“Before I start anything, I check the set procedures and safety precautions”) (t(16)= 1.768, p = 0.096) and for SA8(“If safety cannot be confirmed, I stop what I am doing”) (t(16)= -1.807, p = 0.09).
Responses to the open-ended questions about automated cars listed their merits, including reduction of human error,
accident rate, and fatigue due to extended driving. In relation to the workshop, positive opinions were expressed about
the video materials, instructional flow, and the workshop theme.

Discussion
In designing a workshop to cultivate safety attitudes in the context of automated cars, the CoI framework was deployed
to design online collaborative learning activities and ICT use. The approach proved effective in increasing cognitiveand social presence in the opening activity and ensuring the smooth transition of attention to the workshop theme.
The results were satisfactory at levels 1 and 2 of Kirkpatrick’s evaluation model (Kirkpatrick, 1998); participants
expressed positive opinions in the post questionnaire, and posttest scores increased significantly. In relation to safetyattitude, before and after comparisons confirmed an increase in reflection on past cases (SA4). This outcome was
expected, as three collaborative activities (including the opening activity) required participants to recall past experiencesof accidents or near misses. Results for SA3 (checking safety procedures and precautions) were statistically significant,and SA8 (“If safety cannot be confirmed, I stop what I am doing”) also showed a significant tendency, with a negative
t value. This result is difficult to interpret, but it suggests that participants felt they should not start something before
checking safety, and the negative result for SA8 therefore seems reasonable. The other SA items proved more difficult
to influence in the short term, which may help to explain why further significant differences were not observed.

Conclusion
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Attitude change takes time, and there is a need for ongoing observation after the workshop to evaluate its impact.
Safety education should target everyone from children to the elderly, as well as foreign visitors to cultivate preferredsafe attitudes to automated cars for road safety, security, and smoothness. Future studies should encompass a wider
range of participants over longer periods of time.
Table 2
Results of paired t-test (2-tailed) for test and safety attitudes (N = 17)Paired Differences
t

df

Sig.
(2tailed)

3.755

16

0.002

M

S.D.

S.E.M

Posttest Total Score - Pretest Total 2.294
Score
SA1*. Be sure to follow safety rules -0.059
and established procedures.
SA2_R. Sometimes I don't follow 0.118
safety rules or routines when I am
confident that it’s okay.
SA3. Before I start anything, I check 0.294
the set procedures and safety
precautions.
SA4. When doing something, I reflect 0.529
on accidents and problems that
occurred in the past.
SA5_R. Even if I take some risks, I -0.059
complete tasks in time according to
the work schedule.
SA6. To prevent human error, I have 0.235
some concrete methods.
SA7. If I am uncertain about what I 0.176
am doing, I make sure to adopt a safe
approach.
SA8. If safety cannot be confirmed, I -0.412
stop what I am doing.
SA9_R. As far as possible, I do not 0.059
want to participate in safety education,
training, and seminars.
Note. *SA: safety attitude; R: reverse item.

2.519

0.611

95% ConfidenceInterval
Lower
Upper
0.999
3.589

0.243

0.059

-0.184

0.066

-1

16

0.332

0.993

0.241

-0.393

0.628

0.489

16

0.632

0.686

0.166

-0.059

0.647

1.768

16

0.096

0.8

0.194

0.118

0.941

2.729

16

0.015

0.966

0.234

-0.5560.438-0.251

16

0.805

0.664

0.161

-0.106

0.577

1.461

16

0.163

0.529

0.128

-0.095

0.448

1.376

16

0.188

0.939

0.228

-0.8950.071-1.807

16

0.09

0.659

0.16

-0.28

0.397

0.368

16

0.718

Paired Item (Post - Pre)

Acknowledgement

This work was supported by the Council for Science, Technology and Innovation (CSTI), the Cross-ministerial
Strategic Innovation Promotion Program (SIP) under the title “Human Factors and HMI Research for AutomatedDriving” (funding agency: NEDO).

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