Formalization of Pre-Learning Instructional Method Based on

Information Processing of Learner Driver

Yuta Kurihara

, Motoki Shino

, Wataru Miyazaki

, Minori Kizaki

, Katsuko T. Nakahira

4 a

and

Muneo Kitajima

4 b

Department of Human & Engineered Environmental Studies, The University of Tokyo,

Kashiwanoha, Kashiwa, Chiba, Japan

Department Mechanical Engineering, Tokyo Institute of Technology, Ookayama, Tokyo, Japan

Minami Holdings Co., Ltd., Onojo, Fukuoka, Japan

Department of Information & Management Systems Engineering, Nagaoka University of Technology,

Nagaoka, Niigata, Japan

Keywords:

Driving Skill Acquisition, Driver Training, Formalization of Instructional Methods, Cognitive Processing.

Abstract:

This study aims to formalize instructional methods for driving skill acquisition by examining learner drivers’

information processing during instruction. To examine the effects of pre-learning on skill development, ex-

periments were conducted to analyze how procedural knowledge provided before practice inﬂuences skill

acquisition. The experiment focused on two tasks: lane changes, which require precise execution of proce-

dures at moderate speeds, and S-curve navigation, which involves controlling a vehicle on narrow roads. The

results indicate that, for tasks requiring procedural accuracy, such as lane changes, providing procedural steps

as semantic knowledge before practice facilitated their conversion into procedural memory through verbal rep-

etition and stationary practice. In contrast, for S-curve navigation, effective skill acquisition was achieved by

managing vehicle speed through intermittent stops. This approach reduced working memory load and enabled

learners to more effectively predict vehicle position and orientation. This study underscores the importance

of tailoring pre-learning strategies to the speciﬁc demands of each task and contributes to the development of

instructional designs that enhance the efﬁciency and effectiveness of driving education.

1 INTRODUCTION

Driving skills require the integration of perception,

judgment, and motor operation, making effective ed-

ucation essential for cultivating safe drivers. In Japan,

driving schools play a central role, with government-

certiﬁed instructors providing personalized guidance

based on standardized requirements (National Po-

lice Agency, 2022). At these schools, government-

certiﬁed instructors provide personalized guidance to

learner drivers, resulting in variations in instructional

quality depending on the instructor’s background.

Traditionally, skill education has relied on prac-

titioners’ intuition and experience. In recent years,

efforts have been made to formalize experts’ tacit

knowledge, transforming it into explicit knowledge

and integrating it into educational processes through

https://orcid.org/0000-0001-9370-8443

https://orcid.org/0000-0002-0310-2796

digital transformation (DX). In sports, formalizing

expert movements and enabling novices to observe

and replicate these actions have been shown to signif-

icantly enhance skill acquisition (Nuruki et al., 2011).

Similarly, in piano education, an approach where be-

ginners compare videos of their own movements with

those of experts has been formalized, demonstrating

the effectiveness of e-learning materials based on this

method (Nakahira et al., 2011).

DX-based approaches have been explored for

driving skills. For instance, systems aiding steering

timing during reverse parking have been shown to re-

duce errors (Duan et al., 2019). Additionally, meth-

ods targeting perception and decision-making have

been developed to enhance learners’ hazard recogni-

tion (Crundall et al., 2017).

Despite these advancements, the processes in-

volved in acquiring driving operation skills remain

underexplored, particularly regarding how learner

drivers process instructional content and translate it

682

Kurihara, Y., Shino, M., Miyazaki, W., Kizaki, M., Nakahira, K. T. and Kitajima, M.

Formalization of Pre-Learning Instructional Method Based on Information Processing of Learner Driver.

DOI: 10.5220/0013389400003912

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2025) - Volume 1: GRAPP, HUCAPP

and IVAPP, pages 682-689

ISBN: 978-989-758-728-3; ISSN: 2184-4321

into actions. While previous studies have focused

on speciﬁc operations, such as reverse parking, using

technical approaches, instructional methods integrat-

ing real-time feedback and appropriate timing are yet

to be fully clariﬁed. Consequently, methods that con-

sider novice drivers’ cognitive processes remain un-

derdeveloped, and essential insights for constructing

effective instructional designs are still lacking.

In response to these challenges, this study aims

to formalize instructional methods for driving skills

by analyzing learner drivers’ behaviors and instructor

guidance, with a focus on cognitive processes during

skill acquisition. To achieve this, the study examines

the information processing underlying skill acquisi-

tion. Section 2 deﬁnes the targeted skills and out-

lines the assumed information processing framework

to establish a theoretical basis. Section 3 describes the

experiments to evaluate current instructional methods

while section 4 analyzes the results to clarify the in-

formation processing required for skill acquisition.

2 THEORETICAL FRAMEWORK

AND TARGET SETTING FOR

SKILL ACQUISITION

This section establishes the theoretical framework

for analyzing the skill acquisition process of learner

drivers and introduces models to understand the fun-

damental information processing involved in skill

learning. The speciﬁc skills targeted in this study are

then identiﬁed, and key focus points for instructional

methods are deﬁned in this research.

2.1 Assumed Information Processing

Fitts’ three-stage model (Fitts and Posner, 1967) pro-

vides the theoretical foundation for understanding

skill learning. This model describes skill acquisition

as progressing through three stages. The ﬁrst, the

cognitive stage, involves learners acquiring knowl-

edge of the new skill and understanding the associ-

ated procedures. The second, the associative stage,

focuses on enhancing the skill’s efﬁciency and ac-

curacy through repeated practice. Finally, the au-

tonomous stage is achieved, where the skill becomes

automatic, enabling learners to allocate attention to

other tasks. Based on this model, instructional meth-

ods should align with learners’ progress through these

stages, emphasizing the appropriate timing and con-

tent of guidance.

Additionally, the Cognitive Model (Moreno and

Mayer, 2007) is employed to analyze how learner

drivers process information and acquire skills. As

shown in Figure 1, external information enters work-

ing memory, activates related long-term memory,

and is then processed and translated into motor ac-

tions. Conversely, processed information is inte-

grated into long-term memory, which includes declar-

ative memory (semantic memory for knowledge and

language, and episodic memory for personal ex-

periences) and non-declarative memory (procedural

memory for skills and habits).

Figure 1: The cognitive model for skill acquisition (Moreno

and Mayer, 2007).

Using these two models, this study investigates

how instructors’ guidance is integrated into learners’

memory, forms procedural memory, and facilitates

skill acquisition based on the timing of instruction.

2.2 Research Targets

To understand the information processing involved in

learner drivers’ acquisition of driving operation skills,

this study deﬁnes the targeted skills. In Japanese driv-

ing schools, training is divided into two phases: the

ﬁrst focuses on skill acquisition within the driving

school, while the second involves developing these

skills on public roads. This study focuses on the ﬁrst

phase, emphasizing the acquisition of new skills. For

learners who have already mastered basic operations,

such as starting and cornering, instructional items can

naturally be categorized into two main groups.

The ﬁrst category consists of training items aimed

at learning actions regulated by trafﬁc rules. For in-

stance, tasks such as lane changes and obstacle avoid-

ance require learners to perform precise visual checks

and execute vehicle operation procedures accurately

and efﬁciently The second category involves tasks

that develop learner’s ability to estimate the vehicle’s

position relative to the road using visual informa-

tion. As shown in Figure 2, these tasks enhance spa-

tial awareness, helping learners navigate narrow roads

Figure 2: The S-curve used in driving training.

Formalization of Pre-Learning Instructional Method Based on Information Processing of Learner Driver

683

by determining appropriate paths and speeds. Exam-

ples include navigating S-curves and L-shaped crank

courses. Based on these two categories, this study tar-

gets lane changes and S-curve navigation as represen-

tative tasks for actions regulated by trafﬁc rules and

tasks requiring spatial awareness.

2.3 Targeted Learning Based on

Current Instructional Methods

Based on survey, current instructional methods for

lane changes and S-curve navigation are structured

into three stages: pre-learning (before practice), prac-

tice, and feedback (after practice).

During pre-learning, instructors explain the proce-

dures using training manuals. For lane changes, they

outline the required steps, while for S-curve naviga-

tion, they emphasize maintaining low speed before

detailing the navigation process. Learners then prac-

tice on the actual course and receive feedback after-

ward. When aligned with the information process-

ing model in Figure 1, pre-learning guidance primar-

ily helps learners store procedural steps as seman-

tic memory in long-term memory. During practice,

learners use this memory to perform driving opera-

tions, converting the knowledge into procedural mem-

ory and acquiring skills. Feedback allows learners to

reﬂect on experiences stored as episodic memory, en-

hancing their understanding.

Among these instructional phases, pre-learning is

considered the most inﬂuential for practice and feed-

back. Therefore, this study focuses on pre-learning,

speciﬁcally examining and comparing the practice be-

haviors of learners who successfully acquired skills

with those who did not after pre-learning. This anal-

ysis aims to identify the information processing re-

quired for skill acquisition after learners memorize

new skills during the pre-learning phase. Based on

these insights, the study seeks to formalize instruc-

tional methods grounded in learners’ information pro-

cessing, as outlined in Section 1, speciﬁcally within

the context of pre-learning.

3 PRE-LEARNING EXPERIMENT

This section presents an experiment designed to for-

malize effective instructional methods by evaluating

the impact of pre-learning procedural information on

skill acquisition. Learners studied pre-learning mate-

rials based on instructors’ guidance, and their proce-

dural accuracy was measured during subsequent prac-

tice to evaluate the effectiveness of skill acquisition.

The participants were 10 learner drivers evaluated as

having acquired basic driving skills after completing

training.

3.1 Evaluation

To evaluate skill acquisition, a sensor-equipped in-

structional vehicle was used to measure driving per-

formance, including speed, steering angle, and self-

position estimation (Figure 3). Since visual behavior

is a key indicator of driving performance (Land and

Lee, 1994), a gaze tracking device (Tobii Pro Glasses

2) was also employed to record learners’ visual be-

havior. The recorded data were logged as time-series

ﬁxation targets, as shown in Figure 5.

For lane changes, the evaluation metric focused on

the accuracy of following the instructed procedures.

Skill acquisition was considered successful if learn-

ers executed the procedures correctly before the third

practice attempt. For S-curve navigation, the evalu-

ation criteria included the avoidance of wheel drop-

offs, along with the accuracy of vehicle operation and

visual behavior. A “wheel drop-off” was deﬁned as

a situation where a tire completely falls off the edge

of the road or where more than half of the tire width

extends beyond the road curb. To rule out random

success, skill acquisition was deemed successful if

learners completed both the second and third attempts

without wheel drop-offs.

Figure 3: The vehicle used for driving instruction (Handa

et al., 2023).

3.2 Procedure

In this experiment, learner drivers ﬁrst drove around

the outer loop of the training course to become famil-

iar with driving. Next, for lane changes, participants

studied the pre-learning materials shown in Figure 4

for 5 minutes and then took a written memory test

on the procedure. Those who did not achieve a per-

fect score repeated this study and test process until

they accurately reproduced the procedure in writing,

conﬁrming that participants had memorized the pro-

cedure. They then drove the lane change course three

times as practice. After completing the lane change

experiment, the same procedure was applied to the

HUCAPP 2025 - 9th International Conference on Human Computer Interaction Theory and Applications

684

S-curve course. If a wheel drop-off occurred during

practice, the vehicle’s automatic braking system (Fig-

ure 3) was activated, and the experimenter intervened

to help the participant return to the course.

(a) Pre-learning material for lane change

(b) Pre-learning material for S-course

Figure 4: Pre-learning materials.

3.3 Experimental Results

3.3.1 Lane Change

Among the 10 participants, one was excluded because

they had already received prior instruction on lane

changes. Among the remaining 9 participants, only

one (Participant A) successfully performed the pro-

cedure accurately by the third attempt. For the re-

maining participants, omissions or order errors per-

sisted through the third attempt. In particular, during

the third attempt, all 8 participants skipped the right-

rear check (Step 3), 2 omitted the side mirror check

(Step 3), and 1 performed steps 1 to 3 out of order.

Changes in procedural accuracy across practice at-

tempts were analyzed to observe patterns in partici-

pants’ performance. Participant A showed progres-

sive improvement, initially omitting steps 1 and 3 but

completing all steps by the third attempt. Among

the other 8 participants, 2 exhibited gradual improve-

ment, while 3 showed no change, and 3 experienced a

decline, failing to execute previously completed steps.

In summary, only Participant A achieved full pro-

cedural accuracy by the third attempt, whereas the

majority (6 participants) showed no improvement or

declined in performance despite repeated practice.

3.3.2 S-Curve Navigation

Among the 10 participants, 3 successfully acquired

the skill, while 7 did not. Figure 5 shows the rela-

tionship between visual behavior and vehicle speed

for one participant who acquired the skill. The hori-

zontal axis represents the time from S-curve entry to

exit. The left vertical axis indicates gaze targets, with

each rectangular bar showing the object being ﬁxated

on and the duration of ﬁxation. The colors of the rect-

angles correspond to steps 1–5 in Figure 4. The right

vertical axis displays vehicle speed as a gray line.

Based on the graph, the participant’s speed inter-

mittently dropped to zero during the ﬁrst attempt. In

terms of visual behavior, the participant primarily fo-

cused on the curbs on both sides at the entrance of

the S-curve and on the right curb during the left turn,

consistent with the pre-learning material. In contrast,

during the second and third attempts, the participant

maintained a continuous speed while keeping their

gaze consistently on the same targets. The other two

participants who successfully acquired the skill dis-

played similar patterns of behavior.

In contrast, examples of participants who failed

to acquire the skill are provided. Three participants

who did not intermittently stop during the ﬁrst at-

tempt all experienced wheel drop-offs in subsequent

attempts. Additionally, four participants who initially

reduced their speed intermittently still experienced

wheel drop-offs. Figure 6 compares the driving op-

erations of one such participant with those of the par-

ticipant shown in Figure 5. Figure 6 illustrates the ﬁrst

50 seconds of S-curve navigation, with the left verti-

cal axis representing speed and the right vertical axis

representing the steering angle, where negative values

indicate right turns and positive values indicate left

turns. The left graph shows data from a participant

who failed to acquire the skill and experienced wheel

drop-offs, while the right graph represents the ﬁrst at-

tempt of a participant who successfully acquired the

skill. By comparing these graphs, as highlighted by

circles in the ﬁgure, participants who successfully ac-

quired the skill made gradual adjustments to the steer-

ing angle, while those who failed made abrupt and

rapid steering movements.

In summary, the differences between successful

and unsuccessful participants can be attributed to two

key factors: effective speed management, particularly

the ability to execute intermittent stops, and gradual

steering adjustments.

4 DISCUSSION

The experimental results reveal that memorizing pro-

cedures through pre-learning materials and practicing

based solely on that memorization may sometimes be

insufﬁcient for learner drivers to fully acquire skills.

However, some learners successfully acquired skills

Formalization of Pre-Learning Instructional Method Based on Information Processing of Learner Driver

685

Figure 5: Visual behavior and vehicle speed during S-curve navigation.

Figure 6: Comparison of steering operations.

Figure 7: Information processing based on timing.

despite identical memorization tasks, suggesting that

differences in information processing during the prac-

tice phase inﬂuenced these outcomes.

This study organizes the instructional content pro-

vided by experienced instructors during practice and

feedback sessions for each skill. By analyzing this

content, the study clariﬁes the information processing

required of learner drivers and examines how these

factors contribute to skill acquisition. Finally, the

study formalizes pre-learning instructional methods

based on the observed conﬁrmation and operational

behaviors of learners, as well as the guidance pro-

vided by experienced instructors.

4.1 Analysis of Instructional Content

This section examines instructional content during

pre-learning, practice, and feedback, using the infor-

mation processing model described in Section 2.3.

The analysis relies on the framework shown in Fig-

ure 1, which illustrates how information is processed

after entering working memory through sensory ﬁl-

ters. The ﬁgure shows how procedural information,

acquired as Semantic Knowledge during pre-learning,

is gradually transformed into Procedural Memory

through practice and feedback. This transformation

forms the foundation for analyzing the role of instruc-

tional methods in skill acquisition.

Instructional content was collected from instruc-

tors’ verbal guidance, gestures, and recorded driving

behavior data. Additionally, learners’ behavior was

measured using the training vehicle (Figure 3) and the

gaze tracking device (Tobii Pro Glasses 2) described

in Section 3.1, as instructional content may change

depending on learners’ driving behavior.

4.2 Lane Change

4.2.1 Analysis of Lane Change Instruction

The instructional methods of experienced instructors

for lane changes were analyzed, focusing on their ef-

fectiveness. Based on Figure 7, the following instruc-

tional practices were identiﬁed: First, during the pre-

learning phase, the instructors taught the procedures

described in Figure 4. During the practice phase, re-

gardless of the learner, experienced instructors pro-

vided concise verbal cues, such as ”rearview mirror”

and ”signal”, which represented each step at the ap-

propriate timing. Finally, during the feedback phase,

instructors pointed out learners’ mistakes, reviewed

the procedures verbally with them, and guided them

to rehearse and execute the procedures in a stationary

vehicle.

The impact of this instructional content on skill

acquisition is analyzed based on Figure 7. First, dur-

ing the pre-learning phase, learners retained procedu-

ral information as semantic knowledge. During the

HUCAPP 2025 - 9th International Conference on Human Computer Interaction Theory and Applications

686

practice phase, this was activated through visual and

auditory inputs from the road environment and in-

structor cues, resulting in motor outputs. Therefore,

semantic knowledge must be easy to activate and ap-

plicable during practice. To evaluate this condition,

the procedural instruction was assessed using Cog-

nitive Load Theory (Sweller, 1988), which catego-

rizes cognitive demands into intrinsic load (task com-

plexity), extraneous load (unnecessary elements in the

learning environment), and germane load (elements

that facilitate learning). According to this theory, by

excluding additional information—such as explana-

tions of why certain actions are necessary—and fo-

cusing solely on procedural execution, learners can

concentrate on the steps themselves, reducing task

complexity and minimizing intrinsic load. This ap-

proach ensures that the provided semantic knowledge

meets the condition of being easy to activate and di-

rectly applicable to motor outputs during practice.

During the practice phase, providing only key-

words representing each step likely served, as shown

in Figure 7(b), to trigger learners’ procedural memory

through external stimuli. This approach is considered

effective for tasks like lane changes, where precise ac-

tions must be performed within a short time, as it fa-

cilitates the rapid transformation of activated memory

into motor outputs. Furthermore, since the capacity

of Working Memory is limited, supporting the acti-

vation of already provided information without intro-

ducing new elements likely plays a role in reducing

extraneous load.

Finally, regarding feedback, verbalizing proce-

dural steps has been shown to generally enhance

the accuracy and speed of subsequent motor actions

(Guadagnoli et al., 1992). Additionally, repeating the

steps in a stationary state allows learners to review

them in a low cognitive load environment, facilitating

their transformation into procedural memory in ad-

vance. Therefore, by ensuring that learners accurately

recognize their mistakes and rehearse the steps in a

stationary vehicle, it is believed that they retain ver-

balized semantic knowledge that is easily converted

into procedural memory, alongside procedural mem-

ory developed in a stationary state. This preparation

aids in performing high-speed lane changes in subse-

quent practice.

In summary, pre-learning instruction provided

learners with semantic knowledge that could be effec-

tively transformed into procedural memory. During

practice and feedback, the instruction focused on aid-

ing this transformation, facilitating skill acquisition.

4.2.2 Formalizing Instructional Content Based

on Learner Drivers’ Behavior

Based on the previous section and as described in Sec-

tion 3.3.1, this discussion examines the factors that

led to omissions in procedural execution during prac-

tice, despite learners retaining information about the

procedures. Lane changes require quick execution of

steps at vehicle speeds of 10–20 km/h, and in this ex-

periment, the lack of support for activating semantic

knowledge during practice likely resulted in delayed

activation. In contrast, feedback involving verbaliza-

tion of the steps and repetition in a stationary vehi-

cle likely facilitated the transformation into procedu-

ral memory. These ﬁndings suggest that for skills re-

quiring procedural execution at higher speeds, retain-

ing procedures as semantic knowledge alone is insuf-

ﬁcient; verbalized guidance and repetition in a sta-

tionary environment are effective.

Additionally, the low number of participants who

improved procedural accuracy with increased practice

is discussed. Unlike tasks such as avoiding wheel-

drops in an S-curve, lane changes lack easily recog-

nizable error indicators, and participants may have

mistakenly believed they executed the steps correctly

without adjusting their behavior. This limitation, due

to the lack of feedback, highlights the necessity of

providing feedback for procedural improvement.

4.3 S-Curve Navigation

4.3.1 Analysis of S-Curve Navigation Instruction

Similar to the lane change instruction, the instruc-

tional content for S-curve navigation is summarized

as follows: During the pre-learning phase, as shown

in Figure 4, instructors emphasized the importance of

low-speed control using the brakes before teaching

the speciﬁc steps for navigating the S-curve.

During the practice phase, instruction varied

based on learner performance. For learners driv-

ing at excessive speed, instructors focused solely on

speed control using the brakes. For those maintain-

ing an appropriate speed, instructors provided addi-

tional guidance, such as encouraging them to look

further ahead along the curve to guide their visual be-

havior. For learners who started steering too early in

left curves, instructors avoided direct intervention, in-

stead prompting them to consider whether the front-

right wheel was aligned with the outer edge of the

curve, helping them recognize their steering errors.

In the feedback phase, instructors emphasized the

importance of speed control for learners who drove

too fast, explaining that excessive speed limited their

ability to predict vehicle positioning. They also

Formalization of Pre-Learning Instructional Method Based on Information Processing of Learner Driver

687

guided learners on how to increase awareness of ve-

hicle position and orientation. For example, they ex-

plained that even if the right curb was no longer visi-

ble, the front-right wheel remained aligned under the

driver’s feet, allowing for further adjustments. In-

structors also taught learners to predict vehicle move-

ments based on steering inputs, encouraging them to

set the steering angle, observe the vehicle’s response,

and make adjustments as needed.

The impact of this instructional content is ana-

lyzed using the information processing model in Fig-

ure 7. Procedural guidance is comparable to that pro-

vided for lane changes. The effect of speed manage-

ment during course navigation is also analyzed. In

S-curve navigation, in addition to activating Seman-

tic Knowledge, it is necessary to predict vehicle posi-

tion and orientation based on visual information from

the road environment and to execute precise maneu-

vers accordingly. Practicing at low speeds not only

allows sufﬁcient time for activating procedural mem-

ory but also allocates more Working Memory capac-

ity to vehicle control based on visual inputs, as il-

lustrated in Figure 7(b). As a result, even though

S-curve navigation involves additional tasks, such as

predicting vehicle position and orientation compared

to lane changes, cognitive load can be effectively re-

duced. The emphasis on strict speed management

during practice serves the same purpose.

As shown in Figure 7(c), feedback focused on

guiding learners to adjust their visual and operational

behaviors based on their practice memories retained

as Episodic Knowledge. For example, learners who

experienced wheel drop-offs in left curves were in-

structed on the necessity of predicting vehicle posi-

tion and orientation using visual inputs, as well as

methods to correct their steering. This approach fos-

ters self-feedback, which is essential in the associative

stage of skill acquisition (Fitts and Posner, 1967). By

helping learners understand the relationship between

visual inputs, vehicle control, and the outcomes of

their actions based on Episodic Knowledge, the re-

tention of driving skills can be effectively promoted.

In summary, the pre-learning phase emphasized

speed management to free up Working Memory ca-

pacity, enabling learners to focus on predicting ve-

hicle position and orientation. During practice and

feedback, learners were guided to understand the rela-

tionship between visual information and vehicle con-

trol based on the outcomes of their actions.

4.4 Formalizing Instructional Content

Based on Learner Drivers’ Behavior

The differences between participants who success-

fully acquired the skills and those who did not are an-

alyzed, focusing on two key points: intermittent stop-

ping, and ﬁne steering adjustments. This discussion

particularly emphasizes intermittent stopping related

to speed management, which was a primary focus of

instructor guidance.

Based on the instructors’ guidance, this behav-

ior was effective for skill acquisition in two main

ways during subsequent practice sessions. First, in-

termittent stopping allowed participants to focus on

key visual targets and allocate more working mem-

ory capacity to processing visual information, such

as predicting vehicle position and orientation within

the S-curve. After resuming movement, participants

could concentrate on vehicle control based on their

prior predictions, which improved their overall per-

formance.

Second, as illustrated in Figure 7(c), intermittent

stopping enabled participants to verify whether their

predicted vehicle position and orientation matched

the actual vehicle behavior during short-distance

movements. This process facilitated more accurate

execution of steering corrections, as emphasized in

the instructional content.

4.5 Formalization of Instructional

Methods in Pre-Learning

Based on the identiﬁed information processing char-

acteristics of learner drivers, pre-learning instruc-

tional methods are summarized in Table 1. For lane

changes, memorizing the steps in a verbalized for-

mat and practicing them in a stationary vehicle fa-

cilitates their transformation into procedural memory,

making it easier to convert the information into proce-

dural memory. For S-curve navigation, both procedu-

ral and speed management information are retained as

Semantic Knowledge, with intermittent stopping used

to optimize working memory for predicting vehicle

position and orientation, as well as executing corre-

sponding maneuvers.

5 CONCLUSION

This study aimed to formalize instructional methods

in driving education by focusing on the information

processing characteristics of learner drivers. Specif-

ically, the effects of pre-learning instruction on skill

HUCAPP 2025 - 9th International Conference on Human Computer Interaction Theory and Applications

688

Table 1: Formalization of pre-learning instructional methods.

Category Objective Reason

Lane Change

Memorize procedural information in a ver-

bal format and practice in a stationary state.

To allocate sufﬁcient working memory for con-

verting actions into motor outputs.

S-Curve

Navigation

Memorize low-speed control with intermit-

tent stops.

To allocate working memory for predicting vehi-

cle position and orientation, and for associated op-

erations.

acquisition were experimentally evaluated, and the in-

structional content was analyzed based on instructors’

guidance. As a result, the instructional methods were

formalized as follows: For tasks like lane changes,

which require the accurate execution of procedures

at a consistent speed, procedural steps are retained in

a verbalized format and practiced in a stationary ve-

hicle, facilitating their transformation into procedural

memory. For tasks such as S-curve navigation, which

involve vehicle control on narrow roads, procedural

information must be accompanied by speed manage-

ment strategies - speciﬁcally intermittent stopping - to

allocate working memory during practice and encour-

age the prediction of vehicle position and orientation.

In summary, this study focused on the informa-

tion processing characteristics of learner drivers and

successfully formalized instructional methods for pre-

learning by evaluating its impact on skill acquisi-

tion. However, this study was limited to pre-learning

and did not address the formalization of instructional

methods for practice and feedback phases. Addition-

ally, the analysis was restricted to lane changes and S-

curve navigation, leaving the generalizability to other

tasks unveriﬁed. Future work will focus on formal-

izing instructional methods for practice and feedback

phases and integrating them into a comprehensive in-

structional design, followed by evaluating its effec-

tiveness.

ACKNOWLEDGEMENTS

We would like to express our gratitude to Minami-

Fukuoka Driving School for providing their facilities

as the experimental site for this study.

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