Visual Intelligence for Program Animation

Mohamed Moussa and Nada Sharaf

The German International University, Cairo, Egypt

Keywords:

Program Visualization, Interactive Learning Environments, Program Animation, Algorithm Visualizer.

Abstract:

This paper introduces CodeMotion: an AST-driven web-based program visualization tool designed to improve

learners’ understanding of JavaScript code execution and algorithmic concepts through interactive visual ex-

ploration. By parsing user-submitted code into Abstract Syntax Trees (ASTs), the system enables secure,

step-by-step simulations of program logic without direct execution, addressing common security and scalabil-

ity issues found in traditional approaches. In contrast to existing browser-based and educational visualization

tools, our platform offers granular control over the execution process, supports algorithm visualization (e.g.,

sorting), and is easily extendable to other code structures. Usability testing with a sample of computer sci-

ence students demonstrates that this approach reduces complexity, increases user conﬁdence, and leads to

measurable improvements in comprehension. The tool allows users to trace their programs not pre-deﬁned

algorithms. This allows them to gain more understanding of their bugs. In addition, various aspects of the

visualization are controlled by the user.

1 INTRODUCTION

Program visualization tools have evolved from static

textual explanations to dynamic, interactive environ-

ments that facilitate deeper engagement with code.

Traditional learning resources often struggle to con-

vey the complexity of algorithm execution and logic

ﬂow in an intuitive way. While existing solu-

tions—such as browser-based code editors, debug-

gers, and educational visualizers—offer incremental

improvements, they frequently rely on direct code ex-

ecution and lack a structural representation of pro-

grams that can enhance learning.

This paper presents a novel, AST-driven web-

based visualization tool aimed at bridging these gaps.

Its core contribution lies in leveraging Abstract Syn-

tax Trees to provide a structured, language-aware

model of JavaScript code. By doing so, learners can

explore code execution step-by-step, visualize algo-

rithm states, and manipulate representations without

the security and ﬂexibility limitations of direct code

evaluation.

Unlike standard browsers or existing educational

platforms that show primarily static snapshots or ba-

sic line-by-line execution, the AST-based design of

our tool allows for a more informed and granular

representation of the program. We integrate inter-

active features like dynamic highlighting of current

execution steps, visual transformations of arrays dur-

ing sorting, and explicit representation of conditionals

and loops.

We position this tool as an educational supplement

for beginners and a conceptual aid for more experi-

enced learners. The method is initially applied to fun-

damental algorithms, such as sorting, but the under-

lying architecture is designed for broader scalability.

Through careful evaluation using System Usability

Scale (SUS) survey (Lewis, 2018) and controlled user

tasks, we demonstrate its positive impact on compre-

hension and engagement. The following sections de-

tail the background, implementation, evaluation re-

sults, and future directions of this approach.

This paper introduces CodeMotion, a powerful

tool that signiﬁcantly enhances the way developers in-

teract with their code by enabling them to trace and

understand their own scripts, not just predeﬁned al-

gorithms. This capability is particularly crucial for

debugging, allowing developers to delve deeply into

their speciﬁc implementations to effectively identify

and resolve issues. By tracing their personal pro-

grams, users gain a deeper understanding of the un-

derlying mechanics of their code, which greatly im-

proves their ability to debug and optimize efﬁciently.

CodeMotion stands out due to its high degree of

customizability, empowering users to tailor the visu-

alization experience to meet their individual needs.

Moussa, M. and Sharaf, N.

Visual Intelligence for Program Animation.

DOI: 10.5220/0013432000003932

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 1, pages 565-572

ISBN: 978-989-758-746-7; ISSN: 2184-5026

565

This ﬂexibility allows for adjustments in how visual-

izations represent code execution, such as controlling

how variables are displayed during the process to pro-

vide clear insights into how values evolve. Addition-

ally, CodeMotion’s ability to visualize complex data

structures like arrays offers a comprehensive view of

data manipulation and ﬂow, enhancing the traditional

debugging environment. By creating an interactive

and adjustable visualization environment, CodeMo-

tion serves as an indispensable resource for program-

mers at all levels, aiming to enhance their coding pro-

ﬁciency and problem-solving capabilities.

2 RELATED WORK

Visualization is recognized as a powerful way of pre-

senting complex concepts (Repenning and Sumner,

1995). Various deﬁnitions of Algorithm Visualization

(AV) have been proposed, describing it as the visual

depiction of an algorithm’s execution using discrete

or continuous graphical images that are controlled by

the user (Naps et al., 2000; Keller and Keller, 1993;

Fortner, 1995; Saraiya et al., 2004). AV is often

viewed as mapping sequences of digital operations to

graphical renderings (Naps et al., 2000) and as pro-

ducing animated abstractions of an algorithm’s states

(Kerren and Stasko, 2002).

According to (Naps et al., 2000), the purposes of

Algorithm Visualization (AV) are to help users under-

stand the algorithm’s technique, comprehend why it

works, prove its correctness, analyze the program’s

efﬁciency, and support the algorithm’s implementa-

tion.

However, (de Marneffe, 1998) observed that vi-

sualizations contribute little towards proving the cor-

rectness of the algorithm, analyzing the efﬁciency of

the program, and supporting the implementation of

the algorithm are key objectives.

Empirical studies indicate that combining anima-

tions with textual explanations enhances learning out-

comes. For instance, (Stasko et al., 1993) reported

that learners exposed to both forms performed bet-

ter than those who relied on text alone, and (Saltan,

2017) found higher achievement scores when an AV

tool was used compared to traditional methods. Fur-

thermore, (Hundhausen et al., 2002) discussed AV ap-

plications in lecture slides, laboratories, in-class dis-

cussions, and debugging scenarios.

Recent advances in data and information visu-

alization have enriched computer science education

by effectively bridging theory and practice (Roshdy

et al., 2018; Fırat and Laramee, 2018; Mulvey, 2015).

Innovations such as source-to-source transformation

for Constraint Handling Rules visualization and rule-

based systems for Java code annotation have further

enhanced the educational experience in speciﬁc do-

mains (Abdennadher and Sharaf, 2012a; Sharaf et al.,

2014a; Abdennadher and Sharaf, 2012b; Sharaf et al.,

2014b). Nonetheless, challenges remain in develop-

ing comprehensive tools that accommodate diverse

programming techniques and personalized learning

experiences (Rajala et al., 2008; Cornelissen et al.,

2010).

In contrast, CodeMotion gives users greater con-

trol by enabling them to trace and understand their

own code rather than relying on pre-deﬁned algo-

rithms. This approach, which includes extensive cus-

tomization and dynamic variable tracking, not only

aids debugging but also deepens users’ comprehen-

sion of underlying code mechanics.

3 CodeMotion

This section outlines the conceptual foundation, ar-

chitectural design, and technical implementation of

CodeMotion—a web-based program visualization

tool that leverages Abstract Syntax Trees (ASTs) to

offer an interactive, learner-centered understanding of

programming concepts. By integrating AST-based

analysis with modern web technologies and a ﬂexi-

ble visualization framework, CodeMotion addresses

common limitations in program visualization, includ-

ing security risks, limited structural insight, and nar-

row applicability.

3.1 Conceptual Approach

Traditional educational programming tools often

rely on direct code execution, instrumentation, or

language-speciﬁc conﬁgurations. Such approaches

can limit adaptability, introduce security concerns,

and provide only superﬁcial insights into program

behavior. In contrast, our methodology is built

on the principle that analyzing code as structured

data—rather than executing it directly—can yield a

more nuanced and comprehensive understanding of

code logic.

By parsing user-submitted JavaScript code into an

AST, CodeMotion derives a detailed, language-aware

representation of the program’s syntax and logic. This

structural analysis enables thorough examination of

control ﬂows, data manipulations, and algorithmic

steps while mitigating the security risks associated

with executing arbitrary code. Importantly, CodeMo-

tion is designed to be algorithm-agnostic: it does not

conﬁne users to a set of pre-deﬁned algorithms. In-

CSEDU 2025 - 17th International Conference on Computer Supported Education

566

stead, users are free to input and develop their own al-

gorithms, empowering them to trace and debug their

unique implementations.

Furthermore, although CodeMotion does not ex-

ecute code directly, it simulates code execution in a

manner akin to a compiler. Each line of code is pro-

cessed sequentially, with the effects of its execution

visualized in real time. The tool highlights the cur-

rently simulated line—mimicking the behavior of a

running compiler—to provide users with a clear, step-

by-step illustration of how their code operates.

In essence, the conceptual approach of CodeMo-

tion transforms raw code into a rich, interactive visu-

alization. By making the internal workings of code

transparent and accessible, the system lays a robust

foundation for scalable, extensible, and user-driven

programming education.

4 SYSTEM DESIGN AND

IMPLEMENTATION

CodeMotion is a modern web-based visualization tool

leveraging React and D3.js to create an interactive

environment for understanding JavaScript algorithms.

The design focused on four key objectives:

1. Security and Code Simulation: CodeMotion uses

the Acorn parser to convert JavaScript code into

Abstract Syntax Trees (ASTs), forming the basis

for a secure simulation of code execution. This

approach eliminates the risks of executing arbi-

trary code, provides ﬁne-grained control over sim-

ulation, enables step-by-step visualization of state

changes, and mimics a compiler by processing

each line sequentially while highlighting the cur-

rent execution step.

2. Real-Time Visualization: Integrating D3.js with

React, the system renders variables and arrays

as interactive visual elements. It offers smooth

animated transitions driven by state changes, a

dynamic canvas that adapts to evolving states,

and a control panel for customizing visual aes-

thetics (e.g., choosing among circles, squares, or

hexagons for variables, and selecting between bar

charts and card styles for arrays).

3. State Management and Component Architecture:

Utilizing React’s state management capabilities,

CodeMotion tracks variable values and execution

state while ensuring consistent data ﬂow between

components. This approach:

• Supports responsive user interface updates,

• Facilitates immediate visual feedback without

server roundtrips, and

• Enables ofﬂine functionality since code pro-

cessing occurs entirely on the client side.

4. Extensibility and Customization: The modular ar-

chitecture is designed to accommodate future en-

hancements and user-driven algorithm develop-

ment. The system supports:

• Various visualization formats for arrays and

variables,

• Customizable text sizes and visual properties,

• The easy integration of additional visualization

features and new algorithm types, and

• Expansion to support more complex language

constructs beyond basic JavaScript operations.

The system architecture separates concerns into

distinct components:

• Code Editor: Provides the interface for code in-

put.

• Parser Module: Converts JavaScript code into an

AST using Acorn.

• Execution Engine: Simulates code execution via

AST traversal, processing each node and updating

the simulation state.

• State Manager: Tracks variable values and pro-

gram execution state using React hooks.

• Visualization Canvas: Renders real-time visual

representations of the program state through

D3.js.

• Control Panel: Allows users to customize visual-

ization properties such as variable shapes, array

formats, and text sizes.

This modular design not only ensures maintain-

ability and responsiveness but also provides a solid

foundation for future enhancements. By leverag-

ing modern web technologies, CodeMotion provides

immediate feedback, supports ofﬂine operation, and

secures the simulation process by never executing

user code directly. The system supports fundamental

JavaScript constructs including:

• Variable declarations and assignments,

• Array operations and manipulations,

• Basic control ﬂow statements (if/else, switch

cases, loops), and

• Console output simulation.

Overall, this implementation focuses on provid-

ing clear, real-time visual feedback for algorithm exe-

cution, making it particularly suitable for educational

purposes and in-depth algorithm understanding. The

design also facilitates free algorithm development by

enabling users to trace and debug their own code, thus

enhancing both learning and debugging experiences.

Visual Intelligence for Program Animation

567

Figure 1: High-level architecture of CodeMotion.

4.1 Client-Side Visualization and

Interactivity

CodeMotion is built with D3.js and React. It trans-

forms program states into interactive, animated dis-

plays, providing real-time feedback during code sim-

ulation. Users can see changes in variables, arrays,

and control ﬂows, and customize visual properties via

an intuitive control panel to suit their preferences.

4.1.1 Variable and Array Visualization

CodeMotion represents program state using distinct

visual elements:

• Scalar Variables: Scalar variables are displayed

as geometric shapes (e.g., circles, squares, or

hexagons) with dynamic labels that update to re-

ﬂect current values. This visualization is de-

signed to help users understand variable states and

changes in real time. Figure 3 illustrates the con-

trol panel used for customizing their appearance.

• Arrays: Arrays are visualized as sequences of

connected elements, which can be styled in var-

ious formats such as bar charts or card layouts.

This ﬂexible representation helps in understand-

ing array structure and the effects of operations.

Figures 4 and 5 demonstrate two different visual-

ization styles for arrays.

The following code listing demonstrates how

D3.js updates scalar variable displays using smooth

transitions:

1 // Upd ate sc ala r v ar iab le s : set

po sit io n and u p da t e d is pl a ye d

val ues

2 const sc ala r = me rge d . fil ter (( d ) = >

!d . is Arr ay );

3 s cal ar . s e lec t ( ’ . shape - c on ta i ne r ’)

4 . t ra ns it i on ( t )

5 . at t r ( ’ t ra nsf or m ’ , ( d) = >

tr ans la te (${d . x} , ${ d. y }) )

6 . s t yle ( ’ fil l ’ , (d ) = > d . n ame ===

up da ted Va r ? ’# ff 6 666 ’ : ’ #

ccc ccc ’ );

7 s cal ar . s e lec t ( ’ t e xt ’)

8 . t ra ns it i on ( t )

9 . s t yle ( ’ font - s ize ’ , g et Fon tS iz e ()

)

10 . t w een ( ’ tex t ’ , function(d ) {

11 const t ex tEl em = d3 . se l ect (

this) ;

12 return () = > {

13 const v alS tr = d . v alu e

=== u nd e fi ne d ? ’

un def in ed ’ : d . va lue ;

14 te xtE le m . t ext ( ‘${ d. name }

= ${ v alS tr } ‘) ;

15 };

16 }) ;

Listing 1: D3.js code snippet for updating scalar variable

visualization.

The following listing shows how array visualiza-

tion is implemented, with element widths adjusted

based on the array length:

1 // Upd ate ar ray vi su al iz at io n :

adj ust el em ent wi d th b ased on

CSEDU 2025 - 17th International Conference on Computer Supported Education

568

Figure 2: Example of scalar variable visualization using circles.

Figure 3: Control panel interface for customizing scalar variable visualizations.

arr ay l eng th

2 const ar ray s = me rge d . fil ter (( d ) = >

d. i sAr ray ) ;

3 a rra ys . s e lec t ( ’ re ct . array - bkg ’)

4 . t ra ns it i on ( t )

5 . at t r ( ’ wi dth ’ , ( d ) => {

6 const arr = d . val ue || [] ;

7 return arr . l e ngt h * 50 + 20;

8 })

9 . s t yle ( ’ fil l ’ , (d ) = > d . n ame ===

up da ted Va r ? ’# ff 9 999 ’ : ’ #

ccc ccc ’ );

Listing 2: D3.js code for array visualization.

4.1.2 Real-Time Array Operations and

Animations

Array modiﬁcations are animated to illustrate dy-

namic changes clearly. For example, when an ele-

ment moves during a sorting operation, the system

creates a cloning effect that emphasizes the transition.

This animation provides enhanced clarity during state

changes.

4.1.3 Algorithm-Agnostic Simulation and

Real-Time Execution Tracing

CodeMotion is designed to be algorithm-agnostic. Its

modular architecture allows it to simulate and visual-

ize a wide variety of algorithms without hardcoding

logic for each one. The system interprets algorithm

Figure 4: Array visualization using a bar chart style for

[5,2,6,2].

steps through state updates that are directly mapped

to visual transitions.

CodeMotion simulates code execution similarly

to a compiler, processing each line sequentially and

highlighting the current line in real time. This syn-

chronized highlighting, along with dynamic updates

Visual Intelligence for Program Animation

569

Figure 5: Array visualization using card styling for

[5,2,6,2].

Figure 6: Animation during element transition showing a

cloning effect for arr[0]=arr[3].

to the program state, enables users to trace algorithm

execution and monitor changes in variables, arrays,

and control ﬂow. Figure 7 illustrates a sorting algo-

rithm simulation showing element swaps and compar-

isons.

Dynamic State Management. The integration of

D3.js with React ensures that any change in the pro-

gram state is immediately reﬂected in the visualiza-

tion. This real-time synchronization guarantees that

users receive continuous, interactive feedback as the

algorithm progresses.

4.1.4 Animation and Transitions

To enhance user engagement, CodeMotion imple-

ments smooth transition effects for all state changes.

These include:

• Variable Updates: Fade and transform animations

when variable values change.

• Array Modiﬁcations: Sliding and reordering ani-

mations for array elements.

• Shape Changes: Smooth morphing effects when

variable representations change shape.

• Color Transitions: Gradual color shifts to empha-

size updates.

These effects provide immediate visual feedback

and contribute to an intuitive understanding of the un-

derlying code behavior. Overall, the combination of

D3.js’s visualization capabilities with React’s respon-

sive component model creates an engaging environ-

ment for exploring and tracing algorithm execution.

5 EMPIRICAL TESTING

This section presents the evaluation of CodeMotion’s

usability and educational impact using both quantita-

tive and qualitative measures.

5.1 Evaluation Procedure and

Participants

A group of 29 computer science students from The

German International University (GIU) participated

in the evaluation. The sample included both freshmen

undergraduates and senior graduate students, ensur-

ing a diverse range of programming experience and

algorithmic knowledge. Prior to testing, participants

were briefed on CodeMotion’s purpose and operation.

They then explored two scenarios—a sorting algo-

rithm and a conditional logic case—to interact with

the interface and assess the clarity of the visualiza-

tions.

5.2 Usability Assessment with SUS

The System Usability Scale (SUS) questionnaire was

administered after the tasks. The SUS is a widely

recognized instrument that yields an overall usability

score on a 100-point scale.

CSEDU 2025 - 17th International Conference on Computer Supported Education

570

Figure 7: Sorting algorithm simulation with visualized swaps and comparisons.

5.3 SUS Results and Analysis

The SUS analysis yielded a mean score of 76.98, a

median of 80, and a standard deviation of 15.35, in-

dicating that CodeMotion is generally well-received

and considered to have “good” usability.

Table 1: Summary of SUS Scores.

Statistic Value

Mean 76.98

Median 80

Standard Deviation 15.35

5.4 Insights and Qualitative Feedback

Participants found CodeMotion easy to learn. On av-

erage, they werr able to become comfortable with its

features within 10-15 minutes. Users expressed how

the system could encourage experimentation with var-

ious algorithms. Users also highlighted that the in-

teractive visuals explained the complex algorithmic

processes. One user noted, ”The interactive visu-

als helped me understand the logic behind sorting.

I could clearly see each comparison and swap.” An-

other added, ”It felt like I was ‘watching’ the algo-

rithm think, which made it easier to understand why

certain steps happened.”

6 CONCLUSION AND FUTURE

WORK

CodeMotion allows users to trace and debug their pro-

grams interactively, providing a dynamic alternative

to static pre-deﬁned algorithms. Its real-time simula-

tion highlights active code, mimicking compiler be-

havior and enhancing understanding through clear vi-

sual feedback. Tested with 29 computer science stu-

dents at The German International University, Code-

Motion was found to be intuitive and engaging, scor-

ing a mean SUS of 76.98. It effectively clariﬁes algo-

rithmic concepts and allows for customization. How-

ever, improvements are needed in scalability, device

compatibility, and adaptive guidance to accommodate

users of varying expertise levels.

Future updates to CodeMotion should focus on

supporting diverse data structures. Real-time code

editing and debugging should be also added to en-

hance interactivity. Adaptive difﬁculty levels and per-

sonalized guidance for users are also being consid-

ered.

REFERENCES

Abdennadher, S. and Sharaf, N. (2012a). Visualization

of chr through source-to-source transformation. In

Technical Communications of the 28th International

Conference on Logic Programming (ICLP’12)(2012).

Schloss-Dagstuhl-Leibniz Zentrum f

ur Informatik.

Visual Intelligence for Program Animation

571

Abdennadher, S. and Sharaf, N. (2012b). Visualization of

CHR through Source-to-Source Transformation. In

Dovier, A. and Costa, V. S., editors, ICLP (Technical

Communications), volume 17 of LIPIcs, pages 109–

118. Schloss Dagstuhl - Leibniz-Zentrum fuer Infor-

matik.

Cornelissen, B., Zaidman, A., and van Deursen, A. (2010).

A controlled experiment for program comprehension

through trace visualization. IEEE Transactions on

Software Engineering, 37(3):341–355.

de Marneffe, P. (1998). The problem of examination ques-

tions in algorithmics. In Davies, G. and

OHigeartaigh,

M., editors, Proceedings of the 6th Annual Conference

on the Teaching of Computing and the 3rd Annual

SIGCSE Conference on Innovation and Technology in

Computer Science Education, ITiCSE 1998, Dublin

City Univ., Ireland, 18-21 August 1998, pages 74–76.

ACM.

Fırat, E. E. and Laramee, R. S. (2018). Towards a survey

of interactive visualization for education. Computer

Graphics and Visual Computing, CGVC 2018, pages

91–101.

Fortner, B. (1995). The data handbook - a guide to under-

standing the organization and visualization of techni-

cal data (2. ed.). Springer.

Hundhausen, C., Douglas, S., and Stasko, J. (2002).

A Meta-Study of Algorithm Visualization Effective-

ness. Journal of Visual Languages & Computing,

13(3):259–290.

Keller, P. R. and Keller, M. M. (1993). Visual cues - practi-

cal data visualization. IEEE.

Kerren, A. and Stasko, J. (2002). Chapter 1 Algorithm An-

imation. In Diehl, S., editor, Software Visualization,

volume 2269 of Lecture Notes in Computer Science,

pages 1–15. Springer Berlin / Heidelberg.

Lewis, J. R. (2018). The system usability scale: past,

present, and future. International Journal of Human–

Computer Interaction, 34(7):577–590.

Mulvey, M. (2015). Effects of visualization on algorithm

comprehension. Master’s thesis, The University of

Wisconsin-Milwaukee.

Naps, T. L., Eagan, J., and Norton, L. L. (2000). Jhav

e - an

environment to actively engage students in web-based

algorithm visualizations. In Cassel, L. B., Dale, N. B.,

Walker, H. M., and Haller, S. M., editors, Proceedings

of the 31st SIGCSE Technical Symposium on Com-

puter Science Education, 2000, Austin, Texas, USA,

March 7-12, 2000, pages 109–113. ACM.

Rajala, T., Laakso, M.-J., Kaila, E., and Salakoski, T.

(2008). Effectiveness of program visualization: A

case study with the ville tool. Journal of Information

Technology Education. Innovations in Practice, 7:15.

Repenning, A. and Sumner, T. (1995). Agentsheets:

A medium for creating domain-oriented languages.

IEEE Computer, 28(3):17–25.

Roshdy, A., Sharaf, N., Saad, M., and Abdennadher, S.

(2018). Generic data visualization platform. In

Banissi, E., Francese, R., Bannatyne, M. W. M.,

Wyeld, T. G., Sarfraz, M., Pires, J. M., Ursyn,

A., Bouali, F., Datia, N., Venturini, G., Polese, G.,

Deufemia, V., Mascio, T. D., Temperini, M., Sciar-

rone, F., Malandrino, D., Zaccagnino, R., D

ıaz, P.,

Papadopoulo, F., Anta, A. F., Cuzzocrea, A., Risi,

M., Erra, U., and Rossano, V., editors, 22nd Interna-

tional Conference Information Visualisation, IV 2018,

Fisciano, Italy, July 10-13, 2018, pages 56–57. IEEE

Computer Society.

Saltan, F. (2017). The impact of online algorithm visual-

ization on ict students’ achievements in introduction

to programming course. Journal of Education and

Learning, 6(1):184–192.

Saraiya, P., Shaffer, C. A., McCrickard, D. S., and North, C.

(2004). Effective features of algorithm visualizations.

In Joyce, D. T., Knox, D., Dann, W., and Naps, T. L.,

editors, Proceedings of the 35th SIGCSE Technical

Symposium on Computer Science Education, SIGCSE

2004, Norfolk, Virginia, USA, March 3-7, 2004, pages

382–386. ACM.

Sharaf, N., Abdennadher, S., and Fr

uhwirth, T. (2014a).

Chranimation: An animation tool for constraint han-

dling rules. In International Symposium on Logic-

Based Program Synthesis and Transformation, pages

92–110. Springer.

Sharaf, N., Abdennadher, S., and Fr

uhwirth, T. (2014b). Vi-

sualization of constraint handling rules. arXiv preprint

arXiv:1405.3793.

Stasko, J. T., Badre, A. N., and Lewis, C. (1993). Do algo-

rithm animations assist learning?: an empirical study

and analysis. In Arnold, B., van der Veer, G. C., and

White, T. N., editors, Human-Computer Interaction,

INTERACT ’93, IFIP TC13 International Conference

on Human-Computer Interaction, 24-29 April 1993,

Amsterdam, The Netherlands, jointly organised with

ACM Conference on Human Aspects in Computing

Systems CHI’93, pages 61–66. ACM.

CSEDU 2025 - 17th International Conference on Computer Supported Education

572