Inherent Advantages of Augmented Reality for K–12 Education

Marc Ericson C. Santos, Angie Chen, Takafumi Taketomi,

Goshiro Yamamoto, Jun Miyazaki, Hirokazu Kato Interactive Media Design Laboratory

Graduate School of Information Science

Nara Institute of Science and Technology

Nara, Japan

{chavez-s, chen-a, takafumi-t, goshiro, miyazaki, kato}@is.naist.jp

A Review of Augmented Reality Learning Experiences

Presentation has three parts.

1. Report a survey of Augmented Reality Learning Experiences (ARLEs.)

– Objectives

– Method

– Results

2. Propose a participatory design model for ARLEs.

3. Illustrate the use of participatory design.

– Developing AR x-ray interaction

– Paper-prototyping for authoring AR content

Defining AR learning experiences imposes logical uses in education.

Augmented Reality Learning Experience (ARLE)

“any form of personal experience designed to improve knowledge, facilitated by integrating virtual elements onto a real environment.”

Augmented Reality Interactions

Necessary? Beneficial?

Cost-effective?

Educational Interaction Actual Classroom Use*

*Picture from http://newsinfo.inquirer.net/194569/public-schools-to-give-priority-to-early-enrollees

Conduct a review to understand AR interactions in education.

1. To find empirical proof that AR interactions can be beneficial to learning

2. To observe trends in implementation and evaluation that led to good interfaces

Approach for the survey

Search digital libraries

Filter using criteria

Gather data

Analysis and synthesis

IEEE digital library ACM digital library Learning technology publications

Prototype K-12 Education Availability

publication details prototype description use of AR user study

We found 87 ARLE articles.

15

72

Type of Publication

Journal

Conference

61

26

Is it IEEE-Indexed?

Yes

No

0 2 4 6 8 10 12 14 16

IEEE Advanced Learning Technologies

IEEE Int'l Symp. on Mixed and AugmentedReality

IEEE Virtual Reality

IEEE Wireless, Mobile and UbiquitousTechnology in Education

Computers and Education

Number of Articles

There are five types of evaluation methods.

1. Performance metrics

– Performance test

– Self-reported learning

2. Questionnaires

– Motivation

– Presence

– Usability

3. Behavior observation

4. Focused-group discussion

5. Heuristic evaluation

– Teacher evaluation

– Expert evaluation

11

37

39

User Study

Performance

Usability

None

Ref Content Display Participants (Sample) Effect Size

[3] English Handheld Grade school, teachers (67) Large

[5] Solar system HMD High school (40) Large

[6] Kinematics graphs Desktop High school (80) Large

[7] Elastic collision Handheld University students (36) Large

[4] Spatial ability training Desktop University students (49) Moderate to Large

[2] Masterpieces of

Renaissance Desktop High school (69) Moderate to Large

[8] English Desktop Grade school (Six classes) Small to Moderate

[9] Math game Handheld Grade school (123) No effect

[10] Eulerian and Hamilton

Graphs Projector University students (20) No effect

[11] Spatial ability training HMD High school (215) No effect

[12] Library skills Desktop Grade school (116) Small Negative

Reported effect sizes vary widely.

Multimedia material* Teacher’s words + Real AR Annotation

Multimedia Learning Theory [13] (Learning from words and pictures)

AR Annotation (Learning from virtual words

and real objects)

If we substitute: Words virtual symbols (words, numbers, arrows) Pictures real objects;

Then, all of the empirically tested principles of Multimedia Learning Theory applies to AR annotation.

ARLEs (AR annotation) apply multimedia learning theory.

*Picture from http://science.howstuffworks.com/environmental/earth/geophysics/earth3.htm

Virtual apple

Virtual apple + Picture book*

Virtual apple + Real Fridge

More cues

Situated Learning Theory [14] Learning is situated in a specific context and embedded within a particular social and physical environment.

ARLEs apply situated visualization.

*Picture from http://lifeonthecutoff.wordpress.com/2010/09/18/kerplunk/

Vision but poor haptic Vision and better haptic [4]

Animate Vision Theory [15] Vision data and haptic data are coded in the brain together.

ARLEs apply vision-haptic visualization.

Easier perception, thus more allotment for processing

Processing

Processing

Perception

Perception

Working Memory

AR

N

on

-AR

More cues, better experience, thus more elaborated

knowledge

Some Concept

Some Concept

No

n-A

R

AR

Knowledge as associations

World annotation, situated visualization and vision-haptic visualization improves learning in two ways.

Given the affordances of AR, how can we design ARLEs?

AR and

EdTech

Engineers Teachers

and

Curriculum

Designers

Students

AR

Learning

Object

Implementation Evaluation of

learning

Teaching

Requirements

Delivery to

students

Edit to match

context

Expert

Evaluation

We propose a participatory design model for authoring ARLEs.

Target

Object

Edges

Mask

Alpha

Mask

Virtual

Object

Edge-based

AR X-ray

We tested an implementation of AR x-ray.

AR x-ray on iPad

We conducted two user studies on perception.

1. User Study 1: 23 Filipino students (9 male, 14 female, aged 5-15) attending 13 different schools

2. User Study 2: 47 students (21 male, 26 female, aged 11-16) from Spring Christian School

3. Questionnaire:

– Q1. The object is inside the box.

– Q2. The object is easy to see.

– Q3. The object seems real.

– Q4. The object seems flat.

– Q5. I can see the different parts of the object clearly.

– Q6. My classmates will say the object is real.

User study set up

Control: without occlusion

Experiment: with occlusion

Demonstration

Set up for User Study 1 (involving younger kids)

There are no significant differences in perception.

Question (Construct) Without Occlusion With Occlusion (x-ray)

Mean (95% Conf.)

STDV Mean

(95% Conf.) STDV

Q1 (Depth) 3.7 (3.1 – 4.2) 1.2 3.6 (3.1 – 4.1) 1.3 Q2 (Visibility) 4.0 (3.5 – 4.4) 1.0 3.3 (2.8 – 3.7) 1.2 Q3 (Realism) 3.4 (2.8 – 3.9) 1.4 2.7 (2.2 – 3.3) 1.3 Q4 (Depth) 4.1 (3.7 – 4.6) 0.9 4.0 (3.5 – 4.4) 1.2 Q5 (Visibility) 4.0 (3.6 – 4.5) 0.9 3.6 (3.1 – 4.1) 1.3 Q6 (Realism) 3.3 (2.8 – 3.8) 1.2 3.1 (2.6 – 3.7) 1.3

Question (Construct) Without Occlusion With Occlusion (x-ray)

Mean (95% Conf.)

STDV Mean

(95% Conf.) STDV

Q1 (Depth) 3.4 (2.9 – 3.9) 1.2 3.4 (3.0 – 3.9) 1.2 Q2 (Visibility) 3.8 (3.3 – 4.3) 1.1 4.0 (3.5 – 4.4) 1.1 Q3 (Realism) 3.1 (2.6 – 3.7) 1.2 3.0 (2.5 – 3.5) 1.3 Q4 (Depth) 3.3 (2.9 – 3.7) 1.0 3.3 (2.9 – 3.7) 1.0 Q5 (Visibility) 4.0 (3.5 – 4.5) 0.6 3.7 (3.2 – 4.1) 1.0 Q6 (Realism) 3.4 (3.0 – 3.9) 1.0 3.7 (3.4 – 4.2) 1.0

User Study 1

User Study 2

(p-value = 0.041) Significant based on t-test.

Schools find ARLEs beneficial and would like to use it for specific content.

Focused group discussions with Teachers, Parents,

School Administrators

Perceived advantages in practice 1. “Learning by experience” 2. Improved attention 3. Increased motivation

Perceived issues in practice 1. Cost and availability of technology 2. Accuracy of virtual information 3. Time constraint

On-going paper-prototyping for ARLEs.

Target Object

Authoring on target object

Drop-down Menu

Annotated frog

• Can test knowledge in navigation

• Limited interactions and experience

Summary of contributions

We showed that:

• ARLEs have variable effect to learning.

• ARLEs have inherent advantages that improve perception and knowledge construction.

We recommend that:

• We can design better ARLEs using participatory design.

Using participatory design, we…

• Tested perception in AR x-ray interactions

• Gathered preliminary user requirements for ARLEs

Future Work

Simple Authoring Tool for Teachers

Implement Augmented Reality Learning Objects

(ARLOs)

Standard ARLE file that packages AR content (real object, virtual

objects, interactions)

Main Goal

Continue using Participatory Design

Continue implementing

state-of-the-art AR interactions

Example: AR x-ray, AR Annotation

Model that considers teachers and students in

the authoring process

1. “Advanced Learning Technologies Journal List,” CELSTEC, http://celstec.org/content/advanced-learning-technologies-journal-list. Aug. 2012.

2. A. Di Serio, M. B. Ibáñez and C. D. Kloos, "Impact of an Augmented Reality System on Students’ Motivation for a Visual Art Course," Computers & Education, 2012.

3. T. Y. Liu, "A context‐aware ubiquitous learning environment for language listening and speaking," Journal of Computer Assisted Learning, vol. 25, no. 6, pp. 515-527, 2009.

4. J. Martín-Gutiérrez, J. L. Saorin, M. Contero, M. Alcañiz, D. C. Pérez-López and M. Ortega. "Design and validation of an augmented book for spatial abilities development in engineering students," Computers & Graphics, vol. 34, no. 1, pp. 77-91, 2010.

5. A. K. Sin and H. B. Zaman. "Live Solar System (LSS): Evaluation of an Augmented Reality book-based educational tool," Proc. IEEE Symp. Information Technology (ITSim ‘10), vol. 1, pp. 1-6. 2010.

6. T. F. L. Jerry and C. C. E. Aaron. "The impact of augmented reality software with inquiry-based learning on students' learning of kinematics graph," Proc. IEEE Conf. Education Technology and Computer (ICETC ‘10), vol. 2, pp. V2-1. 2010.

7. N. Li, L. Chang, Y. X. Gu and H. B. L. Duh. "Influences of AR-Supported Simulation on Learning Effectiveness in Face-to-face Collaborative Learning for Physics," Proc. IEEE Conf. Advanced Learning Technologies (ICALT ‘11), pp. 320-322. 2011.

8. H. K. Jee, S. Lim, J. Youn and J. Lee, "An augmented reality-based authoring tool for E-learning applications," Multimedia Tools and Applications, pp. 1-11, 2011.

9. P. O'Shea, C. Dede and R. Mitchell, "A Study of the efficacy of an Augmented Reality curriculum," Proc. AACE Conf. Information Technology & Teacher Education (SITE ’09), pp. 1510-1514, 2009.

10.H. Mitsuhara, Y. Yano and T. Moriyama, "Paper-top interface for supporting note-taking and its preliminary experiment," Proc. IEEE Conf. Systems, Man and Cybernetics (SMC ‘10), pp. 3456-3462, 2010.

11.A. Dunser, K. Steinbugl, H. Kaufmann and J. Gluck, "Virtual and augmented reality as spatial ability training tools," Proc. ACM SIGCHI New Zealand Conf. Computer-human interaction: design centered HCI (CHINZ '06), vol. 158, pp. 125-132. 2006.

12.C. M. Chen and Y. N. Tsai, "Interactive augmented reality system for enhancing library instruction in elementary schools," Computers & Education, 2012.

13.Mayer, Richard. Multimedia Learning 2nd ed. Cambridge, 2009. 14.Lave, Jean, and Etienne Wenger. Situated learning: Legitimate peripheral participation. Cambridge university press,

1991. 15.Ballard, Dana H. "Animate vision." Artificial intelligence 48.1 (1991): 57-86.

References