Spatial Visualization Ability and Impact of Drafting Models: A Quasi Experimental Study

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Spring 2014

Spatial Visualization Ability and Impact of Drafting Models: A Spatial Visualization Ability and Impact of Drafting Models: A

Quasi Experimental Study Quasi Experimental Study

Petros J. Katsioloudis

Old Dominion University

Vukica Jovanovic

Old Dominion University

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Original Publication Citation Original Publication Citation

Katsioloudis, P. J., & Jovanovic, V. (2014). Spatial visualization ability and impact of drafting models: A

quasi experimental study.

Engineering Design Graphics Journal, 78

(2), 1-11.

This Article is brought to you for free and open access by the STEM Education & Professional Studies at ODU

Digital Commons. It has been accepted for inclusion in STEMPS Faculty Publications by an authorized

administrator of ODU Digital Commons. For more information, please contact [email protected].

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Spatial Visualization Ability and Impact of Drafting Models:

A Quasi Experimental Study

Petros J. Katsioloudis

Old Dominion University

Vukica Jovanovic

Old Dominion University

Abstract

A quasi experimental study was done to determine significant positive effects among three different types

of visual models and to identify whether any individual type or combination contributed towards a positive

increase of spatial visualization ability for students in engineering technology courses. In particular, the

study compared the use of different visual models - a 3D printed solid object, a 3D computer generated

drawing and a 2D drawing.

Introduction

It is recognized that the ability to visualize is an important tool required of engineers in

order to function effectively (Deno, 1995; Miller, 1990; Pleck, 1991; Sorby & Baartmans,

2000). More specific, visualization of problems is critical for success in engineering

education (Sorby & Baartmans, 2000), and for that reason spatial abilities have been

used as a predictor of success in several engineering and technology disciplines

(Strong & Smith, 2001).

However, these abilities are not determined genetically, but rather a result of a long

learning process. It has been shown by several studies that some type of intervention,

whether a short course or a semester long course, can improve spatial abilities in

students who score low on tests in this area (Hsi, Linn, & Bell, 1997; Martín-Dorta,

Saorín, & Contero, 2008; Sorby, 2001). For this study, the following was the primary

research question. Is there a difference between the impact of model type (2D drawing,

3D computer generated drawing, 3D printed object) on spatial visualization ability? The

following hypotheses will be analyzed to attempt to find a solution to the research

question. The hypotheses that guided this study were:

: There will be no difference in spatial visualization ability between the impact of

model type (2D drawing, 3D computer generated drawing, 3D printed object).

: There will be significant difference in spatial visualization ability between the impact

of model type (2D drawing, 3D computer generated drawing, 3D printed object).

Review of Literature

According to Piagetian theory, an individual acquires spatial visualization ability through

three distinct stages of development (Bishop, 1978). During the first stage, children

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acquire topological spatial visualization skills with the ability to discern an object’s

topological relationship with other objects. During the second stage of development,

projective representation is acquired and children can conceive what an object will look

like from a different perspective. At the third stage of spatial visualization development,

the individual learns to combine projective abilities with the concept of measurement.

Due to the reduced amount of instructional time given for engineering graphics content

in many engineering and technology programs, faculty have expressed concern that

students’ ability to visualize 3D parts from 2D drawings is not being developed as well

as in the past (Branoff, T. J. & M. Dobelis, 2013; Branoff, 2007; Clark & Scales, 2000;

Meyers, 2000). To measure an individual’s spatial ability, a plethora of standardized

tests are available.

The most commonly used tests include:

a) The Purdue Spatial Visualization Test: Rotations (PSVT:R), devised to test a

person’s ability at the second stage of development (Sorby, 2005).

b) The Mental Rotation Test (MRT) a test designed to assess a person’s ability to

visualize rotated solids (Sorby, 2005).

c) The Differential Aptitude Test: Space Relations (DAT:SR) consists of 50 items

and with a role to test spatial ability (Monahan, Harke and Shelley, 2008).

d) The Mental Cutting Test (MCT) that requires individuals to create a split view of

an object; therefore, forcing to visualize and choose the correct cross-section

among five alternatives (Tsunumi, 2004).

Several studies have been conducted to examine the usefulness of an engineering

graphics literacy test (Branoff & Dobelis, 2012a, 2012b, 2012c; T. J. Branoff & M.

Dobelis, 2013) and some of them have proven to be great predictors of an individual’s

ability to visualize (Kelly, Clark, & Branoff, 2013).Some of the factors that have been

identified by various graphics education researchers are spatial visualization, spatial

relations, spatial orientation, spatial cognition, spatial intelligence, spatial ability, and

visualization (Hartman & Bertoline, 2005; Martin- Dorta, Saorin, & Contero, 2008; Miller

& Bertoline, 1991; Sorby, 1999a).

According to Bodner and Guay (1997) two factors emerged from spatial ability

research: spatial orientation, which involves not being puzzled by changes in visual

inputs, and spatial visualization, which involves the ability to manage visual input

components (Kelly, 2012). Eliot and Smith (1983) showed factors, such as spatial

relations, in the context of mental rotation of objects, spatial orientation as the

understanding of how an object would appear from a different perspective, and

visualization from a surface development context (Kelly, 2012) According to Juhel

(1991) the focus is on three factors: spatial orientation, which determines how an object

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will appear from a different position; spatial visualization, which involves the mental

transformation of an object; and speeded rotation, which is the mental rotation of

objects (Kelly, 2012).

In recent years, 3D spatial abilities have received much attention. Several studies have

involved different interfaces to attempt to manipulate a person's understanding of 3D

space (Carriker, 2009).Cockburn (2004) asked whether or not a person would have a

better spatial memory if they were given a 3D representation of the object's location. For

the specific study, the user is not allowed to move; it is only a simple comparison of

perspective effects in the displays (Carriker, 2009). Cockburn (2004) also added visual

cues that gave the illusion of a 3D object, including shadows, lighting and size, to see if

individuals could recall the 3D objects better than their 2D counterparts. He found that

there were no significant differences between the averages of the 2D and 3D conditions.

Authors, Tan, Gergle, Scupelli, & Pausch (2004) performed a study that was designed

to examine the effects of physical display size on an individual’s cognitive strategy and

performance on an interactive 3D navigation task (Carriker, 2009). Comparable to the

prior study by Cockburn, Tan et al. attempted to analyze 3D spatial ability using different

displays. However, they also addressed whether that performance is directly affected by

the task being interactive or not. Tan et al. (2004) attempted to examine not only the

implications of the display, but the effect on the subject when allowed different means of

interaction with the 3D world.

In addition, several researchers have suggested that spatial ability can be enhanced

and taught by some instructional designs (Alias, Black, & Gray, 2002; Kwon, 2003;

Lajoie, 2003; Potter & Merwe, 2001; Woolf, Romoser, Bergeron, & Fisher, 2003). Many

works demonstrated that instructions using computer-based 3D visualizations can

provide learners with adequate spatial experiences for developing their spatial ability

(Kwon, 2003; Woolf et al., 2003). However, few empirical studies have established the

causal relationships in greater depth (Wang, Chang & Li, 2006).Moreover; few studies

have explored the effects of two-dimensional (2D) versus three dimensional (3D) media

representations on the influence of the spatial ability of undergraduate students (Wang,

Chang & Li, 2006).

Based on this research, it is clear that changing the software or hardware has a high

correlation to a student's understanding of 3D space. This encourages future research

to find the most efficient tools to improve 3D spatial visualization ability for all students.

Methodology

A quasi-experimental study was selected as a means to perform the comparative

analysis of spatial visualization ability during the fall semester of 2013. The study was

conducted in an engineering graphics course, MET 120 (Computer Aided Drafting),

offered at Old Dominion University as a part of the Engineering Technology program.

The participants from the study are shown in Table 1. From the 54 students, 12 were

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females and 18 were African American and using a convenience sample there was a

near equal distribution of the participants between the three groups.

Table 1. Research Design Methodology

Group 1

n1=20

MCT

Sketch from 2D drawing

Group 2

n2=16

MCT

Sketch from 3D image

Group 3

n3=18

MCT

Sketch from 3D object

The engineering graphics course emphasized “hands on” practice using 2-D and 3D

AutoCAD software in the computer lab, along with the various methods of editing,

manipulation, visualization and presentation of technical drawings. In addition, the

course included the basic principles of engineering drawing/hand sketching, dimensions

and tolerance principles.

The students attending the course during the fall semester of 2013 were divided in to

three groups according to the section of the course that they chose to participate the

semester prior to the study. The three groups (n1=20, n2= 16 and n3=18 with an overall

population of N = 54) were presented with a visual representation of an object (drafting

model) and were asked to create a sectional view. The first group (n1) received a 2D

drawing of the cone (see Figure.1), the second group (n2) received a 3D PC generated

image of the cone (see Figure. 2) and the third group (n3) received a 3D printed cone

using a 3D rapid prototyping machine (see Figure. 3).

Figure1. 2D Drawing Figure 2. 3D Computer Figure 3. 3D Printed Object Using

Generated Drawing Additive Technology

In addition, all groups were asked to complete the MCT instrument 2 days prior to the

completion of the sectional view drawing to identify level of visual ability and show

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equality between the three groups. According to Nemeth and Hoffman (2006) the MCT

has been widely used in all age groups. The “standard MCT” consists of 25 problems.

The Mental Cutting Test (hereafter MCT), a sub-set of the CEEB Special Aptitude Test

in Spatial Relations has been used by Suzuki et al.to measure spatial abilities in

relation to graphics curricula (Tsunumi, 2004).

In each problem, subjects are given a perspective drawing of a test solid, which is to be

cut with a hypothetical cutting plane. Subjects are then asked to choose one correct

cross section from among 5 alternatives. There are two categories of problems in the

test (Tsutsumi, 2004). Those of the first category are called `pattern recognition

problems', in which the correct answer is determined by identifying only the pattern of

the section. The other are called `quantity problems' or `dimension specification

problems', in which the correct answer is determined by identifying not only the correct

pattern but also the quantity in the section, e.g., the length of the edges or the angles

between the edges (Tsutsumi, 2004).

Upon completion of the MCT the instructor of the course placed the 2D drawing, 3D

computer generated image and 3D printed object in a central location in the classroom

(the three groups were positioned in to three different rooms) and asked the students to

create a sectional view of the cone. The engineering drawing that was used in this

research was a sectional view of a cone which had different levels of different materials.

These levels had different colors. Sectional views are very useful engineering graphics

tool, especially for parts that have complex interior geometry. Sections are used to

clarify the interior construction of a part that cannot be clearly described by hidden lines

in exterior views (Plantenberg 2013). By taking an imaginary cut through the object and

removing a portion, the inside features could be seen more clearly. Students had to

mentally discard the unwanted portion of the part and draw the remaining part. The

rubric used included the following parts: 1) use of section view labels; 2) use of correct

hatching style for cut materials; 3) accurate indication of cutting plane; 4) appropriate

use of cutting plane lines; and 5) appropriate drawing of omitted hidden features.

Maximum score for the drawing was 6 points.

Data Analysis

Analysis of MCT Scores

The first method of data collection involved the completion of the MCT instrument prior

to the treatment to show equality of spatial ability between the three different groups.

The researchers graded the MCT instrument as described in the guidelines of the MCT

creators. A standard paper-pencil MCT was conducted, in which the subjects were

instructed to draw intersecting lines on the surface of a test solid with a green pencil

before selecting alternatives. The maximum score that can be received on the MCT is

25 and as it can be seen in Table 2, n1 had a mean of 21.47, n2 had a mean of 19.76

and n3 had a mean of 21.37. There was no significant difference between the three

groups as far as spatial ability as measured by the MCT instrument.

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Table 2. MCT Descriptive Results

95% Confidence Interval for Mean

Mean

Std Error

Lower Bound

Upper Bound

21.471

8.02

3.213

13.629

17.513

3D PC

19.766

6.121

2.096

14.169

19.164

3D Solid

21.314

6.945

2.390

18.049

21.379

Total

21.85

6.87

2.56

16.28

19.352

Analysis of Drawing

The second method of data collection involved the creation of a sectional view drawing.

As shown in Table 3, the group that used the 2D drawing as visual aid (n =16) had a

mean observation score of 4.06. The groups that used the 3D computer generated

visual (n = 20) and the 3D printed solid cone (n = 18) had higher scores of 5.87 and

5.12 respectively. A one-way ANOVA was run to compare the mean scores for

significant differences among the three groups. The result of the ANOVA test, as shown

in Table 4, was significant, F(2, 52) = 14.54, p < 0.01. The data was dissected further

through the use of a post hoc Tukey’s honest significant difference (HSD) test. As it can

be seen in Table 5, the post hoc analysis shows a statistically significant difference

between the 3D Solid vs. 3D PC (p < 0.001, d = 2.08) and the 3D Solid vs. 2D (p =

0.008, d = 1.54), with 3D Solid vs. 2D being significantly lower in both cases.

Table 3. Sectional View Drawing Descriptive Results

95% Confidence Interval for Mean

Mean

Std Error

Lower Bound

Upper Bound

4.061

.2672

.0724

3.917

4.235

3D PC

5.876

1.287

.3075

5.209

6.424

3D Solid

5.122

.8492

.2547

4.594

5.790

Total

5.036

1.163

.1859

4.691

5.301

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Table 4. Sectional View Drawing ANOVA Results

Quiz

Between Groups

13.531

11.765

14.536

< 0.001*

Within Groups

22.376

.809

Total

55.907

* Denotes statistical significance

Table 5. Sectional View Drawing Tukey HSD Results

Visual Aids (1 vs. 2)

Mean Diff. (1-2)

Std. Error

3D Solid vs. 3D PC

-1.80

0.334

< 0.001*

3D Solid vs. 2D

-1.07

0.340

0.008*

3D PC vs. 2D

0.724

0.334

0.090

* Denotes statistical significance

Discussion

This study was done to determine significant positive effects among three different

types of visual models and to identify whether any individual type or combination

contributed towards a positive increase of spatial visualization ability for students in

engineering technology courses. In particular, the study compared the use of different

visual models- a 3D printed solid object, a 3D computer generated drawing and a 2D

drawing. It was found that the 3D printed solid model and 3D computer generated

image both provided statistically significant higher scores than the 2D drawing. While

not statistically significant, the students who received treatment via the 3D printed solid

model outperformed their peers who received treatment from the other two models in

the drawing. This could indicate that students were better able to comprehend visual

data given from 3D solid models, over 3D computer generated models or 2D drawings.

It should be noted that the majority of visual models used in the past and today are 2D

drawings, asking the students to recreate different views. Using 3D solid models as

visualizations aids for engineering graphics courses has great potential. With the current

status of additive technologies instructors have the ability to design and built almost any

model in a very short time frame. However, potential issues include: a) availability of 3D

printers at all institutions and b) it appears that more research is needed utilizing

populations with different background. This small quasi experimental study provided

results contrary to the commonly used method of 2D visual modeling. Instead, a 3D

solid model seems to give the students a better understanding of the task being taught.

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Future Plans

Future plans include, but are not limited to:

 Repeating the study to verify the results by using additional types of models.

 Repeating the study using a different population such as technology education,

science or mathematics students.

 Repeating the study by adding additional visual cues during the display of 3D

objects, including shadows, lighting and size.

 Repeating the study by comparing males vs females as it has been suggested

that males tend to do better on spatial ability tasks than females (Carriker, 2009).

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About the Authors

Petros J. Katsioloudis is an Associate Professor and the Industrial Technology Program Leader,

Department of STEM Education and Professional Studies, Old Dominion University, Norfolk, VA. His

research focuses on improving teacher and student performance in STEM education, and enhancing the

development of a national STEM-educated workforce.

Email: pkatsiol@odu.edu

Vukica Jovanovic is an Assistant Professor of Mechanical Engineering Technology Department, Frank

Batten College of Engineering and Technology, Old Dominion University, Norfolk, VA. Her areas of

interest include the following: Mechatronics, Product Lifecycle Management, Digital Manufacturing,

Engineering Collaboration, RFID, and Assembly Systems.

Email: v2jovano@odu.edu