CONSTRUCTION ENGINEERING CLASSROOM PROJECTS: ON-DEMAND AUGMENTED REALITY INFORMATION DELIVERY, AND GEOREFERENCED CAMPUS MAPPING

Amir H. Behzadan

Missouri State University, Springfield, MO

ABSTRACT: Recent technology advancements have provided a great opportunity to revisit and enhance

pedagogical methods in science, technology, engineering, and math (STEM). In construction and civil engineering

domain in particular, an extensive body of knowledge exists on new technologies such as augmented reality (AR)

and location-aware applications. However, most work in these areas has mainly benefited academic research or

industry development, and resulted in prototypes that cannot be readily used and evaluated in classrooms for

educating students and training the future workforce. In this paper, two separate classroom projects conducted by

the author are presented in order to illustrate the value of these technologies to student learning. In the first project,

design and assembly data of a model building were presented to students on a mobile AR application launched on

their smartphones. Students accessed information on-demand and as they made progress in their work. In the

second project, students in a surveying class used handheld GPS devices to collect positional information of

several key points on the campus grounds, and then used georeferenced AR and digital mapping to lay the collected

GPS data over Google Earth maps of the campus. This enabled them to calculate the areas of five main land

classifications (buildings, greens, roads, parking lots, and commons). Results of this project was compared to the

historical land classification data of the same campus area to determine land repurposing trends due to the growth

in campus size and student population over years. Findings of both projects showed that emerging technologies

such as AR and digital mapping helped improve student teamwork, collaboration, and classroom engagement.

This paper is aimed at educators and researchers interested in adopting these or similar classroom techniques and

strategies in order to introduce students to practical applications of new technologies in their areas of study.

KEYWORDS: augmented reality, construction engineering, education, information delivery, global positioning,

georeferenced, digital mapping.

1. INTRODUCTION

Following several years of extensive research in Augmented Reality (AR) visualization, smart sensing, and digital

prototyping within the architecture, engineering, and construction (AEC) domain, the application of these new

technologies in construction and civil engineering education has recently started to gain a rapid interest and draw

the attention of educators in AEC disciplines. During the past few years, more and more educators have explored

the possibility of and the extents to which new technology can be incorporated into their classrooms to provide

vast benefits for students, and help develop crucial 21st century skills for upcoming generations, including higher

scientific reasoning, communication, and critical-thinking skills. According to the latest K-12 Horizon Report, an

annual trendsetting look at the current state of technology and learning produced by the New Media Consortium

(NMC), a major driving factor in adopting new educational technology over the long term is that it results in a

pedagogical shift toward deeper learning approaches, best typified by the move to putting greater emphasis on

project-, inquiry-, and challenge-based learning. At the same time, most classroom technologies facilitate

collaborative learning, thus affecting the way teachers and students learn, and enabling students to transition from

consumers to creators and ultimate beneficiaries of active, hands-on learning (NMC 2014).

Previous work conducted by the author has demonstrated the high potential of using AR technology to enhance

the instructional methods in engineering and science, and promote students’ engagement in the learning process

(Shirazi and Behzadan 2015; Vassigh et al. 2014; Behzadan and Kamat 2012). In particular, Shirazi and Behzadan

(2014) designed and assessed a collaborative context-aware mobile AR tool called CAM-ART, for construction

and civil engineering students by enhancing the contents of an ordinary textbook using computer-generated three-

dimensional (3D) objects, sound, video, and graphs and delivering customized instructional materials to the

students on their smartphones or tablet computers. In another recent effort, researchers at Carnegie Melon

University designed NoRILLA, a mixed reality testing platform to provide personalized feedback to young

learners while experimenting in the real world. They found out that students perform up to five times better when

instruction combines the real world with the virtual world (Yannier et al. 2015). Other researchers have also

investigated the potential of AR in their respective fields (Miyashita et al. 2008; Klopfer et al. 2005). Shi et al.

(2002) worked on a smart remote classroom prototype that enabled users to access a virtual classroom with

different devices and networks at the same time, and provided a rich set of functions for teachers and students to

efficiently carry out the real-time interactive distance learning. In another study, Stamper and Dekker (2000)

discussed how a rapid prototyping machine was used in two different undergraduate mechanical engineering

classes (a design methodologies class for third year students and the senior mechanical engineering laboratory) to

add excitement and realism.

Building upon the author’s previous experience with the application of AR in educational and training contexts,

the aim of this paper is to demonstrate two recent experiments conducted in classroom settings with the

participation of large numbers of engineering students that ultimately resulted in enhanced student learning quality

and involvement in course activities. The experiments presented in this paper are selected for their applicability

and relevance to construction and civil engineering curricula. Nevertheless, the knowledge and skills imparted in

students who participated in these experiments transcends the traditional boundaries of construction and civil

engineering and can be potentially value-adding to a host of engineering and scientific tasks. In Section 2, AR is

used to deliver information in a model building design and assembly project. In Section 3, students used

visualization and digital mapping technologies, together with engineering surveying techniques in order to create

and analyze georeferenced maps of a designated portion of the university campus. In each Section, first, the

objective and design of each experiment is explained and next, the process followed by students as well as key

results and findings will be presented.

2. PROJECT 1: ON-DEMAND INFORMATION DELIVERY

The goal of this experiment was to design and assemble a model building that complies with six performance

measures including three primary (namely building volume, number of elements, and completion time) and three

secondary measures (namely building cost, embodied carbon, and fire resistance). Students in each group were

asked to make a model building with a volume closest to 30,000 cm3 in the least possible time while using the

fewest number of elements. The final model had to be at minimum cost, and result in the least possible carbon

footprint and maximum fire resistance. In order to better assess the value of providing on-demand design

information using augmented reality to students, participants were divided between control and test sessions. In

total, ten teams of three students participated in the control session experiment and ten teams of three students

participated in the test session experiment. Students in the control session used ordinary print manual instructions,

and students in the test session used an AR application on their mobile devices (smartphones or tablets) to

communicate with a virtual instructor and receive on-demand design and assembly information.

2.1 Experiment Design

In each session, participants were first introduced to the overall goals of the experiment. Following this brief

introduction, no additional description was provided and groups were asked to begin the experiment. In control

session experiment, each group was given a print manual handbook that contained descriptions of steps needed to

complete the model building design and assembly task. All necessary primary and secondary measurement data

was also included in the manual. Students were asked to follow the manual to determine what they had to do and

make necessary decisions, as shown in Figure 1(a).

(a) (b)

Fig. 1: (a) Control session students used print manual instructions to design and assemble a model building, (b)

Test session students scanned the tracking image attached to each building element to access information.

In test session experiment, on the other hand, each group was given a 2-page handout containing only three QR

codes that would link them to the virtual instructor and digitally-stored design and performance information of

building elements. A large cardboard cutout of an avatar was placed in one corner of the room. Students used their

mobile devices to scan the first two QR codes and then turn towards the avatar cutout to watch the corresponding

instructional videos. Next, they used their mobile devices to scan the third QR code and gain access to design and

performance information of model building elements. As shown in Figure 1(b), the information was visually

overlaid on top of each building element as soon as the tracking image attached to that element was scanned and

detected by the camera of a mobile device. The experiment consisted of the following six steps:

1. Project description: The goal of the experiment and timing requirement were presented in this step. Also,

a sample final product (i.e. model building) was shown to give students a better idea of what is expected

at the conclusion of the experiment.

2. Sorting the elements: All building elements (a total of 60) were divided into three different categories of

(1) columns, (2) beams, and (3) junctions and finishing. Before any further action, students were asked

to use three labels provided in their building kit to sort all pieces into these three categories. They were

able to check information relevant to each element using the provided tables in the print manual (control

session) or by scanning the tracking image on each element (test session). In this step, AR information

delivery platform helped students (in test session) find such information much easier by scanning each

element rather than having to separately looking up each element in the tables provided in print manuals

(for control session).

3. Rules and regulations: important rules each group had to follow when designing and assembling their

model was presented in this step. The rules described how to use beams and columns in the building,

dimension requirements, and unacceptable configurations.

4. Assessment factors and goals: Each group had to evaluate their final product using 6 equal-weight factors

(3 primary and 3 secondary, as described earlier). Students were introduced to these factors and were told

that their group’s final score will be determined by comparing their design to those of other groups in

their session.

5. Material information: In addition to having three different element types (as explained in step 2), each

element was assumed to be either made of concrete, steel, or wood material. The choice of material could

affect the performance of the final product in different ways. The material information was presented in

this step in printed tables (for control session) and using virtual tables and AR information delivery (for

test session).

6. Overall assessment and conclusion: Each group was also provided with an assessment table to fill it out

at the end of their design and assembly experiment. This helped them organize and present all required

information and check for missing items, or if any correction was needed in their product design to

achieve a better outcome.

2.2 Results and Key Findings

Table 1 shows the results obtained from each session with regard to the 3 primary measures. In this Table, the

average and coefficient of variance (CV) (defined as the ratio of standard deviation to mean) for each factor

considering the performance of all 10 groups in each session are shown.

Table 1: Primary measures statistics for control and test sessions.

Session Building Volume (cm3) Number of Elements Completion Time (min)

Average CV Average CV Average CV

Control 34,801 0.35652 33 0.18 69 0.10

Test 31,015 0.15554 29 0.24 73 0.18

The building volume (in cm3) is calculated by multiplying the elevation of the topmost point on the building (in

cm) by the building footprint (in cm2). As shown in this Table, the average building volume in the test session (that

used the designed AR application for instruction and information delivery) was closer to the target value of 30,000

cm3. Also, the test session used fewer elements in their final design but spent slightly more time on the experiment.

The difference in completion time was about 4 minutes on average which can be mostly attributed to the fact that

students in this session had to spend some time upfront to learn how to use the AR application on their mobile

devices to retrieve instructional videos and element information. It was also observed through the recorded

videotape of the experiment that compared to the control session students in the test session showed more

involvement in the design process and spent larger portions of their experiment time on communication and

exchanging ideas. Table 2 lists the results obtained from each session with regard to the 3 secondary measures. In

this Table, the average and CV of each factor considering the performance of all 10 groups in each session are

shown. As shown in this Table, the average building cost and embodied carbon is significantly less for the control

session than the test session. However, no statistically significant difference was observed between the average

fire resistance factors for both sessions, considering the CV values. The author believes that one contributing factor

to these results is that students in the test session had to scan building elements one by one and for as many times

as needed during the experiment in order to retrieve information, while students in the control session had this

information readily available in their print manuals during the entire time of the experiment. The need for the

repetitive use of mobile devices to retrieve information may have caused frustration in test session students. This

problem coupled with the fact that students were under pressure to finish their designs on time might have

ultimately resulted in less efficient secondary calculations in the test session.

Table 2: Secondary measures statistics for control and test sessions.

Session Building Cost ($) Embodied Carbon (ton) Fire Resistance (hr)

Average CV Average CV Average CV

Control 3,391,140 0.479776 3,665 0.6095 2.515 0.2136

Test 4,412,160 0.436926 5,270 0.6259 2.361 0.0751

In order to scientifically assess the student task load in control and test sessions, the NASA TLX assessment

technique was used (Hart and Staveland 1988). NASA TLX considers six subscales representing independent

clusters of variables indicating workload. These subscales are frustration, effort, own performance, temporal

demand, physical demand, and mental demand (Cao et al. 2009). Figure 2 shows the results obtained from the

control and test sessions with respect to these six subscales. To statistically compare the results, the p-value test

was conducted. The p-value test is a statistical significance testing method which calculates the probability of

obtaining a test statistic result at least as extreme as the one that was actually observed, with the assumption that

the null hypothesis is true (Mendenhall and Sincich 2006). Here, the null hypothesis was that “each subscale is

statistically equal between the two sessions”. Results of the p-value analysis revealed that although students in the

test session felt more frustrated (p-value = 0.044), they believed that they performed relatively better (p-value =

0.081). Additionally, according to the NASA TLX final assessment results, the average workload score (out of

100) achieved by both sessions is almost the same (47 for control session, and 49 for test session). However, it

must be noted that besides the time and effort students in both sessions had to spend on the actual building design

and assembly task, students in the test session had to spend extra time and effort to first learn how to work with

the AR application to extract information. In other words, relative to the control session, the workload of students

in the test session was divided between a main activity (i.e. building design and assembly) and a prerequisite

activity (i.e. learning how to use a mobile application). Therefore, it can be inferred that test session students were

under less workload as far as the actual building design and assembly task was concerned.

Fig. 2: Calculated NASA TLX subscales for control and test groups (out of 100).

3. PROJECT 2: GEOREFERENCED CAMPUS MAPPING

Compared to experiment 1, this experiment was open-ended in the sense that it provided students with more

flexibility in designing and carrying out the steps necessary to achieve the experiment goal. The ultimate

operational goal of this experiment was to evaluate students’ competency in using geospatial tools and data such

as global positioning system (GPS) and computer visualization in mapping a specific portion of university campus,

and calculating and reporting the total area, as well as areas corresponding to five main land classifications

(buildings, greens, roads, parking lots, and commons), and the distribution of these areas in a pie chart. In order to

promote collaboration and constructivism, no other specific guidelines were provided. The underlying pedagogical

need for this experiment was the limited opportunities in the curricula of most ABET-accredited civil and

construction engineering programs that give students the “ability to design and conduct experiments, as well as to

analyze and interpret data” (ABET student outcome 3b). Currently, students in most science and engineering

courses even those that have built-in laboratory components often carry out experiments in accordance with

prescribed rules and guidelines that take away from their ability to think outside the box and be innovative and

creative. Therefore, this experiment was introduced to the undergraduate curricula to address this need in a

systematic measureable manner. According to McCreanor (2001), in order to meet ABET student outcome 3b,

students must construct proficiencies in all four outcome elements, including:

1. Designing experiments: Develop a methodology which will produce high quality data that can be used to

evaluate a specific process or parameter.

2. Conducting experiments: Operate laboratory equipment appropriate to the course or discipline,

demonstrate appropriate laboratory technique and etiquette, and collect data.

3. Analyzing data: Organize data, perform appropriate data manipulations and calculations, and present final

data in an appropriate format.

4. Interpreting data: Use good engineering judgement to determine if data is reasonable, and draw

appropriate conclusions from data.

3.1 Experiment Design

This experiment was conducted over the span of two academic semesters. In each semester, students were divided

into teams of five or six members. In total, 21 teams participated in this experiment. Each team was provided with

a Garmin GPS 72 handheld unit to collect as many positional data points as necessary from a portion of university

campus, as shown in Figure 3(a). This portion of campus consisted of several academic buildings, green areas,

roads, parking lots, and commons (i.e. everything else). As shown in Figures 3(b) and 3(c), students in each team

collaborated to design their own experiment and approach to data collection, analysis, and presentation following

a rigorous brainstorming session. Teams were also encouraged to transcend traditional classroom materials and

instructional delivery by adopting creative tools and technologies such as geographic information system (GIS),

AR, digital mapping, and 3D visualization.

(a) (b)

Fig. 3: (a) Area of university campus that was used for data collection in experiment 2, (b) Students in each team

designed their own experiment and approach for data collection, analysis, and presentation.

In order to calculate the area of a polygon given the GPS coordinates of its corners, a classic mapping technique

first introduced by Vincenty (1975) was used. Following this technique, the length of each side is first calculated

given the longitude and latitude of its two end points. Once all side lengths are known, the total area of the polygon

can be calculated using classic surveying calculations. A sample area calculation formula for a quadrilateral (four-

sided polygon) is shown in Figure 4.

Fig. 4: Calculating the area of a closed polygon using the Cartesian coordinates.

3.2 Results and Key Findings

Some examples of educational mapping projects using GPS and GIS to promote hands-on training, as well as learn

abstract topics such as data accuracy and map scales can be found in the literature (Chen 1998; Rosenberg 2004).

In the project presented in this paper, students combined knowledge of GPS technology and data analysis they had

learned in the classroom with their own creativity in a constructivist and collegial learning environment. Overall,

student performance exceeded expectations in several levels. In particular, giving students the flexibility of going

above and beyond the project requirements and encouraging them to think outside the box resulted in a number of

very creative ideas and approaches in collecting and analyzing data as well as presenting the results. Several groups

presented a historical account of the land development in and around the designated campus area over the past few

decades by collecting and analyzing maps and land classification data from DOT databases and aerial photos.

Figure 5 shows a sample land classification analysis conducted by one group for the period between 1990 and

2014.

Fig. 5: Sample land classification analysis for the period between 1990 and 2014.

1990 1995 1999

2005 2008 2014

Figure 6 shows the trend of change in land classification for the same campus area for the period between 1990

and 2014. As shown in this Figure, with the growth in student population and campus size, there has been a major

decrease in green areas (as a percentage of the total area) between the years 1990 and 1999. Since then, this trend

has been reversed. Also, a meaningful increase in the areas corresponding to parking lots and commons during the

same period can be observed in this plot.

Fig. 6: Change in land classification for the period between 1990 and 2014.

Some groups used GIS in order to verify their hand calculations. They imported digital maps of the university

campus into the GIS software, marked the boundaries of the areas of interest as well as key points on the map, and

calculated the total area and areas corresponding to different land classifications. Results were then compared to

the values obtained from field data and hand calculations to determine and isolate potential errors. Figure 7 shows

a snapshot of GIS software interface used by one of the groups.

Fig. 7: GIS software was used to calculate exact total area and areas corresponding to different land classifications.

Several groups took advantage of the built-in visualization features of digital mapping software such as Google

Earth and Google Map to superimpose collected GPS positional data over the aerial imagery of the campus area

(thus creating a digital AR view) in order to visually verify the accuracy of the field data collection. Figure 8 shows

sample work from two groups who added GPS positional data as a new layer to Google digital maps. At the

conclusion of this experiment, all groups were able to successfully calculate and present their findings in terms of

total area, and areas corresponding to different land classifications (Figure 9).

Fig. 8: GPS positional data were superimposed over digital maps and imagery to verify the accuracy of the

collected field data.

Fig. 9: At the conclusion of the experiment, all groups successfully presented their designed approach to conduct

data collection, analysis, and interpretation, as well as final results and findings.

According to the results of analysis, the average total area calculated from GPS field data collection was 855,694

ft2 with a standard deviation of 89,440 ft2. Figure 10 shows a summary of results obtained from 21 student groups

involved in this experiment within the span of two semesters. As shown in this Figure, out of the total surveyed

land on campus, an average of 9% was occupied by buildings (standard deviation of 2%), 27% was used for green

areas (standard deviation of 10%), 6% was covered in pavement (roads) (standard deviation of 4%), 38% was

assigned to parking lots (standard deviation of 5%), and the remaining 20% was occupied by commons (i.e.

everything else) (standard deviation of 11%).

Fig. 10: Summary of area calculations reported by all 21 groups involved in campus mapping experiment.

4. SUMMARY AND CONCLUSIONS

Recent advancements in and applications of technologies such as AR, smart sensing, and digital prototyping has

created ample opportunities to revisit and enhance pedagogical methods in STEM disciplines. In construction and

civil engineering in particular, an extensive body of knowledge exists on these new technologies. However, most

previous work in these areas has benefited academic research or industry development, and resulted in prototypes

that cannot be readily used and evaluated in classrooms for educating students and training the future workforce.

In this paper, two practical scenarios that involved the use of AR and digital mapping technologies in classroom

settings in order to enhance student learning and encourage classroom participation were introduced. In the first

experiment, a mobile AR tools was used to deliver information to students in a model building design and assembly

project. In the second experiment, students used visualization technologies such as AR and digital mapping,

together with engineering surveying techniques to create and analyze georeferenced maps of a designated portion

of the university campus. Results of both experiments indicated that the incorporation of new emerging

technologies such as AR and digital mapping in traditional classroom materials helped significantly improve

student engagement and performance in classroom activities. The ideas and findings of these and similar

experiments can be effectively replicated in the curricula of construction and civil engineering programs as well

as in other fields where students and educators are expected to gain knowledge of and develop skills in working

with new technologies to solve multidisciplinary engineering and scientific problems.

5. ACKNOWLEDGMENTS

The materials presented in Section 2 (on-demand information delivery) were supported by the Engineering

Information Foundation (EiF) through grant 13.02. The author gratefully acknowledges the support from the EiF.

Any opinions, findings, conclusions, and recommendations expressed in Section 2 are those of the author and do

not necessarily reflect the views of the EiF. The materials presented in Section 3 (georeferenced campus mapping)

are parts of a classroom project that was conducted over the period of two academic semesters with the

involvement of 120 undergraduate students enrolled in civil and construction engineering programs. The author

served as the course instructor and directly supervised student activities related to this experiment.

6. REFERENCES

Behzadan A. H. and Kamat V. R. (2012), A framework for utilizing context-aware augmented reality visualization

in engineering education, Proceedings of the 2012 conference on construction applications of virtual reality

(CONVR), National Taiwan University, Taipei, Taiwan, 292-299.

9

27

6

38

20

0

5

10

15

20

25

30

35

40

45

50

Buildings Green Areas Roads Parking Lots Commons

Per

cen

tage

(%

)

Campus Land Classification

Cao A., Chintamani K. K., Pandya A. K. and Ellis R. D. (2009), NASA TLX: software for assessing subjective

mental workload, Behavior Research Methods, The Psychonomic Society, Vol. 41, No. 1, 113-117.

Chen X. M. (1998), Integrating GIS education with training: a project-oriented approach, Journal of Geography,

Taylor and Francis, Vol. 97, No. 6, 261-268.

Hart S. G. and Staveland L. E. (1988), Development of NASA-TLX (task load index): results of empirical and

theoretical research, Advances in Psychology, Elsevier Science, Vol. 52, 139-183.

Klopfer E., Perry J., Squire K. and Jan M. F. (2005), Collaborative learning through augmented reality role playing,

Proceedings of the 2005 conference on computer support for collaborative learning: the next 10 years, Taipei,

Taiwan, 311-315.

McCreanor P. T. (2001), Quantitatively assessing an outcome on designing and conducting experiments and

analyzing data for ABET 2000, Proceedings of the frontiers in education conference, Reno, NV, 12-16.

Mendenhall W. and Sincich T. (2006), Statistics for engineering and the sciences (fifth ed.), Pearson.

Miyashita T., Meier P., Tachikawa T., Orlic S., Eble T., Scholz V., Gapel A., Gerl O., Arnaudov S. and Lieberknecht

S. (2008), An augmented reality museum guide, Proceedings of the 7th IEEE/ACM international symposium on

mixed and augmented reality (ISMAR), Cambridge, UK, 103-106.

New Media Consortium (NMC) (2014), K-12 Horizon Report, http://www.nmc.org/, Accessed: May 11, 2015.

Rosenberg L. (2004), The design of a constructivist learning experience that uses GPS technology, Proceedings of

the ED-MEDIA worldwide conference on educational multimedia, hypermedia and telecommunications, Lugano,

Switzerland, 4837-4841.

Shi Y., Xie W. and Xu G. (2002), Smart remote classroom: creating a revolutionary real-time interactive distance

learning system, Lecture Notes in Computer Science (Advances in Web-Based Learning), Springer, Vol. 2436, 130-

141.

Shirazi A. and Behzadan A. H. (2015), Content delivery using augmented reality to enhance students' performance

in a building design and assembly project, Advances in Engineering Education, ASEE, Vol. 4, No. 3, 1-24.

Shirazi A. and Behzadan A. H. (2014), Design and assessment of a mobile augmented reality-based information

delivery tool for construction and civil engineering curriculum, Professional Issues in Engineering Education and

Practice, ASCE (published online).

Stamper R. E. and Dekker D. L. (2000), Utilizing rapid prototyping to enhance undergraduate engineering

education, Proceedings of the frontiers in education conference, Kansas City, MO, 1-4.

Vassigh S., Newman W. E., Behzadan A. H., Zhu Y., Chen S. C. and Graham S. (2014), Collaborative learning in

building sciences enabled by augmented reality, American Journal of Civil Engineering and Architecture, Science

and Education Publishing, Vol. 2, No. 2, 83-88.

Vincenty, T. (1975). Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations.

Survey Review, Vol. 23, No. 176, 88-93.

Yannier N., Koedinger K. R., and Hudson S. E. (2015), Learning from mixed-reality games: is shaking a tablet as

effective as physical observation?, Proceedings of the 33rd annual ACM conference on human factors in computing

systems, Seoul, South Korea, 1045-1054.