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Assessment of Students’ Preconceptions in the Introductory Transportation
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Engineering Course
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Miloš N. Mladenović (Corresponding author)
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Graduate Research Assistant
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Charles Via Department of Civil and Environmental Engineering
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301-D, Patton Hall
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Virginia Tech
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Blacksburg, VA, 24061
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Phone: (540) 553-5949
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E-mail: [email protected]
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Katerina Mangaroska
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Department of Information Technology
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Worchester Polytechnic Institute
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Montasir M. Abbas, Ph.D., P.E.
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Associate Professor
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Charles Via Department of Civil and Environmental Engineering
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Virginia Tech
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Key words: Transportation Engineering Education, Preconceptions, Concept map, Student survey
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Word count: 5,250
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Tables and Figures: 11
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ABSTRACT
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Introductory transportation-engineering courses are critical in developing student’s interests for
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transportation and for providing foundational knowledge base for high-level courses. This
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research has been initiated by the need for appropriate learning environment in the introductory
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transportation-engineering course. Students’ preconceptions are an important element of the
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learning environment, considering that people construct new knowledge based on the previous
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understandings and beliefs about important concepts. The research presented here has focused on
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assessing students’ preconceptions in the introductory transportation-engineering course at
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Virginia Tech. The critical approach of this research was the use of concept maps, as tools for
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assessment of students’ preconceptions. The paper starts with an evaluation of the course, and
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methodology for collecting, coding, and analyzing concept maps. The results of this assessment
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point to the variation of students’ preconceptions based on their major and year, along with
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specific positive, negative, or missing preconceptions. The analysis also includes students’
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concepts about transportation engineering at the end of the course. Considering this is the initial
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research in the area, there are several implications for further research.
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INTRODUCTION
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In the next decade, transportation profession will face significant retirements in both public and
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private sector [1, 2]. In order to meet the demands of the 21st century transportation systems,
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there will is a critical need for attracting more and academically better students into
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transportation engineering [3, 4]. In addition, there will be a critical need for educating a highly
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qualified transportation workforce. As a positive fact of the current status, at least one
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transportation engineering course is offered in over 90% of undergraduate programs in civil
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engineering [5]. However, this is also a part of the drawback of the current system, since the
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dominant practice is to have one or two undergraduate and several graduate courses in civil
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engineering curriculum with focus on transportation [3]. In addition, the issue is even greater
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since many students do not pursue higher level courses in transportation [4]. Moreover,
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additional requirements for educating future engineers are originating from the Accreditation
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Board for Engineering and Technology (ABET), which requires student outcome criteria [6].
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Consequently, transportation-engineering education needs to respond to very high learning
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requirements, imposed by the profession and ABET, along with the lack of undergraduate
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courses focusing on transportation and the lack of student interest in them.
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The Need for Investigating Students’ Preconceptions
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Considering the need for improved quality of learning and attracting more students to
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transportation profession, first course in transportation engineering (TE) is of great importance
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because it often determines if the student will pursue specialization in TE [7]. In addition,
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introductory course is also important because it provides critical foundational knowledge for
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learning that students will experience in higher-level TE courses. However, it is important to
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remember that learning does not happen under any condition [8, 9]. For learning, as knowledge
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acquiring, to happen, there is a need for an appropriate environment and deliberate influence
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[10]. Elements of the learning environment are students themselves, too. Learners always enter
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education with a range of prior knowledge, skills, beliefs, and concepts [11]. All these
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preconceptions can significantly influence what they notice about the learning material, how they
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interpret it, and how they organize it. Consequently, this affects their abilities to memorize, to
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reason, to solve problems, and to acquire new knowledge.
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Research Objectives
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Previous research efforts for improving transportation engineering education focused primarily
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on the improved teaching practice, in-class activities, assessment, or curriculum development for
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academic education or training of transportation engineers [5, 12-20]. However, in the previous
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research, there was one effort focusing on learners’ preconceptions in a traffic signal control
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course [21]. Students’ preconceptions were assessed using a knowledge survey. The
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preconceptions were related to the knowledge gained in the introductory engineering course and
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students’ experience as transportation users.
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The research presented here also relates to students’ preconceptions based on their previous
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engineering education and experiences as transportation users. However, the learning
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environment in this research is an introductory TE course. Considering the aforementioned
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importance of introductory TE course and the impact of preconceptions on learning, this study
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addressed the following questions:
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1. What are students’ preconceptions before entering introductory TE course?
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2. How the preconceptions vary based on student’s background?
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3. How are preconceptions modified based on the course curriculum?
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METHODOLOGY
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Learning Environment
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The course that was under assessment in this research is CEE 3604 Introduction to
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Transportation Engineering (CEE 3604) at Virginia Tech. The course includes aspects of
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transportation planning (e.g., estimation of flows on transportation networks over time),
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transportation design (e.g., design of specific roadway curve parameters) and transportation
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operations (e.g., traffic-signal timing optimization). The course objectives are identified as
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follows:
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1.
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Model vehicle acceleration and deceleration behavior and estimate the distance required
to accelerate and decelerate.
2.
Forecast traffic volumes for the design of transportation facilities using Travel Demand
Modeling
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3.
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Estimate different traffic stream parameters (flow, density, and speed) and estimate
queues at roadway bottlenecks.
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4.
Estimate freeway, multi-lane highway, and two-way highway level of service.
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5.
Design and evaluate traffic signal timing parameters.
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6.
Design the geometric vertical and horizontal alignment of highways.
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7.
Design flexible and rigid pavements using the AASHTO procedures.
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The order in which the objectives are presented is the order in which the course curriculum was
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structured. In addition, the course is structured around the book “Principles of Highway
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Engineering and Traffic Analysis – Fifth Edition” by Fred Mannering and Scott Washburn. The
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textbook is selected as an entry-level transportation-engineering book that focuses on highway
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transportation and provides the depth of coverage needed to serve as a basis for future
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transportation courses. In addition, the book covers the material likely to appear in the FE and PE
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exams in Civil Engineering. The course is mapped to the following ABET student outcome
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criteria:
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a) an ability to apply knowledge of mathematics, science, and engineering;
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b) an ability to design and conduct experiments, as well as to analyze and interpret data;
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c) an ability to design a system, component, or process to meet desired needs within realistic
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constraints such as economic, environmental, social, political, ethical, health and safety,
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manufacturability, and sustainability;
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d) an ability to function on multidisciplinary teams;
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e) an ability to identify, formulate, and solve engineering problems;
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f) an understanding of professional and ethical responsibility;
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g) the broad education necessary to understand the impact of engineering solutions in a
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global, economic, environmental, and societal context;
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h) a recognition of the need for, and an ability to engage in life-long learning;
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i) a knowledge of contemporary issues;
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j) an ability to use the techniques, skills, and modern engineering tools necessary for
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engineering practice.
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The following Table 1 presents a general teaching-goal inventory developed for this class using
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an online tool [22].
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Table 1: Teaching goals inventory
Goals Included
in Cluster
Percent Rated
"Essential"
Mean Rating
Higher Order Thinking Skills
1-8
25%
3.88
Basic Academic Success Skills
9-17
0%
1.56
Discipline-Specific Knowledge and Skills
18-25
63%
4.5
Liberal Arts and Academic Values
26-35
0%
2.4
Work and Career Preparation
36-43
0%
2.25
Personal Development
44-52
11%
2.11
Cluster
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In addition, the same tool used to develop previous table was used to provide a list of the
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essential goals, identified as:
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1.
Learn concepts and theories in this subject;
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2.
Learn terms and facts of this subject;
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3.
Learn to understand perspectives and values of this subject;
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4.
Develop ability to synthesize and integrate information and ideas;
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5.
Develop ability to think holistically: to see the whole as well as the parts;
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6.
Learn to appreciate important contributions to this subject;
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7.
Prepare for transfer or graduate study;
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8.
Develop capacity to think for oneself.
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To conclude, CEE 3604 is a course focused on road TE, aiming at a wide range of ABET student
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learning outcomes, with a focus on discipline-specific knowledge and higher-order thinking.
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Study Description
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The study primarily focused on using concept maps, which were part of the regular assignments
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in the course. There were no exclusion criteria, since the participation in the study was voluntary.
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Study was presented through an in-class verbal script at the beginning of the class period. After
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the verbal script, a consent form was distributed for signing by those interested to participate in
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the study. Interested participants had 15 minutes to read the consent form and decide whether
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they want to participate in the study. Students were asked to approve that their concept map
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assignments be used in the study. Participants did not have any additional time commitment to
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the study. There was no risk for participants, since concept maps were coded and evaluated after
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the semester was over and final grades were assigned.
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The course is open for registration for student majoring in Civil and Environmental Engineering
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(CEE), Construction Engineering and Management (CEM), Industrial and Systems Engineering
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(ISE), and Mechanical Engineering (ME). The course is part of required curriculum only for the
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students majoring in Civil Engineering, while it is an elective course for students from other
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majors. This creates a diverse learning environment, where students enter CEE 3604 from
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different engineering fields and consequently different knowledge base. In addition, 95% of the
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students have a driving license, thus also entering the course with previous experience as
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transportation users. The participants of this study were undergraduate students enrolled in CEE
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3604 during the spring term 2013. The total number of participants was 38, with 36 in beginning-
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of-semester concept map (BCM) and 30 in end-of-semester concept map (ECM). From this
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number, seven students were from CEE, one from CEM, 24 from ISE, and six from ME. The
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course distribution of these students was one sophomore, eight junior (mainly majoring in Civil
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Engineering), and 29 senior (mainly majoring in ISE and ME). Gender distribution of these
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students was 33 male and five female students.
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An anonymous survey was conducted before the beginning of the course. The following Table 2
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presents a compiled list of answers to three questions directly related to the study. Other
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questions were intended to help the instructor with the preparation for the course. As it can be
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seen from the Table 2, most of the students answered that they selected this course as an elective
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course that was somewhat interesting to them. Considering that majority of the students was
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from ISE, some of them had previous experience with transportation topics. The answers to the
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question on the learning expectations from the course were twofold. Some students wanted to
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learn generally about various aspects of TE and its relation to society. On the other hand, some
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students had some specific interest areas, e.g., traffic management or road design. The answers to
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this question already implied that some students had some strong preconceptions about
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transportation systems and infrastructure. Answer to the question on relation to the previous
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courses provided a wide range of courses students considered as related to CEE 3604. CEE and
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CEM students primarily related CEE 3604 to Building Materials and Environmental
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Engineering. However, it was interesting to see how ME students related CEE 3604 to Fluid
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Dynamics course, while ISE students related it to Operations Research and Simulation courses.
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The information from this table positively supports the premise that students are entering the
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course with previous knowledge that can shape the way they learn concepts in TE. In addition, it
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supports the premise that these students are entering CEE 3604 with different knowledge base,
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depending on their major.
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Table 2: Pre-course survey questions and answers
Question
Answers
Why did you select this course?
- Taking it as a technical elective for my major.
- To fulfill the civil engineering requirement.
- Interested in roads and transportation. Might want to pursue a career in transportation.
- I selected this course because it qualifies as a tech elective, and seemed an interesting topic.
- As a non-in major technical electives.
- I selected this course because I needed a technical elective, it was available, and I have
always been interested in transportation particularly more so public transit. I take public
transit often sometimes across multiple states and while the US public transit is manageable,
it could certainly be improved a great deal so I want to learn more about it.
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I thought it would pertain to Industrial and Systems.
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I wanted to learn more about how roadways are designed and traffic flow is managed.
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- To gain a general knowledge of transportation engineering.
- Traffic management.
What are your learning expectations from this course?
- Learn why traffic is horrendous in Northern Virginia! Also, want to know how traffic can be
managed logistically.
- To learn how roads are designed to meet certain requirements and how to possibly improve
roads and decrease congestion.
- Learn the basics of transportation.
- I hope that I will learn more about the technical challenges facing our transportation
infrastructure, and the considerations that must be taken when designing them.
- I hope to learn traffic and road design to aid in my understanding in order to benefit future
construction projects. Also to explore traffic control methods.
- To gain a greater understanding of what transportation engineering is and what it does.
- More in-depth knowledge regarding the engineering concepts behind transportation
engineering. I am not considering field in engineering transportation after graduation,
however, I believe that the concepts that I learn in this class can be applicable to my future
career interest.
- I expect to learn a little about a lot of different areas of transportation including cars, roads,
systems, infrastructure and hopefully public transit as well.
- I expect to learn more about queuing theory and basic road design.
course?
think will help you learn during this
Name your previous courses that you
- To get a basic understanding of what transportation engineers do on a daily basis
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Introduction to Civil Engineering
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Building Materials
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Environmental Engineering
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Physics
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Vehicle Dynamics
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Fluid Dynamics
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System Dynamics
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Simulation
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Production and Operation Management
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Probabilistic Operations Research
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Deterministic Operations Research
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Concept Maps as Preconception Assessment Tools
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This study used concept maps as tools for assessing students’ preconceptions. Concept maps are
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defined as “graphical tools for organizing and representing knowledge” [23]. Typical concept
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map consists of circles or boxes containing concepts (usually using nouns), connected by
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directed lines that show the relationships between those concepts (usually using verbs). Concept
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maps are usually constructed to answer a specific focus question, and are an excellent method for
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measuring students’ conceptual knowledge and relation among their thoughts. An important
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feature of a concept map is that it forces students to challenge their own understanding, while
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allowing for flexibility in representing differences in thinking.
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There were two assignments in the form of a concept map. BCM was assigned during the first
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class, right after the students introduction, without presenting any information on TE (examples
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are presented on Figure 1 and Figure 2). The concept map assignment was partially completed
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during class time, with the intention to obtain originally preconceived students’ concepts. In
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addition, there was a second assignment in the form of a concept map, as ECM, assigned after all
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the lectures and assignments, and before the last class in the semester (example is presented in
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the Figure 3). Each student has devised an individual concept map during both assignments.
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Most of the students in this study have not developed a concept map before, so this is the reason
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why concept mapping was presented to them as an assignment with accompanying explanation.
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The explanation of the concept map assignment was divided into three sections. The first section
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introduced a concept map as a visualization tool and its features. In the second part, students
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were given step-by-step instructions how concept maps are created. Here, students were
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encouraged to first define primary concepts, organize them with links, then choose secondary
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concepts and organize them too. As the last step, a cross-links from one part of the concept map
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to the other are created. In the last part, an example figure of concept map was presented,
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depicting a concept map for the term “scientific method”.
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Figure 1 below presents examples of BCM from two students majoring in CEE and CEM,
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respectively. In addition, Figure 2 presents examples of BCM from two students majoring in ISE
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and ME, respectively. Here, one can immediately see the benefit of using concepts maps, which
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visually present the concepts and their inter-relations. A brief observation of concept maps on the
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Figure 1 one can immediately show similarities in preconceptions for these two CEE and CEM
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students. Both students considered there is a close relation between TE, economy, and
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infrastructure. In addition, both students have identified the importance of safety as a goal of TE
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activities. On the other hand, observing concept maps on the Figure 2, one can see more
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differences among ME and ISE students, both between them, and comparing to CEE/CEM
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students. The ME student includes an important concept of vehicles in relation to TE. In
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addition, it is interesting to see that the same student incorporated railways as a concept related
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to infrastructure. ISE student on the other hand, has placed a great emphasize on data analysis as
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a crucial part of TE. Furthermore, it is interesting to see how ISE student preconceived logistics
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as part of TE, and recognized different transportation modes. From just these four concept maps,
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one can conclude that students enter CEE 3604 with a range of preconceptions about TE.
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However, in order to analytically analyze all the concept maps in this study, research team has
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devised a coding procedure, presented in the next section.
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Figure 1: BCM from students from CEE and CEM
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Figure 2: BCM from ME and ISE student
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Methodology for Coding of Concept Maps
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In addition to the concept map capability to visually depict information on student’s
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preconceptions, the research team has devised a methodology for analytically assessing each
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concept map. Each concept in every concept map is assessed using three parameters: concept
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level, relatedness, and connectedness. Level of each concept can be primary, secondary, or
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tertiary, thus determining the position the concept has in the concept map. Primary terms are
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closer to core term “transportation engineering”, while secondary and tertiary are respectively
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further away. Relatedness relates to determining how directly the concept is related to the
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concept of TE. Relatedness is determined on the scale from one to five. The definition of TE
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used in this research was “the application of technology and scientific principles to the planning,
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design, operation, maintenance and management of systems and facilities for any mode of
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transportation (highway, rail, air, water, and pipeline) in order to provide for the safe, rapid,
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comfortable, convenient, economical and environmentally compatible movement of people and
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goods.” Finally, the measurement of connectivity is used to determine how well connected is the
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concept with the other concepts before or after that specific concept. Connectivity is determined
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based on how well the logical flow is from the surrounding concepts and the relation between
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them. Consequently, aggregation of all the three parameters that each concept has describe a
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concept map on the depth of development, relation to the concept of TE, and connection among
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the concepts themselves. Scoring criteria for concept level, relatedness, and connectedness are
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presented in the Table 3 below. In addition, Figure 3 shows a scored example of ECM. On this
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figure, each concept’s level, relatedness, and connectedness are presented in brackets on the
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upper right side, as [level, relatedness, connectedness].
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Table 3: Level, relatedness and connectedness scoring criteria
Level
1 - Primary
2 - Secondary
3 - Tertiary
Relatedness
1 - no relation
2 - very little relation
3 - fair relation
4 - very good relation
5 - excellent relation
Connectedness
1 - no connection
2 - very little connection
3 - fair connection
4 - very good connection
5 - excellent connection
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From Figure 3 one can observe the details of this methodology. The central concept
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“Transportation Engineering” is placed in the left central side of the concept map. For example, a
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concept “Traffic Analysis” is coded as [1, 5, 5], thus marked as having primary level, and
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excellent relation and connection. This concept is coded as primary since it is very close to the
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central concept. Furthermore, it has excellent relation to the TE as directly related to the
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definition of TE, and it is excellently related to the other concepts around it, since it is connected
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to six surrounding concepts. On the other hand, on the right side of the concept map, a concept
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“Aerodynamic” is coded as [3, 3, 3], thus defining this concept as tertiary, and with fair relation
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and connection. This concept is coded as such, considering that it is far away from the central
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concept, since it is only fairly related to TE (as more related to mechanical engineering), and
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since it has only one good connection to the surrounding concepts. Finally, if the concept in the
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circle has contained two or more different concepts (e.g., roads, metro, airplane), each of these
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concepts has been coded separately.
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Figure 3: Example of ECM and analysis of concept levels, relatedness and connectivity
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ASSESSMENT RESULTS
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After coding all the BCM and ECM from study participants, the research team performed a
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qualitative analysis of this information. Software for qualitative analysis of concept maps used in
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this research was NVivo [24]. This software enabled search and query analysis necessary to
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process coded information. Starting from the most global level of analysis, the following Table 4
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presents a list of the most frequent words used in BCM and ECM. This table, as several similar
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tables below, presents the most frequently used words and its frequency of use. This information,
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and the order of other most frequent concepts used in BCM provide an idea of how these
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students perceived TE before entering the course. The relative position of each concept in
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comparison to other concepts provided important information on the frequency of certain
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students’ preconceptions. In addition, the research team has user previously described coding to
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identify specific details on students preconceptions.
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General Analysis of Concepts
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From Table 4, one can observe that students frequently used concepts related to road
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transportation (e.g., roads, highways, cars, vehicles), and infrastructure design and construction.
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Besides road transportation, students had a strong preconception about relation between air
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transportation and TE (e.g., air, airports). Furthermore, students recognized the relation between
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TE and traffic flow, and between TE and safety. Finally, students also had somewhat of a
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preconception about public transportation. Observing the other most frequent words in BCM,
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one can see that they are related to general engineering concepts, potentially from previous
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engineering knowledge base of students.
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In addition, in the middle of the table, in the column Difference, values in green or red represent
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relative difference in the word count between BCM and ECM, with respect to concepts from
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BCM. In cases when the value is in green cell, this word is more frequently mentioned in BCM,
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and if the value is in red cell, the word is more frequently mentioned in ECM. This column
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shows that words “traffic”, “design”, “flow”, “highway”, “safety”, “highways”, and “vehicles”
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are more frequently mentioned in ECM than in BCM. This tells us that students have changed
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their preconceptions related to these concepts, considering them even more related to TE.
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Furthermore, observing the concepts in the ECM columns, one can conclude that they are not as
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general concepts as they were in BCM column, but that they are more closely related to TE. This
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positive trend of increasing concepts has been noted also using concept count for BCM and
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ECM. Research team has noted a difference in the maximum, minimum, and average number of
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concepts in BCM and ECM. Maximum number of concepts in BCM was 21, minimum was 6,
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and average 14.6. On the other hand, maximum number of concepts in ECM was 42, minimum
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was 7, and average was 21.2, thus signifying evident increase in the number of concepts used.
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In addition to this general representation of concepts in Table 4, we have focused on the analysis
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of concepts depending on students major, presented in Table 5. A student from CEM was
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grouped with students from CEE, due to the similarity of majors, in comparison to ISE or ME.
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From this table, one can observe that students from CEE/CEM have strong preconceptions about
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TE related primarily to construction and infrastructure, while students from ME have strong
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preconceptions related to air transportation. Both CEE/CEM and ME students had a
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preconception related to safety. Moreover, students from ISE had strong preconceptions about
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several concepts: traffic flow, logistics, data and data analytics (minimize, maximize), time, and
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queues. Each of these preconceptions is logical, considering previous coursework of these
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students.
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Table 4: Word frequency analysis for all BCM and ECM and relative difference
BCM
Difference
Word
roads
traffic
design
construction
cars
Count
34
31
21
16
13
flow
minimize
11
11
10
9
9
8
-6
11
ECM
roads
highway
Count
57
54
24
24
19
-9
-1
1
3
flow
signal
rigid
flexible
vehicle
lane
17
16
16
16
15
13
8
7
7
6
6
6
2
6
3
0
3
5
highways
horizontal
type
vehicles
vertical
queuing
12
12
12
12
12
11
6
6
6
5
5
5
5
2
4
4
2
0
performance
safety
speed
curve
multilane
trip
10
10
10
9
8
8
4
4
highways
management
move
planning
5
5
5
5
5
5
signals
transportation
service
forecasting
braking
theory
8
8
7
7
7
7
public
queue
vehicles
5
5
5
highway
safety
transportation
engineering
time
air
efficiency
infrastructure
limited
logistics
maximize
people
system
airports
civil
control
data
engineers
10
-26
-33
12
11
-7
4
4
2
Word
traffic
design
pavement
1
5
-7
321
322
323
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Table 5: Most frequent concepts for BCM and ECM per student’s major
BCM - CEE/CEM
Word
construction
design
traffic
road
highway
Count
8
7
7
6
4
safety
infrastructure
management
population
roads
4
3
3
3
3
BCM - ME
Word
traffic
air
design
engineers
people
public
safety
BCM - ISE
Count
7
3
3
3
3
3
3
Word
traffic
roads
Count
17
13
11
9
9
design
cars
road
flow
minimize
transportation
construction
engineering
time
data
limited
ECM - CEE/CEM
traffic
design
curve
population
highway
horizontal
20
12
6
5
4
4
safety
signal
trip
vehicle
4
4
4
4
4
4
vertical
4
pavement
road
logistics
maximize
queue
ECM - ISE
ECM - ME
8
8
8
6
6
6
5
5
5
5
5
element
alignment
flexible
10
7
6
5
4
4
design
traffic
vehicles
highways
pavement
roads
30
28
17
16
16
16
flow
pavement
rigid
transportation
4
4
4
4
lane
flexible
11
10
10
10
9
7
design
highway
traffic
flow
rigid
signal
queuing
horizontal
speed
forecasting
LOS
multilane
service
signals
6
6
5
5
5
5
5
325
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326
Lower part of Table 5 shows the most frequent concepts that students had at the end of the
327
semester, based on their major. These concepts confirm how students’ knowledge has been
328
modified on the basis of their preconceptions at the beginning of the course. Overall, as in Table
329
4, concepts are more specific to TE, but depend on the major of student. One can see that
330
CEE/CEM students evolved their preconceived relation between TE and construction into a
331
relation towards road and pavement design. Furthermore, ISE students maintained their
332
preconceptions related to flow and queuing theory. All students have greatly strengthened their
333
relation between TE and design.
334
As in Table 4, the number of concepts and their frequency in BCM and ECM from Table 5 are
335
also additional information, taking into consideration relative percentages of concepts that are
336
mentioned only once or twice. In BCM, there has been 45.21%, while in ECM there has been
337
29.12% of concepts mentioned once or twice. Furthermore, considering there is smaller number
338
of highly frequent concepts in BCM, and greater percentage of concepts that are mentioned only
339
once or twice, this shows that students’ preconceptions at the start of the course had a wide
340
variance. After the course, we were able to observe a greater number of high frequency concepts.
341
This tells us that students’ conceptions about TE have been consolidated into similar concepts,
342
even if they have been increased in overall number.
343
Table 6 below presents students concepts from BCM and ECM, based on students’ year.
344
Observing the left side of Table 6Table 6, with BCM concepts, one can conclude that senior
345
students had preconceptions related to engineering activities (e.g., design, minimize, limited,
346
engineering, maximize, planning, etc.). By comparison, sophomore and junior students had
347
preconceptions related to TE primarily as transportation users (e.g., management, population,
348
bridges, cars, people, goods, etc.). The right side of this table depicts how the concepts between
349
senior and junior students have changed after the course. One can observe that senior students
350
have maintained their established engineering perspective on TE, which has evolved into
351
corresponding TE concepts related to infrastructure or systems. However, although junior
352
students have modified their concepts (e.g., “design” is a 2nd most frequent factor), they have still
353
maintained some of their previous perspective on TE from the role of transportation users (e.g.,
354
population, trip).
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355
Table 6: Most frequent concepts for BCM and ECM per student’s year
BCM - senior
Word
roads
traffic
BCM - junior/sophomore
ECM - senior
design
minimize
cars
Count
25
23
17
11
10
Word
roads
traffic
construction
design
highway
Count
9
8
7
4
4
construction
engineering
flow
transportation
air
highway
8
8
8
8
7
7
example
flow
infrastructure
3
3
3
3
3
3
flexible
rigid
flow
lane
safety
time
limited
7
7
6
6
6
5
queuing
vehicles
build
building
cars
efficiency
2
2
2
2
2
2
5
5
5
5
5
5
geographical
goods
growth
move
people
safety
2
2
2
2
2
2
5
5
speed
vehicles
2
2
maximize
system
civil
control
data
efficiency
engineers
logistics
planning
public
queue
management
population
roads
airports
bridges
Word
design
traffic
highways
pavement
roads
signal
vehicle
speed
horizontal
signals
based
braking
capacity
multilane
theory
transportation
vertical
ECM - junior
Count
39
35
23
20
18
14
14
13
12
12
11
9
9
8
7
7
6
6
6
6
6
6
6
Word
traffic
design
vehicle
vertical
curve
highway
horizontal
performance
population
road
safety
trip
flow
generation
measured
pavement
signal
example
highways
route
Count
22
15
7
6
5
5
5
5
5
5
5
5
4
4
4
4
4
3
3
3
356
357
Analyzing the concepts based on their level in the concept map, we have found some interesting
358
findings too. As mentioned previously, primary concepts are closer in their position in the
359
concept map to the core concept of TE. This also tells us that in the thinking process students
360
thought of them first, thus potentially giving them more significance. As in previous cases,
361
analyzing by level we have verified that concepts such as traffic, road transportation and
362
infrastructure construction are of a great significance among students’ preconceptions since they
363
are the most frequently among primary concepts. However, one can observe that the greatest
364
variance is in tertiary terms. Students often used tertiary concepts to provide examples for
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365
primary or secondary concepts, so one might conclude that beside similarity of the most
366
important concepts there are potentially different students’ perspectives on them.
367
Table 7 below presents the least frequent concepts from BCM and ECM. The column with BCM
368
concepts shows that students had used mostly general concepts related to general engineering or
369
as transportation users. On the opposite, concepts presented in the ECM column are more
370
directly related to specific aspects of TE that were a part of the course curriculum.
371
Table 7: Least frequent concepts for BCM and ECM
BCM
ECM
acceleration, access, aircrafts,
airplanes, airport, alignment, analysis,
architecture, area, CAD, capacity,
center, city, commercialization,
computer, concrete, conditions,
conveyors, culture, demand,
destinations, devices, disaster, draft,
dynamics, economics, emissions,
environment, errors, ethics, foundations,
freight, funding, geology, grade,
guidance, guidelines, hill, hypothesis,
idea, improvements, jobs, labor, lakes,
land, lanes, limit, limitations, logic,
mall, mass, material, method, metro,
mode, money, mountains, number,
objective, observation, obstacles, online,
optical, optimal, order, organized,
outcomes, overloaded, parallel,
passenger, path, pedestrians, persons,
prevention, probability, problem,
processes, profit, purpose, quantity, rail
track, ramps, regulations, ridership,
route, runways, satisfaction, ship,
signal, signs, simulation, sinage,
software, stakeholders, stoplights,
strategy, structures, sustainability,
terminals, timeliness, topography,
traveling, trucks, users, variables, way,
weight (1)
accessibility, accidents, aerodynamic, air, airplane,
alignment, areas, arrivals, attitude, automobile, aviation,
background, barrier, base, behavior, breaking, budget,
building, cargo, chart, cities, congestion, coordination,
crashes, crest, daily, data, deceleration, delay, departures,
diagrams, direct, distribution, distributions, division, drag,
driver, economy, efficiency, emissions, enforcement,
engineer, eyesight, factor, failure, fares, FIFO, foot, forecast,
formula, free, freight, frequency, fuel, fundamentals,
geometrical, goods, grades, gravitational, green, guide,
guidelines, industrial, information, infrastructures, jam, jet,
jobs, land, life, lifespan, LIFO, light, lights, limit, lines,
local, logistics, macro, macroscopic, management,
markings, material, materials, measurements, method, micro,
microscopic, model, motive, multi, numbers, observation,
optimal, optimization, optimized, overloaded, parameter,
passenger, pavements, pedestrians, perception, planes, police,
pollution, ports, power, prediction, preparation, priority,
problem, procedures, products, project, psychology, rail, rails,
railways, ramps, random, rate, recreation, regional,
regulations, reliability, research, resistances, resources,
ridership, ring, runways, safe, sag, schedules, scientist,
section, semi, serviceability, services, ships, sight, situation,
slab, snow, spacing, specification, SSD, standard, standards,
structure, study, subway, surface, survey, surveys,
sustainability, systems, tandem, taxes, taxi, technology, trains,
traveling, trolley, understandable, values, variables, variety,
wasted, water, width, yearly, yellow (1)
372
373
In-depth Analysis of Individual Student’s Preconceptions
374
In order to perform a detailed analysis of student’s preconceptions, the research team has used
375
NVivo to run query analysis. Using queries, one can distinguish between concepts that have
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Paper revised from original submittal.
376
specific levels of relatedness or connectedness, and thus can identify positive or negative
377
preconceptions.
378
Positive preconceptions
379
Positive preconceptions were all those concepts coded to have high relatedness and
380
connectedness values. As a part of positive preconceptions, students were frequently relating
381
construction, infrastructure management, geography or topography to TE activities. In addition,
382
students recognized the importance of transportation for society (e.g., economy, jobs, budget),
383
and safety as a very important goal of transportation systems. Besides this, students frequently
384
recognized there are different transportation modes, and established a relation between TE and
385
mass transit. As a part of less frequent but positive preconceptions, students recognized the
386
relation between physics and TE, considering the focus of TE on different vehicles. Furthermore,
387
mostly ISE students recognized the role of traffic control, the effects of congestion, a focus of
388
transportation on the movement of people or goods, and some specific tasks, such as finding
389
optimal routes or optimization.
390
Negative preconceptions
391
Beside positive, as potentially more important preconceptions are wrong or incomplete concepts.
392
Querying for negative preconceptions, the research team has selected concepts that were
393
matched to have relatedness of one (no relation) or two (very little relation). As first important
394
misconception, it was interesting to see that students frequently related traffic and efficiency to
395
negative connotations (e.g., traffic means bad flow, traffic causes pollution, traffic causes
396
accidents, traffic means wasted resources, delay, wasted time,). In addition, important
397
misconceptions were related to traffic as a physical phenomenon (e.g., traffic flow as the amount
398
of vehicles on the road, traffic control as the flow of traffic). Furthermore, CEE and ISE students
399
related TE activities with enforcement, regulation, laws, or dealing with clients.
400
It is important to note that students, although recognizing some individual concepts related to
401
TE, have lacked a holistic perspective, established wrong or overgeneralized relations among
402
elements (e.g., TE is affected by new technology), mentioned non-relevant concepts (e.g., optical
403
instrument), and used non-engineering terminology (e.g., timeliness, implemented carefully). It
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TRB 2014 Annual Meeting
Paper revised from original submittal.
404
was interesting to see that some younger students did not have clear understanding of the
405
relations between planning, design, operations, construction, and maintenance.
406
Change of concepts after the course
407
Although students’ concepts in ECM were in general more specific to TE, one important
408
negative effect has to be noted here. In BCM, students frequently mentioned airports, trains,
409
boats, or other concepts related to non-road transportation modes. However, the number of
410
concepts related to non-road transportation modes have been reduced by more than half in ECM.
411
Figure 4 below shows an overall change in the relatedness and connectedness of concepts in
412
BCM and ECM. It can be observed that significant number of the concepts in BCM are assessed
413
as having relative very good or lower relatedness and connectedness. On the other hand,
414
concepts in ECM were mostly assessed as having excellent relatedness. The highest number of
415
concepts in ECM is with only fair connectedness, which can be attributed to the content of the
416
course. Introductory course develops relation towards specific TE concepts, but does not
417
completely establish strong relations among them.
250
Number of Concepts
200
150
BCM
100
ECM
50
R1C1
R1C2
R1C3
R1C4
R1C5
R2C1
R2C2
R2C3
R2C4
R2C5
R3C1
R3C2
R3C3
R3C4
R3C5
R4C1
R4C2
R4C3
R4C4
R4C5
R5C1
R5C2
R5C3
R5C4
R5C5
0
Coded Relatedness and Connectedness Value
418
419
Figure 4: Number of concepts with specific coded relatedness or connectedness value
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Paper revised from original submittal.
420
LESSONS LEARNED AND COURSE DESING IMPLICATIONS
421
Considering the analysis presented in the previous section, following are the major points related
422
to students’ preconceptions:
423

Students had entering preconceptions relating TE to all transportation modes, infrastructure
424
design and construction, traffic, safety, mass transit, and economy. Most of the primary
425
concepts among students were similar, but tertiary concepts were different, thus potentially
426
pointing out at differences in individual perspectives.
427

safety.
428
429


432
433
ISE students had strong preconceptions about traffic flow, data and its analysis, logistics,
queuing theory, and efficiency as related to TE.

434
435
ME students had a strong preconception about air and rail transportation as related to TE. In
addition, similar to CEE students, ME students related safety to TE activities.
430
431
CEE students had strong preconceptions that related TE to infrastructure construction and
Senior students had preconceptions related to specific engineering activities (e.g., design),
while sophomore and junior students had preconceptions related to transportation as users.

Students conceptions about TE have been expanded and were made more similar at the end
436
of the course, although there have been concepts that have evolved based on students major
437
or year. Furthermore, students’ preconceptions that related all transportation modes to TE
438
activities have been modified with a focus only on road transportation.
439

Students had negative preconceptions related to efficiency of transportation systems,
440
misunderstood traffic as a psycho-physical phenomenon, or related TE to marginal concepts,
441
such as police enforcement, laws, or dealing with clients. Some of the important missing
442
preconceptions noticed relate to driver psychology and Intelligent Transportation Systems
443
technologies.
444
Knowledge about these preconceptions can be used in several different ways. For example, this
445
information can be used to develop group discussion-based activities that involve students with
446
different preconceptions, in order to create a beneficial knowledge overlap. Furthermore,
447
identifying critical preconceptions that need to be expanded (e.g., strong relation between TE and
448
construction) can lead to development of additional learning units based on other scientific
449
disciplines (e.g., statistics, optimization, control theory, sociology, etc.).
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Paper revised from original submittal.
450
CONCLUSION
451
The need for this research has been motivated by the consideration that people construct new
452
knowledge based on the previous understandings and beliefs about important concepts.
453
Consequently, ignoring students’ preconceptions can result to negative effects on learning
454
outcomes intended by curriculum. This paper focuses on investigating students’ preconceptions
455
in the introductory transportation-engineering course. Focus of the course is on road
456
transportation engineering, with a wide range of student learning outcomes. As a part of this
457
research, a methodology was devised for instrumenting, coding, and analyzing concept maps, as
458
tools for preconception assessment. As a result of this research, some of the major points related
459
to student preconceptions were identified.
460
All the identified preconceptions verify the assumption that entering knowledge base can affect
461
preconceptions about TE, and consequently affect the learning outcomes. This provides a good
462
starting point for other researchers that recognize the need to examine students’ preconceptions
463
in introductory transportation engineering courses. The research methodology presented here can
464
be used as a template for methodological development and further investigations of
465
preconceptions across universities.
466
Implications for Further Research
467
Considering that one potential limitation of this study is the number of participants, further
468
research should try to involve greater number of students for investigating their preconceptions.
469
Furthermore, considering the diversity in majors, it would be beneficial to investigate further
470
preconceptions of students majoring in Civil Engineering, bearing in mind they constitute the
471
majority of future transportation workforce. This need is supported by the fact that the majority
472
of preconceptions related to transportation systems originates from ISE students, while CEE
473
students have mainly preconceptions related to transportation infrastructure.
474
One of the points from this research was that student preconceptions about TE, as including
475
several transportation modes, were modified during the course. As previous research [3]
476
mentions, this leads to the old debate of breadth vs. depth in transportation education. The
477
question of the focus of transportation education becomes even more important taking into
478
consideration the development of Intelligent Transportation Systems technology and consequent
479
evolving paradigms in transportation engineering profession. Considering this unclear focus of
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Paper revised from original submittal.
480
TE profession, there needs to be greater research that can potentially include expert opinions on
481
important introductory concepts in transportation engineering (similar to methodology in [5]).
482
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