1. AN ASSESSMENT OF PERFORMANCE BETWEEN ON- AND OFF- CAMPUS
STUDENTS IN AN AUTOMATIC IDENTIFICATION AND DATA CAPTURE
COURSE
Stephen J. Elliott1; Eric P. Kukula 2, & Nathan C. Sickler3
Abstract  Universities have been exploring distance Distance education as a supplement to classroom
education for years, but typically do not offer program instruction is common practice in many traditional higher-
courses that require laboratory exercises. Had they education institutions. Instructors deliver classroom
offered such courses, off-campus (distance) students lectures, but homework assignments and supplemental
would have been academically disadvantaged by the material may be retrieved online. This approach is
inability to participate in hands-on laboratory exercises. feasible because these institutions have sufficient numbers
Therefore, an undergraduate course in Automatic of on-campus computers with Internet access. The
Identification and Data Capture was designed to residence halls typically accommodate students’ personal
accommodate distance students and ensure a laboratory computers by offering in-room Internet access, while
experience comparable to on-campus students. The students without personal computers can avail themselves
increased availability of technological advances of on-campus computer laboratories.
(broadband Internet) provides an opportunity for distance Distance education as a stand-alone form of
students to gain comparable knowledge. This paper instruction is not as common, but its usage has grown
outlines the experiences of three groups of students tremendously in the past ten years; note the flourishing
completing laboratory experiments: on-campus students, numbers of online institutions offering degree programs.
undergraduate distance students, and graduate distance Many students choose this type of education because full-
students. The results showed that students who interacted time jobs, other commitments, or physical limitations
with equipment, experience the same level and extent of prevent their participation in more traditional approaches
learning, whether interaction with the AIDC equipment to instruction. This approach requires the student to have
occurred on-campus or remotely; but a lack of interaction a reliable computer and Internet connection. In addition,
resulted in test scores that were statistically different. providing a new channel for homework and learning
opportunities not limited by time or location is required.
This approach to education offers numerous benefits:
Index Terms  curriculum development, automatic
identification and data capture, distance education • Accommodates different learning styles and
schedules
INTRODUCTION • Uses various educational resources or media (e.g.,
paper-based, video, audio, online material) as
Distance education is popularly understood to
instructional tools
mean the planned learning or educational processes that
• Allows usage of multiple communication methods
occur with the instructor and student separated either
geographically or in time, requiring special methods for (e.g., e-mail, teleconference, video conference,
course design, instruction, communication, and instant messaging)
administrative tasks [1-2]. Distance education is often • Supports self-directed and self-paced learning style
used in two ways: and method [1-5].
• As a supplement to brick-and-mortar (on-campus) It is common to consider distance education in
instruction two distinct categories of instruction: synchronous and
asynchronous [2,5]. Synchronous instruction focuses on
• As a stand-alone form of instruction
group work at a distance. It requires all students and
instructors to interact simultaneously. The advantage of
1 Stephen Elliott, Ph.D., Assistant Professor, Biometrics Standards Performance, and Assurance Laboratory, Department of Industrial Technology, Purdue
University, 401 North Grant Street, West Lafayette, IN 47906, USA, elliott@purdue.edu
2 Eric Kukula, Research Assistant, Biometrics Standards Performance, and Assurance Laboratory, Department of Industrial Technology, Purdue University,
401 North Grant Street, West Lafayette, IN 47906, USA, kukula@purdue.edu
3
Nathan Sickler, Research Assistant, Biometrics Standards Performance, and Assurance Laboratory, Department of Industrial Technology, Purdue
University, 401 North Grant Street, West Lafayette, IN 47906, USA, sicklern@purdue.edu
©2006 WCCSETE March 19 - 22, 2006, São Paulo, BRAZIL
World Congress on Computer Science, Engineering and Technology Education
93

2. synchronous instruction is that it occurs in real time, well as laboratory exercises – both the presentation and
giving participants the “feel” of a traditional classroom; the content of the module components had to be
questions can be asked and answered in an immediate scrutinized. A review of literature showed that there were
manner, and the class interacts as a group. A disadvantage a number of ways to provide laboratory experiences to
of synchronous instruction is that it requires everyone to distance students, including LabVIEW™, Virtual
be available at the same time, as well as have access to Network Computing software [7,8], or providing the
the necessary communication and / or visualization software on a CD-ROM for students to execute at home.
infrastructure, which may not be feasible due to A combination of the latter two strategies was chosen.
technological, financial, or scheduling constraints.
Asynchronous instruction, on the other hand, is flexible. It Lecture Material
allows students to choose their own instructional time To provide students with the lecture material,
frame and complete learning materials according to their and to ensure that both distance and on-campus students
schedules [2,5]. Asynchronous instruction allows for received the same lecture material, each on-campus
multiple learning levels and schedules, as this category lecture was video-taped, digitized, and posted to a
makes use of video- or audio-taped lectures, e-mail, and streaming server. Additionally, distance students were
Internet-based programs, all of which are time- provided with a CD that contained the software necessary
independent [2,4,5]. Additionally, disciplines that to remotely connect to designated on-campus host
structure courses by supplementing lectures with computers, as well as software or videos for specific
laboratory exercises offer enhancements in the way of laboratory exercises (see discussion below). For a more
related lecture material [6]. detailed description on the course material see [9].
Equipment requirements
TRANSFORMATION OF THE LABORATORY INTO
All the laboratory activities were evaluated to
A VIRTUAL ENVIRONMENT
ensure their remote accessibility via the Internet. There
were three categories of laboratory activity remote
General Course Structure accessibility:
Initially, all of the lecture assignments,
• The laboratory activities were equivalent (no
laboratory assessments, and exams in IT 345 Automatic
differences were detected between the coursework
Identification and Data Capture (AIDC) were exclusively
that distance and on-campus students would
paper-based. When the university adopted the use of
complete)
WebCT Campus Edition™ course software, additional
• The laboratory was not convertible (i.e., students had
material was developed to take advantage of the new
to physically interaction with equipment)
technology. For the fall 2003 semester, the instructors
migrated the course to a truly online environment • The laboratory was converted with some
(WebCT Vista™), dividing the course into modules, each modifications.
of which covers an individual AIDC technology. Course
material was available electronically; tests were graded Table 1 shows the results of the assessment of the
automatically by the course management system, when course’s various laboratory activities.
applicable. The WebCT Vista™ portal was instrumental
in the department’s ability to offer this course to distance TABLE I
CONVERTIBILITY OF LABORATORY ACTIVITIES FOR DISTANCE
students; the technology made it possible for distance STUDENTS
students to complete and submit material at their Convertible
Not
convenience. Nevertheless, there remained two main Laboratory Activity Equivalent
Convertible
with
issues to be addressed: Modifications
IT 345 On Line Pre- X
Test
• How would the instructor present lecture material? POSTNET Bar Code X
• How would the instructors deliver and allow students — Practical
to complete the laboratory activities? Data Density — Post- X
Test
Linear Bar Code — X
Adapting the Course Post-Test
Each of the AIDC technology modules was PDF 417 — Practical /
Post-Test X
assessed to see whether any modification would be Data Matrix —
needed to enable distance students to participate fully in Practical / Post-Test X
the laboratory activity. The modules included lectures as Verification — X
©2006 WCCSETE March 19 - 22, 2006, São Paulo, BRAZIL
World Congress on Computer Science, Engineering and Technology Education
94

3. Not
Convertible Performance measures examined included pre-test scores,
Laboratory Activity Equivalent with total module scores, module scores of material not
Convertible
Modifications
Practical / Post-Test requiring laboratory activities (no laboratory required),
module scores of material supported by laboratory
Verification — Color
Post-Test X activities that could NOT be modified for distance
Configuration Lab — (laboratory unconvertible), module scores of material
Argox Practical X supported by laboratory activities that could be modified
Configuration Lab — for distance (laboratory with distance modification), as
Quadrus Practical X
Configuration Lab —
shown in Table 1, and combined average exam scores.
Depth-of-Field Additionally, all non-zero scores were used in the
Practical X calculations. Zero scores resulting from students missing
iButton® — Practical / exams, assignments, or assessments were removed to
Post-Test X
prevent the artificial lowering of any group’s
Magnetic Stripe —
Practical / Post-Test X performance.
RFID Evaluation —
Practical X Pre-test scores
Voice Recognition — X
Post-Test An analysis of each group’s pre-test scores was
Hand Recognition — X performed to determine if any statistically significant
Post-Test difference existed between the groups’ starting knowledge
Hand Software — PT2 of the course material. The on-campus students had a
Assessment X
Face Recognition —
mean score of 735.3 and median of score of 736
Cognitec Checkoff X (approximately 41 percent for mean and median).The
Face Recognition — X distance students had a mean score of 714.6 and median
Post-Test score of 720 (approximately 40 percent for mean and
Fingerprint — Post- X median). A comparison of the means and medians proved
Test
that outliers were not influencing the mean score for
Fingerprint — Practical X either group. Furthermore, the results of the ANOVA test
Iris — Panasonic
Authenticam™
indicated that no statistically significant difference existed
Submission X in the starting knowledge of the distance and on-campus
Iris Recognition — X students (p = 0.29). This was important to establish, as all
Post-Test other comparisons were based on the assumption that the
groups started out with similar knowledge. Had the
groups not started out with equal levels of similar
TEST POPULATION knowledge, then the results of further comparisons would
The on-campus IT 345 course had 86 students have been difficult to interpret.
enrolled. The course was divided into eight laboratory
sections, each of which included between 10 and 12 Module score analysis
students. Two teaching assistants taught four sections The total modules score consisted of all students’
each. All on-campus students attended the same lecture post-test, assignment, and practical scores for each
period. module, which were then compared by group. The
The group of distance students consisted of 27 ANOVA test result indicated that a statistically significant
individuals, each of whom had access to a two-hour block difference existed between the distance education and on-
of computing time for performing the laboratory campus students. Specifically, the on-campus students
experiments. Distance students had access to the had an average score of approximately 87 percent,
recording of the lecture that on-campus students received. compared to 80 percent for distance students. This
Graduate and undergraduate distance students were resulted in a p value of 0.001. However, the r2 value
grouped together as distance students. suggested only 10.10 percent of the difference could be
explained by the total modules score alone. Therefore, it
RESULTS was desired to determine were the differences were
occurring.
One-way ANOVA tests were conducted to Module scores were divided into three types,
identify and compare various performance measures of parallel to the “modification required” categories denoted
both distance education and on-campus students. Each in Table 1. An ANOVA test was run to compare the
ANOVA was used to test for statistically significant module scores between the groups when no laboratory
differences between the performance of distance activities were required. The results indicate that no
education and on-campus students using α =.05.
©2006 WCCSETE March 19 - 22, 2006, São Paulo, BRAZIL
World Congress on Computer Science, Engineering and Technology Education
95

4. statistical difference exists. However, it is interesting to REFERENCES
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In assessing the overall performance of the
students in the course, it is interesting to note that the on-
campus and distance students performed as well on their
exams. The combined average exam score is the resulting
average score of the three exams administered throughout
the semester. The ANOVA test result indicates no
statistical difference; in fact, the averages are almost
identical, resulting in a p value of 0.922.
CONCLUSIONS
This paper was written to understand whether
there were any differences in how and whether the
material in an Automatic Identification and Data Capture
course is learned by two different populations. Results of
the module score analysis indicate that a statistically
significant difference exists between the distance
education and on-campus students. However, upon
module breakdown (whether it had been modified for
distance) and further comparison, the results indicate that
the difference in overall module scores can be attributed
to instances of modules containing laboratory activities
that could not be modified for distance education. In the
other two instances, where no laboratory exercises were
required, and in laboratory exercises with distance
modifications, the groups exhibited results that were not
statistically different.
©2006 WCCSETE March 19 - 22, 2006, São Paulo, BRAZIL
World Congress on Computer Science, Engineering and Technology Education
96