Creative Education
2012. Vol.3, Special Issue, 773-783
Published Online October 2012 in SciRes (http://www.SciRP.org/journal/ce) http://dx.doi.org/10.4236/ce.2012.326116
Copyright © 2012 SciRes. 773
Student-Centered Learning Objects to Support the Self-Regulated
Learning of Computer Science
Ali Alharbi, Frans Henskens, Michael Hannaford
School of Electrical Engineer ing and Computer Science, The University of Newcastle , Newcastle, Australia
Email: Ali.H.Alharbi@uon.edu.au, Frans.Henskens@newcastle.edu.au, Michael.Hannaford@newcastle.edu.au
Received August 30th, 2012; revised September 3 0th, 2012; accepted Octob e r 14th, 2012
The most current computing curriculum guidelines focus on designing learning materials to prepare stu-
dents for lifelong learning. Under the lifelong learning paradigm, students are responsible for controlling
and monitoring their learning processes. This undoubtedly includes the ability to choose suitable learning
materials. Correspondingly, instructional paradigms are shifting from teacher-centered to more stu-
dent-centered models that require students to be self-regulated learners. On the other hand, recent trends
in learning materials’ instructional design focus on moving toward the concept of Learning Object-based
instructional technology. A learning object is a unit of instruction with a specific pedagogical objective
that can be used and reused in different learning contexts. Designing learning objects to support students
in their self-regulated learning is not an easy task due to the lack of underlying pedagogical frameworks.
It is difficult to find learning objects related to students’ specific preferences and requirements. In this
study, a number of learning objects are designed to support the self-regulated learning of programming
languages concepts based on the theory of learning styles. Students’ interactions with these learning ob-
jects are managed using an online learning object repository. The repository helps students identify their
preferred learning styles and find the relevant learning objects. The results of the evaluations of these
learning objects revealed that students perceive them to be easy to use and effective in supporting their
learning about different programming languages concepts.
Keywords: Learning Objects; Learning Styles; Self-Regulated Learning; Computer Science Education
Introduction and Problem Statement
The current trend in instructional design is moving toward
the concept of the “learning object”. Learning objects are
learning materials with pedagogical objectives that are intended
for use and reuse in different learning contexts (Sosteric &
Hesemeier, 2002). The number of learning resources stored in
online learning object repositories has increased dramatically.
Many learning object repositories that store a large number of
learning objects for use in different disciplines are available
today. Learning objects are designed to be used by students and
instructors in their learning and teaching. Therefore, pedagogy
should be the primary factor taken into consideration in the
design and delivery of learning objects to learners. Learning
theories are at the core of any pedagogical framework, and
learning objects represent a new era in instructional technology
that aims to support teaching and learning in many disciplines.
On the other hand, in recent years, the concept of self-regu-
lated learning has received increasing attention in educational
research, especially in higher education. Self-regulated learning
provides students with the ability to control and monitor their
learning processes and determine how to locate the suitable
learning resources (Pintrich & Zusho, 2007). Self-regulated
learning is an active topic in education research due to its im-
portance to academic success and lifelong learning (Dettori &
Persico, 2008). Correspondingly, instructional paradigms are
shifting to more student-centered instead of teacher-centered
models (Berglund et al., 2009). This implies that students must
become more self-regulated learners. The recent computing
curricula guidelines produced by the IEEE/ACM stress the
importance of designing computer science learning materials
that help prepare computer science students for lifelong learn-
ing (Sahami, Guzdial, McGettrick, & Roach). To this end, the
learning materials should take the diversity of students’ prefer-
ences and requirements into consideration. However, there is a
scarcity of instructional design theories that guide the design
and use of learning objects (Wiley et al., 2004).
The primary goal of learning object instructional technology
is to simplify and enhance the process of the instructional de-
sign and distribution of learning materials (Wiley et al., 2004).
In its simplest form, instructional design theory is about helping
people learn better; it involves the processes of analyzing
learners’ requirements and designing learning materials to sat-
isfy these requirements (Reigeluth, 1999). The instructional
design of learning objects differs significantly from the conven-
tional instructional design process. Shifting toward self-regu-
lated learning paradigms stresses the importance of designing
learning objects that are compatible with students’ needs and
preferences and improving students’ interactions with these
objects.
Theoretical Background
Learning Object Instructional Technology
Different definitions of the term “learning object” can be
found in the literature. The IEEE Learning Technology Stan-
dards Committee (LTSC) define the learning object as “any
entity, digital or non-digital, which can be used, re-used or
A. ALHARBI ET AL.
referenced during technology supported learning” (LTSC,
2002). This definition is broad, suggesting that anything used
for education could be considered a learning object. However,
many of the subsequent definitions of the term “learning ob-
ject” are attempts to narrow the scope of the IEEE LTSC defi-
nition. Wiley excluded non-digital items from the IEEE defini-
tion, describing a learning object as “any digital resource that
can be reused to support learning” (Wiley, 2000). Sosteric and
Hesemeier (Sosteric & Hesemeier, 2002) synthesized the vari-
ous definitions of learning object, defining a learning object as
any item with a pedagogical objective that is intended for used
and reuse in different learning contexts.
A learning object is a collection of learning items such as
Feature of Intere st Supported example s fr om stude nts’
responses
Step-by-step
description of the
dynamic proce ss
“The ability to step through each process
of a concept with an explanation and
visual display”
“Everything is clearly explained step by
step”
“The flow of information is what I like
the most”
Highlighting of
interesting events
“Highlighting specific components to
show their isolation”
“Highlighting and shadowing allowed
consistent illustrations of the ideas and
sound; much better and easier than
drawing on a b oard”
Visual representation
of abstract concepts
“I think the vi sual animations were very
nice”
“Easier to see what is going on”
“Yes. The visual anima tions do help with
understanding different concepts. It is
good to see how the data are stored”
The user’s ability to
control the animation
“the implementation of student contro ls
were also useful ideas for self-learning”
“changing the speed of the animation is
useful for learning at my own pace”
Textual description of
each step in the
animation
“The descriptions of what was happening
were also very helpful”
“The description at t he bottom o f what is
happening was also useful”
learning process by increasing students’ motivation to use the
learning materials. First, the study revealed that the students in
the experimental course had diverse learning styles. The major-
Copyright © 2012 SciRes. 781
A. ALHARBI ET AL.
ity of students were visual learners (83%), compared to the
17% who were verbal learners. In addition, the majority of
students were sensing learners (67%) who preferred concrete
examples over abstract concepts, compared to 33% were intuit-
tive learners with a preference for abstract concepts.
The average ratings of the learning objects indicated that
students have positive perceptions toward using these learning
objects to learn about different programming language concepts.
This is reflected by the high overall average rating, which
reaches 4.18 out of 5. Grouping the ratings based on the differ-
ent topics covered in the course shows that the learning objects
related to memory management concepts received the highest
average ratings (4.6), while the learning objects related to the
concepts of inheritance and polymorphism received the lowest
average ratings (3.7). When learning about the memory man-
agement concepts of programming languages, it is essential for
students to see how the data are stored in different sections of
memory. The learning object designed for this study used an-
imations to depict where different variables are stored in the
memory and the dynamic changes in the memory as the pro-
gram executes. Furthermore, the learning objects presented a
comparison between the memory management approaches of
Java and C++. The high ratings assigned to the memory man-
agement learning objects indicated that using the animation to
design these learning objects is educationally effective. The
students identified the features of the learning objects that were
the most useful to them. These include showing the dynamic
changes in the data structure using animations accompanied by
highlighting of the code at each step and including text descrip-
tions during the animation. The results of the students’ percep-
tion questionnaire reveal that the students perceived the learn-
ing objects as easy to use and useful to support their self-regu-
lated learning.
Conclusion and Future Work
The main objective of the research presented in this paper
was to design learning objects to support students in their
self-regulated learning of computer science. The study pro-
posed a framework based on the theory of learning styles to
improve the design of and interactions with learning objects.
Based on this framework, a number of learning objects were
designed to support the students’ learning of certain program-
ming language concepts. All the learning objects were stored in
an online collaborative repository that identified the students’
preferred learning styles and monitored the students’ interact-
tions with the learning objects. The study revealed that the stu-
dents in the course had diverse learning styles. The majority of
students were visual and sensing learners. The result of using
these learning objects during the course revealed that the stu-
dents perceived the learning objects to be easy to use and useful
in supporting their learning about programming language con-
cepts.
It should be noted that the sample size was not very large and
based on this, the result of the study may be valid only in the
area of teaching programming languages. However, the study
used mixed methods of data collection which increases the
validity and the generalization of the resul ts.
The work described in this paper will continue by comparing
the final exam scores of the students who used the learning
objects this semester with those of another group who were
taught using the traditional instructional approach and did not
use the learning objects. In addition, an analysis of the students’
interactions with the learning objects will be conducted. This
will help obtain further insight into the students’ self-regulated
learning behaviors.
REFERENCES
Akdemir, O., & Koszalka, T. (2008). Investigating the relationships
among instructional strategies and learning styles in online environ-
ments. Computers & Education, 50, 1451-1461.
doi:10.1016/j.compedu.2007.01.004
Alharbi, A., Paul, D., Henskens, F., & Hannaford, M. (2011). An inves-
tigation into the learning styles and self-regulated learning strategies
for computer science students. In Proceedings Ascilite 2011 (pp.
36-46). Hobart: Ascilite.
Allert, J. (2004). Learning style and factors contributing to success in
an introductory computer science course. In Proceedings of the IEEE
International Conference on Advanced Learning Technologies (pp.
385-389). Charlotte: SIGCSE. doi:10.1109/ICALT.2004.1357442
Ben-Ari, M., Berglund, A., Booth, S., & Holmboe, C. (2004). What do
we mean by theoretically sound research in computer science educa-
tion? Proceedings of the 9th Annual SIGCSE Conference on Innova-
tion and Technology in Computer Science Education (pp. 230-231).
New York, NY: ACM.
Benaya, T., & Zur, E. (2008). Understanding object oriented program-
ming concepts in an advanced programming course. Informatics
Education—Supp orting Computational Thinking, 5 0 90, 161-170.
doi:10.1007/978-3-540-69924-8_15
Berglund, A., Eckerdal, A., Pears, A., East, P., Kinnunen, P., Malmi, L.
et al. (2009). Learning computer science: Perceptions, actions and
roles. European Journal of Engineering Educa t i o n , 34, 327-33 8.
doi:10.1080/03043790902989168
Bruning, R., Schraw, G., & Ronning, R. (1999). Cognitive psychology
and instruction (3rd ed.). Upper Saddle River, NJ: Prentice-Hall.
Chamillard, A., & Karolick, D. (1999). Using learning style data in an
introductory computer science course. ACM SIGCSE Bulletin, 31,
291-295. doi:10.1145/384266.299790
Coffield, F. (2004). Learning styles and pedagogy in post-16 learning:
A systematic and critical review. London: Learning and Skills Re-
search Centre.
Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should we
be using learning styles. London: Learning and Skills Research Cen-
tre.
De Raadt, M., & Simon (2011). My students don’t learn the way I do.
Proceedings of the 13th Australasian Computing Education Confer-
ence (ACE 2011) (pp. 105-112). Perth: Australian Computer Society,
Inc.
Dettori, G., & Persico, D. (2008). Detecting self-regulated learning in
online communities by means of interaction analysis. IEEE Transac-
tions on Learning Technologies, 1, 11-19.
doi:10.1109/TLT.2008.7
Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles
in engineering education. Engineering Education , 78, 674-681.
Felder, R. M., & Soloman, B. A. (1997). Index of learning styles. URL
(last checked 20 June 2011).
http://www4.ncsu.edu/unity/lockers/users/f/felder/public/ILSpage.ht
ml
Fjuk, A., Bennedsen, J., Berge, O., & Caspersen, M. E. (2004). Learn-
ing object-orientation through ICT-mediated apprenticeship. Pro-
ceedings of IEEE International Conference on Advanced Learning
Technologies (pp. 380-384). Washington, DC: IEEE Computer Soci-
ety. doi:10.1109/ICALT.2004.1357441
Fleury, A. E. (2001). Encapsualtion and reuse as viewed by java stu-
dents. URL.
http://dis.eafit.edu.co/depto/docu mentos/p189-fleury%20-%20Encapsu
lation%20and%20Reuse%20as%20Viewed%20by%20Java%20Stu
dents.pdf
Hadar, I., & Leron, U. (2008). How intuitive is object-oriented design?
Communications of the ACM, 51, 41-46.
Copyright © 2012 SciRes.
782
A. ALHARBI ET AL.
Copyright © 2012 SciRes. 783
doi:10.1145/1342327.1342336
Holland, S., Griffiths, R., & Woodman, M. (1997). Avoiding object
misconceptions. ACM SIGCSE Bulletin, 29, 131-134.
doi:10.1145/268085.268132
Holmboe, C., McIver, L., & George, C. (2001). Research agenda for
computer science education. In Proceedings of PPIG (pp. 207-223).
Bournemouth: 13th Workshop of the Psychology of Programming
Interest Group.
IEEE/ACM (2005). Computing curricula 2005: The overview report.
URL (last checked 20 June 2011).
http://www.acm.org/education/education/curric_vols/CC2005-March
06Final.pdf
Keefe, J. W. (1988). Profiling and utilizing learning style. Reston, VA:
NASSP Learning Style Series.
Liberman, N., Beeri, C., & Ben-David Kolikant, Y. (2011). Difficulties
in learning inheritance and polymorphism. ACM Transactions on
Computing Education (TOCE), 11, 4.
doi:10.1145/1921607.1921611
LTSC (2002). Draft standard for learning object metadata. URL (last
checked 4 July 2011).
http://ltsc.ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf
Mills, J., Ayre, M., Hands, D., & Carden, P. (2010). Learning about
learning styles: Can it improve engineering education? Mountain
Rise, 2, 16.
Milne, I., & Rowe, G. (2002). Difficulties in learning and teaching
programming—Views of students and tutors. Education and Infor-
mation Technologies, 7, 55-66. doi:10.1023/A:1015362608943
Or-Bach, R., & Lavy, I. (2004). Cognitive activities of abstraction in
object orientation: An empirical study. ACM SIGCSE Bulletin, 36,
82-86. doi:10.1145/1024338.1024378
Pintrich, P., & Zusho, A. (2007). Student motivation and self-regulated
learning in the college classroom. The Scholarship of Teaching and
Learning in Higher Education: An Evidence-Based Perspective, 3,
731-810. doi:10.1007/1-4020-5742-3_16
Pritchard, A. (2009). Ways of learning: Learning theories and learning
styles in the classroom (2nd ed.). London: David Fulton Publishers.
Ragonis, N., & Ben-Ari, M. (2005a). A long-term investigation of the
comprehension of OOP concepts by novices. Computer Science
Education, 15, 203-221
Ragonis, N., & Ben-Ari, M. (2005b). On understanding the statics and
dynamics of object-oriented programs. ACM SIGCSE Bulletin, 37,
226-230.
Reigeluth, C. M. (1999). What is instructional-design theory and how is
it changing. Instructional-Design Theories and Mod els, 2, 5-29.
Sahami, M., Guzdial, M., McGettrick, A., & Roach, S. (2011). Setting
the stage for computing curricula 2013: Computer science—Report
from the ACM/IEEE-CS joint task force. In Proceedings of the 42nd
ACM Technical Symposium on Computer Science Education (pp.
161-162). New Yo rk: ACM.
Sosteric, M., & Hesemeier, S. (2002). When is a learning object not an
object: A first step towards a theory of learning objects. The Interna-
tional Review of Research in Open and Distance Learning, 3, 2.
Thagard (Ed.) (2010). The Stanford encyclopedia of philosophy. Palo
Alto, CA: Stanford University.
Thomas, L., Ratcliffe, M., Woodbury, J., & Jarman, E. (2002). Learn-
ing styles and perf ormance in th e introductory progra mming sequence.
ACM SIGCSE Bulletin , 34, 33-37. doi:10.1145/563517.563352
Watson, J. (1997). Behaviorism. New Brunswick, NJ: Transaction Pub-
lishers.
Weinstein, C. E., & Acee, T. W. (20 08). Cognitive view of learning. In
N. Salkind, & K. Rasmussen (Eds.), Encyclopedia of educational
psychology. New York: Sage Publications.
Wiley, D. (2000). Connecting learning objects to instructional design
theory: A definition, a metaphor, and a taxonomy. Learning Tech-
nology, 2830, 1-35.
Wiley, D. (2002). Learning objects need instructional design theory. In
A. Rossett (Ed.), The ASTD e-Learning Handbook (pp. 115-126).
New York, NY: McGraw-Hill.
Wiley, D., Waters , S., Dawson, D. , La mbert, B., Barcla y, M., Wad e, D.,
et al. (2004). Overcoming the limitations of learning objects. Journal
of Educational Multimedia and Hyp er m ed i a , 1 3 , 507-521.
Zander, C., Th omas, L., Simon, B., Murphy, L., McCau ley, R., Hanks,
B. et al. (2009). Learning styles: Novices decide. ACM SIGCSE Bul-
letin, 41, 223-227.