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Updating Student Profiles in Adaptive Mobile Learning using ASP.net MVC, dotNetRDF, Turtle, and the Semantic Web

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In this paper, we present a conceptual framework for using the Semantic Web to get student data from other educational institutions, enabling the educational institutions to communicate and exchange student data. Educational institutions could then use this information to adjust the students' profiles and modify their learning paths. Semantic Web will create a more personalized dynamic course for each student, according to his/her ability, educational level, and experience.
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PaperUpdating Student Profiles in Adaptive Mobile Learning using ASP.net MVC, dotNetRDF,…
Updating Student Profiles in Adaptive Mobile Learning
using ASP.net MVC, dotNetRDF, Turtle,
and the Semantic Web
https://doi.org/10.3991/ijim.v11i3.6165
Dr. Samir E. Hamada
Farmingdale State College, Farmingdale NY, USA
shamada@bridgeport.edu
Dr. Ibrahim Alkore Alshalabi
Ma'an Community College, Al-Balqa Applied University, Jordan.
ialkorea@my.bridgeport.edu
Dr. Khaled Elleithy
University of Bridgeport, Bridgeport CT, USA
elleithy@bridgeport.edu
Dr. Ioana Badara
University of Bridgeport, Bridgeport CT, USA
ibadara@bridgeport.edu
Dr. Saeid Moslehpour
University of Hartford, Hartford CT, USA
moslehpou@hartford.edu
AbstractIn this paper, we present a conceptual framework for using the
Semantic Web to get student data from other educational institutions, enabling
the educational institutions to communicate and exchange student data. Educa-
tional institutions could then use this information to adjust the students’ profiles
and modify their learning paths. Semantic Web will create a more personalized
dynamic course for each student, according to his/her ability, educational level,
and experience.
KeywordsAdaptive Learning; dotNetRDF; Graphs; mLearning; Ontology;
RDF; Semantic Web; Shortest Path; Turtle; User Profile.
1 Introduction
Throughout the most recent decades, numerous research studies have examined the
possibilities of changing the educational instruction model from the customary one-
size-fits-all model to a more adaptive and customized learning model. Most of the
techniques calculate the optimal learning path, based on the characteristics in the
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student’s profile to make the course more personalized. However, we have not seen
any technique that updates profiles dynamically using the Semantic Web to exchange
information between educational institutions. The student profile contains information
about the student (such as first name, last name, address, course units that have been
completed, and grades of those course units). “eLearning is learning utilizing elec-
tronic technologies to access educational curriculum outside of a traditional class-
room. In most cases, it refers to a course, program, or degree delivered completely
online.” [1].
This current model can be applied to mobile learning and eLearning in community
colleges, as well as, in a typical graduate or undergraduate program at the university
level for any course. Students could benefit from and personalize their college experi-
ence and graduate earlier by completing their requirements. However, this model does
not apply to K-12 students, because they are outside the scope of our research. Adap-
tive learning is an educational method that aids students in the learning process ac-
cording to their needs. In addition, adaptive learning assists instructors in conveying
course content to their students in a personalized manner based on the students’ abil-
ity and background. Furthermore, from a developer’s point of view, adaptive learning
is a technique using computers and other resources to assist in producing a better
learning experience. One of the most challenging tasks for adaptive mobile learning is
to create an adaptive course. Several researchers have used different techniques in
order to make the course adaptive in terms of the course content and units. To the best
of our knowledge, there has been no research that attempts to make the course adap-
tive in terms of previously completed materials by the students at another educational
institution by using the Semantic Web to communicate directly with various educa-
tional institutions’ systems to acquire the students’ profiles.
According to our proposed system, the students complete their profile information
at the time of the course registration. If there is a claim that the student has success-
fully completed a course unit at another educational institution, our system will run a
query against the Semantic Web files; this will be performed using the SPARQL
(SPARQL Protocol and RDF Query Language) [2], where (RDF) the Resource De-
scription Framework, which “is a general-purpose language for representing infor-
mation in the Web.” [3]. For the purposes of our research, we ran the query against
Turtle files (“Terse RDF Triple Language, a concrete syntax for RDF” [4]) on another
website to simulate the other educational institution. We were able to obtain the stu-
dents’ profiles and grades in that course unit. If the grades fell within the accepted
grade range (which is predetermined by each academic institution’s subject matter
expert), then that course unit will be marked as completed. Otherwise, the student will
be presented with a quiz for that course unit. If the student successfully passes the
quiz (it is a computerized quiz provided by our system), the course unit will be
marked as completed. However, if the student does not pass the quiz, the student must
go through the course unit’s learning materials and retake the quiz until he or she
successfully passes the quiz in that course unit.
When students sign-up and complete their profile information during the sign-up
process, they include the completed course units from different educational institu-
tions. The system will then query the Semantic Web files (Turtle) of that institution to
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obtain the student’s profile, verify the student’s grade, and determine if the student
passed the course unit (according to the passing grades imposed by the subject matter
expert of each educational institution using our system). If there is no result to ensure
that the student has passed the course unit, the student must take a quiz to evaluate
his/her knowledge in that course unit. If the student passes the quiz, the course unit
will be marked as completed. Otherwise, the student has to go through that course
unit’s materials and then retake the quiz in that course unit in order to complete it.
In this paper, we propose a technique using ASP.Net MVC, dotNetRDF, Turtle,
and Semantic Web to show how we can exchange information between educational
institutions in order to update student profiles (in terms of the course units that have
been completed) in order to calculate the optimal path for other course units. Student
profiles contain information such as student name, completed course units, and grades
in each course unit. As a result, students do not have to learn the same course unit
more than once. We oftentimes have introductory modules at the beginning of the
course, in which we introduce essential notions/concepts assumed to have been
learned elsewhere. To counter the common problem of students forgetting previously
learned content over time, the system starts by reviewing previously learned concepts
and modules; it then teaches students the new required content in order to finish the
course.
2 Related Work
In 2011, Bhatia and Jain [5] showed that the Semantic Web is an augmentation of
the current Web, in which data are characterized to empower computers and individu-
als to work with better coordination. This coordination will help in our research, be-
cause we will communicate with various educational institutions in order to verify
student claims. In 2013, Hadi et al. [6] stated that the Internet changed the way we
collect and deliver information. In their paper, they expressed that the methodology of
executing RDF queries against the Semantic Web information requires an exact
match between the inquiry structure and the RDF content. They addressed this prob-
lem by converting RDF content into a matrix of features and treated queries as classi-
fication problems. They effectively built up a working model framework that exhibit-
ed the appropriateness of their methodology; this approach might help in our research,
as we will use RDF queries against the Semantic Web data.
In 2013, Soualah et al. [7] stated that new technical capacities exist in the area of
learning, because of the improvements to mobile phones and wireless technologies.
They expressed that mobile learning (m-learning) is a natural extension of eLearning;
it has the ability to make learning available on a wide scale, because of the rapid ad-
vancements in wireless technologies and the broad utilization of mobile devices. They
also stated that learners have different backgrounds and objectives, located in differ-
ent learning environments (heterogeneity of time, learning time, visual support, ambi-
ent noise, etc.). By having more information about the learners, we could adjust the
learning strategies to satisfy every learning need. Their approach consisted of two
levels:
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1. The semantic level aimed to express semantic characteristics of learning content
and learner context.
2. The behavioral level provided users with only the most relevant information.
Their approach made use of learning practices already deployed in eLearning sys-
tems and adapted them to m-learning. This idea is fundamental to our current work,
since the new technical capacities provide a greater number of possible tools for en-
hancing learning. In 2014, Grivokostopoulou et al. [8] stated that eLearning frame-
work is turning into a crucial means of educational delivery. Information delivery is
one of the fundaments of this current study.
In 2015, Walia et al. [9] stated that the Semantic Web approach to eLearning pro-
vides relevant and meaningful information to the learner. Since the human mind de-
velops its own cognitive structure based on personal experiences and background, the
mind is usually ambiguous and inconsistent. It is not difficult to learn and secure
semantically associated information when the domain of knowledge is huge and well-
connected. In this method of eLearning, the Semantic Web adds the human conceptu-
al representation and the mechanism for using the learner profile and experience.
Providing relevant and meaningful information to the learner is fundamental to our
research.
As previously mentioned, various related works have contributed to the foundation
of our research. The following studies addressed security issues of the Semantic Web
that are relevant to our research, (since we have to secure sensitive data). In their
research, Kagal et al. [10] concluded that in order to secure the Semantic Web, the
following two fundamental parts are required: (1) a semantic strategy that character-
izes security necessities and (2) a distributed policy management approach. Further-
more, in the distributed policy management, each entity can determine its own partic-
ular strategy for security and privacy. It is essential for Web entities to have the ca-
pacity to express their security clearly. In order to achieve this end goal, Kagal et al.
utilize a policy language according to a semantic language to markup security infor-
mation for Web entities. Kagal et al., also, developed two security frameworks: one
for distributed environments and one for supply chain management.
In a study by Thuraisingham [11], he provided an overview of the Semantic Web
and discussed security issues. Additionally, he stated that security must apply to all of
the Semantic Web layers. The security of the Semantic Web should start at the begin-
ning of the project. Also, he concluded that there are situations in which 100% securi-
ty should be guaranteed; however, he acknowledged that there are situations that do
not require 100% security. At this stage we have not incorporated any security policy,
because it is not within the scope of our research. However, we intend to incorporate
a security policy in a later stage of our research.
In 2015, Alshalabi, Hamada, et al. [12, 13] demonstrated the Learning Path Graph
(LPG) that is a good representation of online courses in a computer-based usage of an
educational framework. This adaptive learning system is displayed as weighted di-
rected graphs, where each course unit is represented by a node on the graph. The
Learning Path Graph represents the structure of domain knowledge, learning goals,
and all available learning paths as shown in Figure 1.
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Fig. 1. Learning Path Graph.
In this current research project, we implemented an optimal adaptive learning path
algorithm utilizing learner information from the learner profile to enhance specific
end goals. This algorithm provides suitable content sequence in a dynamic structure
for all learners to accomplish their learning goals in the most effective manner. The
optimal path is calculated using our algorithm, which was designed to obtain the low-
est cost between the two course units on the path. The cost is determined by the sub-
ject matter expert. Cost factors include, but are not limited to, the difficulty level of
course units and estimated time required to complete the unit. This study shows how
to optimize an adaptive mobile learning system by using the LPG [12]. Furthermore,
we will demonstrate how to customize student profiles by using the Semantic Web in
order to provide online credit to students for the course units completed in other ac-
credited educational institutions. We, also, describe the conceptual framework of an
adaptive mobile learning system and how student profiles are used to adjust the learn-
ing path, thereby, making the learning path more dynamic. This means that when
students learn a course unit there will be an adjustment to their learning path. A new
optimal path will be generated by the system. The interesting point in this study is the
ability to use the Semantic Web to exchange student information among educational
institutions and to credit students for the course units that they have already complet-
ed. This feature may have the potential to boost the efficiency of the adaptive learning
systems and increase the chance for student success.
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3 Proposal
Ontology, as a term, is derived from the Greek words “onto” (which means being),
and the word “logia (which means written or spoken discourse). Ontology means
different things to different people. In philosophy, it represents the study of the exist-
ence and nature of being. In the Semantic Web, ontologies are formal definitions or
representations of vocabularies or knowledge that allow the user to define resource
classes, resource properties, and relationships between resource class members [14,
15]
Eisenstadt and Vincent [16] said that, “An ontology is a partial specification of a
conceptual vocabulary to be used for formulating knowledge-level theories about a
domain of discourse.”
The three parts of the RDF triple are shown below in Figure 2,
Fig. 2. Resource Description Framework (RDF) Triple.
For example, as shown in Figure 3, the triple "(John) (Knows) (Jane)," (John) is
the subject, (Knows) is the predicate, and (Jane) is the object.
Fig. 3. Resource Description Framework (RDF) Triple Example.
Using Turtle syntax, it can be written as follows as shown in Figure 4.
Fig. 4. Parts of the Triple in Turtle format.
There are several advantages of ontologies including:
1. Publishing data using common vocabulary and grammar
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2. Preserving data semantic descriptions in ontologies
3. Data are ready for inference
4. Better visibility
5. Extensibility
6. Flexibility
7. Visibility
8. Ability to add new properties at any time without breaking compatibility [17, 18].
Table 1 shows a rough interpretation of terms used to describe relational databases
and ontologies.
Table 1. Relational Database and Ontology
Relational database
Ontology
row subject
column
predicate
table data literal nodes
As shown in Figure 5, we have a students’ file in Turtle format and each student
has the following properties:
1. ID
2. Given Name
3. Family Name
4. Email
5. Street Address
6. Address Locality
7. Address Region
8. Postal Code
9. Address Country
10. Student Group
The following are some vocabularies from different schemas:
@prefix d: <http://adaptivemobilelearning.com/ns/data#>
@prefix person:<http://schema.org/Person>
@prefix address: <http://schema.org/>
@prefix place: <http://schema.org/Place/>
@prefix aiiso: <http://purl.org/vocab/aiiso/schema#>
@prefix contains:<http://schema.org/hasPart>
@prefix teach: <http://linkedscience.org/teach/ns#>
@prefix completed: https://schema.org/Completed
With the available schemas listed above, we were able to map to our data files;
they might not be an exact match, but this study was about a demonstration of how to
be able to get student results from another educational institution using Semantic
Web. Figure 6 shows student’s data in Turtle format.
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Fig. 5. Student’s Properties.
Fig. 6. Students’ File in Turtle Format.
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In Figure 7, we are going to use the SPARQL query to select students in Connecti-
cut (CT).
Fig. 7. SPARQL Query for students in CT.
Table 2. Result of query from Figure 7
First
City
State
John
Bridgeport
CT
Joe
Stratford
CT
As shown in Table 2, two students are in Connecticut, John Smith and Joe Bloggs.
Now, we will add one more condition: the city of Stratford as indicated in Figure 8.
Fig. 8. Query for students in city=Stratford and state=CT
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The only student in our table who lives in Stratford is Joe Bloggs, and the result of
running the query confirmed that, as shown in Table 3.
Table 3. Result of query from Figure 8
Last
First
City
State
Bloggs
Joe
Stratford
CT
Implementing dotNetRDF [13, 14, 19]
The dotNetRDF project aimed to create an open source .Net library using the latest
versions of the .Net framework to provide a powerful and easy-to-use API to work
with RDF, SPARQL, and the Semantic Web. The primary goal is to provide an effi-
cient method for working with reasonable amounts of RDF in .Net. Using dotNetRDF
is extremely simple. Reading Turtle files can be done as follows: The following snip-
pet loads the Turtle files to the computer memory, as a structured graph, as shown in
Figure 9.
Fig. 9. Loading the Turtle files to memory.
Figure 10 shows the SPARQL query that is going to be executed on graph g.
Fig. 10. A SPARQL query on the files loaded in Figure 9
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The value of the variable firstName equals “John”, the value of the variable last-
Name equals “Smith” and value of the variable topic equals “Introduction”. The que-
ry is going to display last name, first name, and course unit, where first name equals
John, last name equals Smith, and the course unit equals “Introduction”. The
SPARQL queries can be executed with ExecuteQuery method, as shown in Figure 11.
Fig. 11. Executing Query in Figure 10
The ExecuteQuery method runs the query against a loaded ontology. In the follow-
ing code snippet, we will verify that the query-executed results are not null and then
parse them to a SparqlResultSet. The SparqlResultSet consists of a number of
SparqlResults. Each SparqlResult corresponds to a single fetched "row". We also will
get the count of records and then store it into the ViewData["Count"], which will be
displayed in the view. We will then create a ViewData["Result"] in which we will
store the value “Passed” if the record count is greater than zero; otherwise we will
store “Not Passed.” As shown in Figure 12.
Fig. 12. Evaluating the Query result from Figure 11
4 Development of the Framework
We have performed tests on the system using ASP.Net MVC and the Turtle files:
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1. CourseModules.ttl
2. Courses.ttl
3. Courses_CourseModules.ttl
4. Faculty.ttl
5. FacultyCourses.ttl
6. Students.ttl
7. StudentsCourses.ttl
8. StudentsModules.ttl
We were able to get student information about the completed course units by
providing the parameters, first name, last name, and course unit to the controller via
the view. We then received the results that indicated whether or not a student passed
the course unit. This information could be used to update the student’s profile and
used to adjust the learning path to make it more adaptive, according to the system
diagram of Figure 13.
Fig. 13. System Diagram
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Figure 13 shows when a student registers and completes the questionnaire, if there
are claims about completing successfully a course unit in another educational institu-
tion, the system will query the Turtle files located on that institution’s website to
verify the claim. Once the claim is verified, the course unit will be marked as com-
pleted, and then the system will check to see if the required course units (the units that
need to be completed in the course) were successfully completed. The system will
then mark the course as completed; otherwise, the student has to complete the course
unit quiz successfully in order to mark this course unit as completed. However, if the
student does not pass the quiz, then the student must finish one of the available course
units and take the quiz. Upon passing the quiz successfully, the course unit will be
marked as completed. The system will then check if the required course units to com-
plete this course are successfully completed. The system, afterwards, will mark the
course as completed.
Figures 14 through 17 illustrate the relationship between the student and completed
course units using the relations between the Turtle files.
Fig. 14. Relations 1/4
Fig. 15. Relations 2/4
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Fig. 16. Relations 3/4
Fig. 17. Relations 4/4
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The following Turtle files were used to illustrate the relations on Figures 14
through 17:
1. CourseModules.ttl
2. Courses.ttl
3. Courses_CourseModules.ttl
4. Faculty.ttl
5. FacultyCourses.ttl
6. Students.ttl
7. StudentsModules.ttl
As indicated from these figures, we have a student with the following attributes:
1. ID =122874839
2. Given Name ="John"
3. Family Name = "Smith"
4. Email = "john.smit@developmentstaging.com"
5. Student Group = "Under Graduate"
6. Postal Address:
7. Street Address "221 University Avenue”
8. Address Locality = "Bridgeport"
9. Address Region = "CT"
10. Postal Code = "06604"
11. Country = "USA"
John Smith has completed the following course units:
1. Introduction
2. Arrays
The Introduction and Arrays are parts of the course 390, which has the following
attributes:
1. Study Program = "CPSC"
2. Course Title = "Programming Pact"
3. Building = "Main Campus"
4. ects “Credits” = "6"
5. Student Group = “Undergraduate"
This course has five modules:
1. "Introduction"
2. "If Statement"
3. "Arrays"
4. "Loops"
5. "Sorting Algorithms"
This course has an instructor with the following attributes:
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Given Name = "Jane"
Family Name = "Roe";
Email = "jane.roe@developmentstaging.com"
Teacher = "course"
We could, also, query that the grade is in a specific acceptable range, as described
in the Semantic Web: Real World Applications from Industry Book to using RUD
(University Resource Descriptor) [20], SUD (Student University Descriptor), RQL
(RDF Query Language), RDQL (RDF Data Query Language), SWRL (Semantic Web
Rule Language), Buchingae, SPARQL, as shown in Figure 18.
Fig. 18. Querying Students with GPA > 3.5
5 Experiment
In this section, we present the results of our experiment. For this experiment, we
have used the Network Security course CPEG 561. CPEG 561 is a graduate course
offered as an elective for Computer Science and Computer Engineering students. We
tested two student groups, the AML Group and the Control Group. The AML is the
group that used our system. Both the AML group and the Control Group completed a
Pre-Quiz. The Pre-Quiz included 25 multiple choice questions and true/false ques-
tions. The Post-Quizzes contained three essay questions and the other 5 quizzes con-
tained 66 multiple choice and true/false questions, as follows:
Quiz 1 ! 16 Questions
Quiz 2 ! 12 Questions
Quiz 3 ! 12 Questions
Quiz 4 ! 12 Questions
Quiz 5 ! 14 Questions
Table 4 shows the grades of the AML group and the Control Group for the Pre-
Quiz and Post Quizzes
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Table 4. Students Grades in our Study
AML Group
Control Group
Pre-Quiz
Post-Quizzes
Pre-Quiz
Post-Quizzes
56.00
50.12
72.00
1.56
80.00
53.89
40.00
37.72
56.00
55.10
48.00
41.33
60.00 57.11
84.00 42.78
56.00
61.47
80.00
42.82
64.00 63.91
56.00 43.90
52.00
65.98
64.00
44.37
60.00
67.27
64.00
44.54
84.00
69.13
52.00
47.40
56.00
70.47
84.00
47.98
60.00
71.45
84.00
49.96
36.00
74.44
72.00
52.40
56.00
74.76
72.00
55.58
44.00
77.92
76.00
65.38
48.00
85.35
60.00
71.84
Figures 19 and 20, illustrate the grades of both Groups for the Pre-quiz and the
Post-Quizzes. The graphs clearly show improvement in the AML Group compared to
the Control Group.
Fig. 19. AML Group Improvement
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Fig. 20. Control Group Improvement
Figure 21 illustrates the distribution of the grades in the two groups.
Fig. 21. The distribution of the grades in the two groups
AML Group Average for Post-Quizzes = 66.56
Control Group Average for Post-Quizzes = 45.97
From the previous tables and charts, we can see that the AML Group is more effec-
tive (44.79%) than the Control Group, furthermore, the grades in the AML group are
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steadier than the Control Group. A statistical analysis is conducted, to demonstrate
that the results are statistically significant.
The parameters below are computed in order to decide whether to accept or reject
the hypotheses H0.
Hypothesis Testing
Null hypothesis (H0):
!!"# !!!"#$%"&!!"#$% !
These two groups have the same outcome.
Alternative hypothesis (Ha):
!!
!"# !!!"#$%"&!!"#$%
These two groups do not have the same outcome.
Where:
!
!"#!!"!!"#!!"#!!"#$%!!"#$!
!!"#$%"&!!"#$%!!"!!"#!!"#$%"&!!"#$%!!"#$!
By doing two tails t-value calculations.
The calculated t-value = 4.38
The Critical t-value (with !=0.05) = 2.05
According to the values above, the calculated t-value is greater than the Critical t-
value. We reject the Null Hypothesis, because the calculated t-value of 4.38 is larger
than the Critical value which is 2.05 with 95% confidence level. This proves that the
results are statistically significant.
6 Conclusions
On March 11, 2016, prior to any survey data were collected, an Institutional Re-
view Board (IRB) approval was granted at the University of Bridgeport. The AML
system indicated substantial improvement over the Control System. The experiment
has shown that we could use the Semantic Web with adaptive mobile learning to
enhance the courses, making them more dynamic. The Semantic Web obtains the
information about completed course units, which are applied to the learning path
graph, and a new optimal path is generated. Furthermore, if the student completes the
target module, the student does not have to go through the rest of the modules. We
can also prevent teaching the same course unit for the student more than once. Thus,
the proposed approach could significantly improve the cost effectiveness for the stu-
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dents causing them to better manage their time more efficiently. Since there is a pre-
view component to each course, we made sure that the students have the required
information. The approach presented in this paper is expected to improve the perfor-
mance of adaptive mobile learning and provide a learning experience to students that
is more personalized and dynamic.
7 References
[1] (30 Jan. 2016). What is eLearning? Available: http://www.elearningnc.gov/about_ele
arning/what_is_elearning/
[2] W3.org. (25 Oct. 2015). SPARQL 1.1 Protocol. Available: http://www.w3.org/TR/
sparql11-protocol/
[3] (2016). RDF Current Status Available: https://www.w3.org/standards/techs/rdf#w3c_all
[4] (2016). Turtle - Terse RDF Triple Language. Available: https://www.w3.org/
TeamSubmission/turtle/
[5] C. S. Bhatia and S. Jain, "Semantic Web Mining: Using Ontology Learning and Grammat-
ical Rule Inference Technique," in 2011 International Conference on Process Automation,
Control and Computing (PACC), 2011, pp. 1-6. https://doi.org/10.1109/PA
CC.2011.5979024
[6] A. S. Hadi, P. Fergus, C. Dobbins, and A. M. Al-Bakry, "A Machine Learning Algorithm
for Searching Vectorised RDF Data," in 2013 27th International Conference on Advanced
Information Networking and Applications Workshops (WAINA), 2013, pp. 613-618.
https://doi.org/10.1109/waina.2013.204
[7] F. Soualah-Alila, F. Mendes, and C. Nicolle, "A Context-Based Adaptation In Mobile
Learning," IEEE Computer Society Technical Committee on Learning Technology (TCLT),
vol. 15, p. 5 pages, 2013.
[8] F. Grivokostopoulou, I. Perikos, and I. Hatzilygeroudis, "Utilizing semantic web technolo-
gies and data mining techniques to analyze students learning and predict final perfor-
mance," in 2014 International Conference on Teaching, Assessment and Learning (TALE),
2014, pp. 488-494. https://doi.org/10.1109/TALE.2014.7062571
[9] A. Walia, N. Singhal, and A. K. Sharma, "A Novel E-learning Approach to Add More
Cognition to Semantic Web," in 2015 IEEE International Conference on Computational
Intelligence & Communication Technology (CICT), 2015, pp. 13-17.
https://doi.org/10.1109/cict.2015.15
[10] L. Kagal, T. Finin, and A. Joshi, "A policy based approach to security for the semantic
web," in International Semantic Web Conference, 2003, pp. 402-418.
https://doi.org/10.1007/978-3-540-39718-2_26
[11] B. Thuraisingham, "Security issues for the semantic Web," in Computer Software and Ap-
plications Conference, 2003. COMPSAC 2003. Proceedings. 27th Annual International,
2003, pp. 633-638. https://doi.org/10.1109/cmpsac.2003.1245408
[12] I. A. Alshalabi, S. Hamada, and K. Elleithy, "Automated adaptive learning using smart
shortest path algorithm for course units," in 2015 IEEE Long Island Systems, Applications
and Technology Conference (LISAT 2015), 2015, pp. 1-5.
https://doi.org/10.1109/lisat.2015.7160187
[13] S. Hamada, I. A. Alshalabi, K. Elleithy, and I. Badara, "Automated Adaptive Mobile
Learning System using the Semantic WEB," in 2016 IEEE Long Island Systems, Applica-
tions and Technology Conference (LISAT 2016), 2016. https://doi.org/10.1109/
lisat.2016.7494150
iJIM Vol. 11, No. 3, 2017
35
PaperUpdating Student Profiles in Adaptive Mobile Learning using ASP.net MVC, dotNetRDF,…
[14] B. DuCharme, Learning Sparql: O'Reilly Media, Inc., 2013.
[15] J. H. Sofia, B. D. Sofia, A. D. José, and J. H. Leontios, Fuzzy Logic-Based Modeling in
Collaborative and Blended Learning. Hershey, PA, USA: IGI Global, 2015.
[16] M. Eisenstadt and T. Vincent, The knowledge web: Learning and collaborating on the net:
Routledge, 2012.
[17] L. Ding, P. Kolari, Z. Ding, and S. Avancha, "Using ontologies in the semantic web: A
survey," in Ontologies, ed: Springer, 2007, pp. 79-113. https://doi.org/10.1007/978-0-387-
37022-4_4
[18] J. Gajek. (28 Oct. 2015). Ontology, Notation 3, and SPARQL. Available:
http://www.codeproject.com/Articles/156888/Ontology-Notation-and-SPARQL
[19] (14 Apr. 2016). dotNetRDF - Semantic Web, RDF and SPARQL Library for C#/.Net.
Available: http://dotnetrdf.org/
[20] J. Cardoso, "Developing course management systems using the Semantic Web," in The
Semantic Web. vol. Chapter 10, ed: Springer, 2008, pp. 169-188. https://doi.org/10.1007/
978-0-387-48531-7_8
8 Authors
Dr. Samir Hamada received his PhD in Computer Science and Engineering and
MS in Computer Science from University of Bridgeport in 2017 and 2001. He, also,
received his B.S. in Accounting from Ain Shams University in Egypt. He is currently
an Assistant Professor of Computer Systems, School of Business at Farmingdale State
College in Farmingdale NY, USA. His research interests include Adaptive Learning,
Mobile Learning and the Semantic Web. (e-mail: shamada@bridgeport.edu)
Dr Ibrahim Alkore Alshalabi received his B.Sc. in Computer Science from Al-
Isra Private University, Amman, Jordan in 1997, his Master of Computer Applica-
tions (MCA) from Bangalore University, India in 2007, and his PhD in Computer
Science and Engineering from the University of Bridgeport, USA in 2016. From
1997-2004 he was Assistant Lecturer at Ma'an Community College, Al-Balqa Ap-
plied University, Jordan. From 2007 to 2009 he was an assistant lecturer at Al-
Hussein Bin Talal University, Jordan. He is currently an adjunct Professor at Al-
Hussein Bin Talal University, College of Information technology, Jordan. His re-
search interests are E-Learning, M-Learning, wireless communications, and networks.
He was an active committee member of the International Conference on Engineering
Education, instructional technology, Assessment, and E-Learning (EIAE 2010, EIAE
2011). (e-mail: ialkorea@my.bridgeport.edu)
Dr. Khaled Elleithy is the Associate Vice President for Graduate Studies and Re-
search at the University of Bridgeport. He is a professor of Computer Science and
Engineering. His research interests include wireless sensor networks, mobile commu-
nications, network security, quantum computing, and formal approaches for design
and verification. He has published more than three hundred fifty research papers in
national / international journals and conferences in his areas of expertise. Dr. Elleithy
is the editor or co-editor for 12 books published by Springer.
Dr. Elleithy received the B.Sc. degree in computer science and automatic control
from Alexandria University in 1983, the MS Degree in computer networks from the
same university in 1986, and the MS and Ph.D. degrees in computer science from The
36
http://www.i-jim.org
PaperUpdating Student Profiles in Adaptive Mobile Learning using ASP.net MVC, dotNetRDF,…
Center for Advanced Computer Studies at the University of Louisiana - Lafayette in
1988 and 1990, respectively.
Prof. Elleithy has more than 30 years of teaching experience. His teaching evalua-
tions were distinguished in all the universities he joined. He is the recipient of the
"Distinguished Professor of the Year", University of Bridgeport, academic year 2006-
2007. He supervised hundreds of senior projects, MS theses and Ph.D. dissertations.
He developed and introduced many new undergraduate/graduate courses. He also
developed new teaching / research laboratories in his area of expertise. His students
have won more than twenty prestigious national / international awards from IEEE,
ACM, and ASEE.
Dr. Elleithy is a member of the technical program committees of many internation-
al conferences as recognition of his research qualifications. He served as a guest edi-
tor for several international journals. He was the chairperson of the International Con-
ference on Industrial Electronics, Technology & Automation. Furthermore, he is the
co-Chair and co-founder of the Annual International Joint Conferences on Computer,
Information, and Systems Sciences, and Engineering virtual conferences 2005 - 2014.
Dr. Elleithy is a member of several technical and honorary societies. He is a Senior
Member of the IEEE computer society. He is a member of the Association of Compu-
ting Machinery (ACM) since 1990, member of ACM SIGARCH (Special Interest
Group on Computer Architecture) since 1990, member of the honor society of Phi
Kappa Phi University of South Western Louisiana Chapter since April 1989, member
of IEEE Circuits & Systems society since 1988, member of the IEEE Computer Soci-
ety since 1988, and a lifetime member of the Egyptian Engineering Syndicate since
June 1983. (e-mail: elleithy@bridgeport.edu)
Dr. Ioana Badara holds a Ph.D. (Teacher Preparation/Science Education) from
University of Tennessee–Knoxville and an M.Phil. (Microbiology & Immunology)
from University of Edinburgh, Scotland. Prior to completing her doctoral work, she
has worked as a research scientist in the biomedical field for about ten years, having
been affiliated with Weill Medical College of Cornell University and Mount Sinai
School of Medicine, in New York City. Her passionate interest in the exploration of
connections between scientists’ epistemologies and the teaching of science led her to
pursuing doctoral studies in Science Education. She has taught a multitude of core
Biology courses for Biology/Pre-Medical undergraduates and mentored student re-
search projects in this field. Dr. Badara is currently a faculty member at University of
Bridgeport, where she teaches core research courses in the doctoral (Ed.D.) program,
Science Education courses in the Science Teacher Preparation program, and History
and Philosophy of Science courses at the undergraduate level. She has been the recip-
ient of several grants for research, including a National Science Foundation grant for
conducting research on the teaching of science in urban school districts. She has pre-
sented her work at national and international conferences in the field of STEM educa-
tion. (e-mail: ibadara@bridgeport.edu)
Dr. Saeid Moslehpour is a Professor and Assistant Dean of Graduate Studies in
College of Engineering, Technology, and Architecture at the University of Hartford.
Currently he is currently editor of Computer in Education Journal of ASEE. He holds
Ph.D. (1993) from Iowa State University, Master of Science (1990) and Bachelor of
iJIM Vol. 11, No. 3, 2017
37
PaperUpdating Student Profiles in Adaptive Mobile Learning using ASP.net MVC, dotNetRDF,…
Science from University of Central Missouri and (1989). His research interests in-
clude modeling, simulation, CPLDs, FPGAs, analog/digital mixed, embedded elec-
tronic system testing, rapid prototyping and cyber learning. He is former faculty sen-
ate chair, ASEE Section I chair, co-director of Connecticut NASA Space Grant and
ECE department chair. (e-mail: moslehpou@hartford.edu)
Article submitted 21 August 2016. Published as resubmitted by the authors 23 December 2016.
38
http://www.i-jim.org
ResearchGate has not been able to resolve any citations for this publication.
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