Case Study: Evaluating student [621003]

Case Study: Evaluating student: [anonimizat] alina
Faculty of Automatic Control and Computer Science
Politehnica University of Bucharest, Systems Engineering
1st year, 313 AC
malina [anonimizat]

Contents
1 Introduction 3
2 Literature review 4
2.1 The social framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 The technical entity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 An overview regarding models for student's satisfaction evaluation in e-Learning environ-
ments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Material and method 6
3.1 Presentation of the algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.3 Questionnaire submission: Selected questions for this study . . . . . . . . . . . . . . . . . 9
3.4 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.5 Variable inputs / outputs / statistics results . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Discussions and results 13
4.1 Statistical analysis vs. evaluation of "distance" . . . . . . . . . . . . . . . . . . . . . . . . 13
5 Conclusions 15
2

Chapter 1
Introduction
Electronic learning, through all its forms (Internet, audio or video, satellite broadcast-
ing, interactive television, CD-ROM) has become one of the most signiant concerns in
the information systems industry. [1]
With time, the internet has taken on a great deal in everyday life, becoming both a
necessity and a fat. The biggest impact of the Internet was on adolescents,because it is
used in their free time and also when they need to study. Very often, students prefer the
online learning environment rather than the traditional learning environment.
Students prefer the online learning environment because they nd the information
(much) easier, the data is presented in an interactive way, and children are often at-
tracted to the magic provided by the Internet.
In today's world ,traditional schools try to implement the online learning environment
in order to meet the students expectations, and to get them to take delight in learning.
Research has been conducted in the eld of psychology, informatics and education in
ordert to discover the impact of e-learning systems, thus trying to evaluate them from
all points of view (for example: Arbaugh and Fich (2007) studied the importance of in-
teraction of participants in online environments and Gilbert (2007) only investigated the
perspective of student: [anonimizat]).
The online learning environment has become a way of solving all sorts of issues, being
already used in many Educational Institutions.
3

Chapter 2
Literature review
"Volery and Lord (2000) dened e-learning as a combination of learner, faculty, in-
structor, technical sta, administrative, learner support, and use of the Internet and
other technologies. In parallel, the success of an e-learning system may be considered as
an emerging concept of `social issues' and `technical issues' and depends on numerous cir-
cumstances, rather than a black-and-white formula. e-Learning systems are open systems
so they are aected by the environment and in
uenced by the people who use them. Since
e-learning systems are socio-technical entities, the e-learning literature has been reviewed
under two sections in the following paragraphs." [2]
2.1 The social framework
In terms of building an e-learning site, it is necessary to nd a quality instructor who
can expose the information in the best possible ways. "Liaw et al. (2007) claimed that
the 'instructor' is the major aspect of e-learning. Within learning environments, instruc-
tors should have sucient time to interact with students in their learning process (Khan,
2005). In parallel, Collis (1995) emphasizes the importance of the 'Instructor' highlight-
ing the fact that it is not the technology itself but the instructional implementation of
the technology which determines its eects on learning. Similarly, Webster and Hack-
ley (1997) state that instructors' attitudes towards a technology, their learning styles
and their control over the technology aect learning outcomes. Moreover, Dillon and
Gunawardena (1995) suggested that instructors' attitudes towards technology-mediated
distance learning systems should be considered when evaluating these systems."[2]
To make e-learning ecient and sought-after, the online environment must present a
ground-breaking, attractive image to capture the user's attention.Design is a key factor
in gaining satisfaction and success. In order to develop eective learning environments,
it is important to consider the target group to whom we dedicate this benet and the
4

common characteristics of people: motivation, trust, faith, anxiety.
2.2 The technical entity
Besides the social problems mentioned, there are technical problems such as the qual-
ity of the system and the quality of the internet. Software quality implies stability,
security, reliability, rhythm, responsiveness, ease of use, user friendliness, well organized
design, personalization Shee &Wang; 2008. The quality of peripherals involves the health
of microphones, headphones, electronic boards, electronic mail, online discussion forums,
synchronized chats and desktop videoconferencing. The higher the quality and reliability
of the technology used, the greater the learning eects will be.
The quality of content in e-learning depends on how the learning environment is orga-
nized and managed. Students highlight quality content, ease of translating information,
clarity of ideas, eective presentation.[3]
2.3 An overview regarding models for student's satisfaction eval-
uation in e-Learning environments
Student satisfaction with e-learning environments is achieved through the use of specic
methods:
creating a questionnaire for collecting relevant information to the students' satisfac-
tion and their perception of organizational performance assessment.
using feedback from students in order to improve e-learning, aiming at growth satis-
faction.
The questionnaire should contain clear questions about the use of e-learning, such as:
questions about eciency, visual impact, technical questions. Students must provide hon-
est answers that will form the basis for statistics on the eective delivery of the e-learning
environment. Eective responses are grid-based, with gures ranging from 0 to 5 each
representing a certain degree of satisfaction.
Following the survey, the results are used to achieve student/user satisfaction,in order to
improve the quality of the e-learning environment.
5

Chapter 3
Material and method
3.1 Presentation of the algorithm
Using the Euclidean distance between two points, I made a statistic based on a ques-
tionnaire from a poll related to the realisation of a subject in the Univeristy.
The Euclidean distance is determined by the formula:
d(p; q) =d(q; p) =q
(q1p1)2+ (q2p2)2+ (q3p3)2+:::+ (qnpn)2=vuutnX
i=1(qipi)2
(3.1)
In the case of the statistics provided by this case study, we used the Euclidian distance
to determine the degree of pupils' satisfaction for a matrix studied at college. I have de-
termined the average answers for each part of the questionnaire, and then I will calculate
the nal average of all the answers.
After calculating the average for each part of the questionnaire, which is 5, we com-
pared each average with the ideal ideal average 5. To apply the Euclidean distance we
made the dierence between 5 (ideal average) and each average calculated experimentally.
Each result obtained (a total of 5) was raised to square, and in the next step of the al-
gorithm the 5 results calculated above were added. The last step of the algorithm is to
apply the square root for the value of the sum obtained.
Applying the square root we obtained the experimental distance. Thus, they can make
a comparison between the theoretical distance (the maximum) and the experimentally
determined distance can be made.
6

3.2 Data collection
In order to get the results from the questionnaire we counted the frequency of the
questionnaire responses and we determined the average of all answers for each question.
The research has shown that we have obtained 57 responses of 0, 113 responses of 1, 295
replies of 2, 707 replies of 3, 755 replies of 4 and 577 replies of 5.
After collecting the data, the following graph was generated.
The diagram shows that the most frequent response of the students was 4, which had
a number of appearances equal to 755. Answer 4 suggests a student's satisfaction with
the subject matter of the statistics, being close to the maximum and the middle one.
After calculating the average of answers for each questionnaire request, I designed a
table showing the value of each result and approximated the result to a full value to high-
light the response to the average of each requirement.The table is structured on the basis
of the questionnaire form, consists of 5 parts and totally contains 27 values approximated
to the nearest nearest value.
7

I 1. 3,139784946 approx. 3
2. 3,35483871 approx. 3
3. 3,462365591 approx. 3
4. 3,215053763 approx. 3
II 5. 3,892473118 approx. 4
6. 3,924731183 approx. 4
7. 4,139784946 approx. 4
8. 3,956989247 approx. 4
9. 3,569892473 approx. 3
III 10. 3,268817204 approx. 3
11. 3,569892473 approx. 4
12. 3,139784946 approx. 3
13. 3,247311828 approx. 3
14. 3,677419355 approx. 4
15. 3,634408602 approx. 4
IV 16. 4,784946237 approx. 5
17. 4,150537634 approx. 4
18. 3,706521739 approx. 4
19. 3,075268817 approx. 3
20. 3,793478261 approx. 4
21. 4,139784946 approx. 4
22. 3,430107527 approx. 3
23. 2,652173913 approx. 3
V 24. 2,344086022 approx. 2
25. 2,720430108 approx. 3
26. 2,677419355 approx. 3
27. 3,408602151 approx. 3Table 1
Depending on the averages determined in the rst table I calculated the averages for
each part of the questionnaire, so the following table resulted with the ve experimentally
determinative averages:
I 3,293010753
II 3,894396552
III 3,422939068
IV 3,717948718
V 2,787634409Table 2
In the second table we obtained the nal average of the questionnaire calculating it as
the arithmetic average of the 5 averages for the 5 parts of the questionnaire. The nal
8

average is: 3,4231859 average that can be approximated to 3.
Value 3 is in the positive half of the grades oered in the questionnaire. As a result
of the statistics, we re
ected the idea that students have a good insight into e-learning
course, e-online faculties, and the wealth of knowledge accumulated over time.
I implemented the Euclidean distance for the averages obtained for each part of the
questionnaire. We compared the averages to the ideal maximum value of 5, making the
dierence between 5 and each average. The questionnaire contains ve parts, each part
having a dierent number of questions.
Following the calculations (5-3.293010753, 5-3.894396552, 5-3.422939068, 5-3.717948718,
5-2.787634409) I have obtained the following results that I have structured in the form of
a table .
1,706989247
1,105603448
1,577060932
1,282051282
2,212365591Table 3
The results in Table 3 rise to squares and translate them to Table 4.
2,913812289
1,222358984
2,487121183
1,64365549
4,894561508Tabel 4
To apply the last part of the Euclidean distance determination algorithm I have gath-
ered the results suggested in Table 4 and I have a total of 13,161,50945. These values will
apply to the radical and I get the value of 3,62787947, which is the Euclidean distance
applied to the 5 part questionnaire with a number of 27 questions answered by 93 students.
Euclidean distance is: 3,627,87947 . This distance re
ects student satisfaction for
the course studied in the e-learning.
3.3 Questionnaire submission: Selected questions for this study
The questionnaire contains 27 questions divided into 5 large parts:
I. Technical aspects: Curricular area
9

II. Technical aspects: Analysis of presentation mode
III. Technical aspects: Assessment and corrections
IV. Technical aspects: Temporal analysis of courses
V. Social opportunities
The questionnaire presents topics about structuring courses, laboratories, teaching ef-
ciency, online course, coordinator teacher, and how students are graded and evaluated.
The rst part of the questionnaire is structured in 4 curricular area requirements:
1. The curricular area of courses matches my expectations.
2. This gives me the opportunity to deepen the knowledge I have.
3. Online learning methods are appropriate.
4. Exercise/ homework / quizzes are appropriate.
The second part contains 5 questions about the analysis of the subject presentation
studied by the students:
1. In attending the courses, you consider that the visual elements are more important
than the narrative ones.
2. During the courses, you prefer to summarize your ideas in the form of written notes or
diagrams.
3. Laboratory work must be based on the direct interaction of the student with the virtual
work environment.
4. The virtual learning environment must be visual rather than narrative.
5. Because it has many visual components, the virtual learning environment allows me
to better acquire knowledge than classical <<in class >>.
The third part highlights issues related to how students assess and correct through 6
requirements:
1. The information on the stages and structure of homework / quizzes are appropriate.
2. Scoring criteria is clear.
3. Teacher assessments are consistent with the results of self-evaluation.
4. As part of the quiz / homework process, I used my syntheses on online courses.
5. Student assessment should consider the ability to solve homework / quizzes.
6. Student evaluation should take into account the ability to synthesize accumulated
knowledge.).
The fourth part is the most complex part of the questionnaire. It presents 8 questions
about the temporal analysis of the course, the eectiveness of the themes / exercises and
the interaction of the teacher with the pupil, which is the most important relationship in
10

building an eective program in the in-depth study of the online course.
1. The online course must be permanently at the student's disposal.
2. When I go through an online course, I am only limited by my learning schedule.
3. Online learning must be based on systematic / regular study.
4. Online learning activity needs to be more rigorously planned than classroom learning.
5. The advantage of online courses is that they can be traced in any order.
6. The advantage of online courses is that they can be browsed without a pre-set program
"when I'm in the mood".
7. Solving homework / quiz exercises should only be done when the student is ready for
this purpose.
8. Interaction with the teacher should be reduced in the eLearning environment.
The last part of the questionnaire outlines some conclusions that capture the social
opportunities, the human interaction in the online environment, the eciency of the on-
line environment from the individual study:
1. Online courses have given me more opportunities to interact with other students.
2. I am part of a group of students who change ideas / techniques of solving exercises /
homework / quizzes.
3. The time spent in the online community is higher than the one dedicated to the indi-
vidual study.
4. It is important that within the online community we share individual elements / sum-
maries of courses "result of individual activities".
3.4 Participants
The research participants are represented by 93 university students, who practice the
respective subjects. They can express their opinion on certain aspects that can improve
the realisation of the courses.
Each student expresses their opinion about the 27 questions with variants from 0 to 5,
0 being meaningless and 5 meaning very high. Those who participate in the questionnaire
are able to express their own opinions about the subject's presentation, the role of the
teacher and the eciency of the acquired information.
11

3.5 Variable inputs / outputs / statistics results
For the beginning, we chose 93 entry students as input variables, each oering 27
options for the points of the questionnaire. Students' answers are obtained as average
outputs of the answers, the frequency of a response, and the averages for each question
in the questionnaire.
The result of the statistics is obtained by applying the Euclidean distance for all the
averages resulting from the questionnaire.
As output variables I can consider the Euclidean distance, because in order to obtain
it I started going through the algorithm of answers from 93 students that I used as input
variables.
12

Chapter 4
Discussions and results
4.1 Statistical analysis vs. evaluation of "distance"
The questionnaire was processed in two ways: statistical analysis and using Euclidean
distance.
As a result of the statistical analysis, I obtined an average of 3,4231859. This average
can be approximated to integer number 3.
Regarding how to get the result, I've mastered several arithmetic environments that
I've worked in dierent rows. We started from the answers of all the students for the 27
questions, we calculated the arithmetic averages for each question. The next step was to
use the previously computed averages to calculate the averages for the ve parts of the
form by arithmetic mean method.
After obtaining the arithmetic averages for each part of the questionnaire (a total of 5
parts), I have calculated the arithmetic mean of these averages and the value obtained is
considered the value of the statistics analysis.
In determining the statistic analysis we only used the caliber of some arithmetic av-
erages, which we gradually processed. Thus, the statistical analysis is not a dicult one
regarding the compelxity of the steps, and the result is safe, concrete and clear.
To achieve the Euclidean distance we used the 5 averages obtained for the 5 parts of
the questionnaire. We have lowered these values from the maximum average (the ideal
average) 5. In the next step of determining the Euclidean distance we raised to square
each previous obtained, then we gathered all the results, and for the nal result we applied
the radical and we obtained the value of the Euclidean distance .
13

In accomplishing the distance we used basic arithmetic operations, assembly opera-
tions, and square root of the averages.
Regarding the complexity of the algorithm, the distance calculation was developed by
a small number of operations with a number of terms equal to 5, while for the statistical
analysis for the beginning we repeated a step 27 times to determine the average for each
question. In my opinion, for the questionnaire from which we started the statistics, it was
more ecient to calculate the Euclidean distance.
By Euclidean distance I obtained the nal value: 3,627,87947, and by statistical analy-
sis I obtained the value: 3,4231859. If I approximate these values, the one determined by
Euclidean distance can be approximated to 4 and the one obtained by statistical analysis
at 3.
The two values presented by the two dierent methods are close together, the dier-
ence being 0.2 because the result obtained by the Euclidean Distance is higher than the
one obtained by the statistical analysis. Euclidean distance was achieved starting from
the comparison of the results of the questionnaire with the ideal results that the question
can have, ve.
By comparing an essential dierence between the two methods, I could say that the
Euclidean distance was temporally more eective than the statistical analysis, although
the results of the two methods are close, with very low deviations.
14

Chapter 5
Conclusions
In this case study, I brought to the forefront the analysis of a questionnaire on studying
a course in the online learning environment. In this questionnaire, 93 students attended
this course, so they were able to express their opinion in dierent situations. The purpose
of the research was to gain the students' opinions about the eciency of studying a course
in the online learning environment and checking eectiveness. Moreover, another impor-
tant aspect of the research was to determine whether a-learning is more constructive than
the traditional methods.
As a result of the results obtained by the two methods (statistical analysis and evalua-
tion of Euclidean distance), pupils' satisfaction and gratitude was achieved regarding the
curricular area of the course, the possibility to deepen the presented information, online
learning methods, themes, exercises, presentation of the course, techniques used to expose
information, motivate and evaluate students.
By concluding the case study, we highlighted the importance of today's online learning
environment, the importance of it being used in as many schools and universities and the
eectiveness of studying such courses. Although today the online learning environment is
not very developed, its development is needed to outline technical, computer, engineering
skills of students and students.
15

References
[1] A study on the student's perspective on the eectiveness of using e-learning, Johan
Eddy Luaran, Nur Nazleen Samsuri, Fazyudi Ahmad Nadzri, Kamarol Baharen,
Mohamad Rom
[2] Computers & Education 53 2009 1285{1296, Article Multi-dimensional students' eval-
uation of e-learning systems in the higher education context: An empirical investiga-
tion
[3] Student Perspectives on the Importance and Use of Technology in Learning, Allison
BrckaLorenz, Heather Haeger, Jennifer Nailos, and Karyn Rabourn, Indiana Univer-
sity
16