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CLASSROOM VS. E-LEARNING: A CASE STUDY ON THE PERFORMANCE OF STUDENTS IN DIFFERENT LEARNING SCENARIOS

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This study was conducted to compare the performance of students using an online mathematics course with the performance of students who were taught in a face-to-face classroom course. While many approaches exist to measure the learning progress of students in e-learning environments, practical comparisons of different learning approaches are still rare. To address this gap, this paper provides a direct comparison of e-learning and classroom teaching in practice. The preparatory mathematics course of RWTH Aachen University in Germany was used to perform this study. More than six thousand students began studying in the semester of fall 2015. About one-quarter of them took the preparatory mathematics course. To get a homogeneous group of students, we restricted our experiment to the group of students in the field of engineering. One hundred and thirty students agreed to participate in the study. Before the start of the course, they were randomly divided into two groups. The first group was taught in a face-to-face lecture format. The second group used an online mathematics course. All students learned the same topics by using different approaches. The e-learning platform used was the online preparatory course Online Mathematik Brückenkurs OMB+. More than twenty German universities recommend this course for prospective students in preparation for their studies. The aim of the study was to evaluate whether there is a significant difference in performance between students learning with the e-learning system and students who are taught by the typical lecture format. Multiple assessments were conducted to measure the performance of the students. In addition, a survey conducted at the end of the course allowed the students to give feedback about the perception and satisfaction of the study. Based on our results, the face-to-face teaching was more successful than the e-learning format in this case. While the overall performance of the students in the control group significantly improved, the e- learning group did not improve their average performance. We performed a t-test to show statistical significance (p = 0.018) and compared the different results of the students in the tests. Using the results of the survey, we also explain how motivation and distraction of the students impacted the overall performance. In our scenario, the use of an e-learning application led to a significantly worse performance. We do not claim that the results are generally applicable for people using an e-learning application, but we point out which factors influenced the learning experience most and the necessary changes for successful e-learning.
Screen capture of different OMB+ features 2 Figure 1 shows screen captures of the OMB+ platform. At the top, the header of the page is located containing the navigation bar with links to the home page, the course, a board and other pages. Additionally, the different types of practice can be selected. To the left, the different topics can be selected. In total, the platform offers ten different chapters. Each is divided into multiple sections. In Figure 1 the fourth topic Fractional arithmetic ("Bruchrechnung") of the first section ("Zahlen") in the first chapter ("Elementares Rechnen") is selected. Figure 1a shows the starting page of the selected topic. At the top the title of the topic is displayed followed by the different types of practice which can be chosen, namely exercise ("Übung"), training and quiz. The starting page provides the knowledge of the topic. The information is presented similarly as in a textbook. Multiple pages also contain interactive elements like videos. Figure 1b shows the exercise. The reader is presented with multiple tasks and can reveal the answer by a click. No input from the user is required in this step. The training section as shown in Figure 1c presents other mathematics problems. The training is divided in multiple categories, where each category contains a specific type of problem. The leaner is expected to fill in the answers to the questions and can then check whether the answer is correct. Figure 1d shows the final task for the topic, the quiz. On this page, the reader is confronted with multiple questions and tasks. In contrast to the training section, the different types of problems are mixed. After the user answers the questions, he can again check which answers are correct and in some cases is presented with some information why the answer is wrong and hints how he can correct his answer. One additional question type is the final exam ("Schlussprüfung") question type. For each of the ten chapters one final exam can be made which contains questions of all the sections of the chapter. If the learner fails the exam, he is allowed to repeat the questions but will get new ones. As we describe in the next paragraph, not the whole mathematics course was replaced by the OMB+. Therefore, the last learning type was not used very frequently by the students.
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CLASSROOM VS. E-LEARNING: A CASE STUDY ON THE
PERFORMANCE OF STUDENTS IN DIFFERENT LEARNING
SCENARIOS
T. Dondorf, R. Breuer, H. Nacken
RWTH Aachen University (GERMANY)
Email: {dondorf, breuer, nacken}@lfi.rwth-aachen.de
Abstract
This study was conducted to compare the performance of students using an online mathematics
course with the performance of students who were taught in a face-to-face classroom course. While
many approaches exist to measure the learning progress of students in e-learning environments,
practical comparisons of different learning approaches are still rare. To address this gap, this paper
provides a direct comparison of e-learning and classroom teaching in practice.
The preparatory mathematics course of RWTH Aachen University in Germany was used to perform
this study. More than six thousand students began studying in the semester of fall 2015. About
one-quarter of them took the preparatory mathematics course. To get a homogeneous group of
students, we restricted our experiment to the group of students in the field of engineering. One
hundred and thirty students agreed to participate in the study. Before the start of the course, they were
randomly divided into two groups. The first group was taught in a face-to-face lecture format. The
second group used an online mathematics course. All students learned the same topics by using
different approaches. The e-learning platform used was the online preparatory course Online
Mathematik Brückenkurs OMB+. More than twenty German universities recommend this course for
prospective students in preparation for their studies.
The aim of the study was to evaluate whether there is a significant difference in performance between
students learning with the e-learning system and students who are taught by the typical lecture format.
Multiple assessments were conducted to measure the performance of the students. In addition, a
survey conducted at the end of the course allowed the students to give feedback about the perception
and satisfaction of the study.
Based on our results, the face-to-face teaching was more successful than the e-learning format in this
case. While the overall performance of the students in the control group significantly improved, the e-
learning group did not improve their average performance. We performed a t-test to show statistical
significance (p = 0.018) and compared the different results of the students in the tests. Using the
results of the survey, we also explain how motivation and distraction of the students impacted the
overall performance.
In our scenario, the use of an e-learning application led to a significantly worse performance. We do
not claim that the results are generally applicable for people using an e-learning application, but we
point out which factors influenced the learning experience most and the necessary changes for
successful e-learning.
Keywords: e-learning, study, practical evaluation, preparatory course, mathematics
1 INTRODUCTION
Electronic educational technology, mostly called e-learning, has become more and more important in
the last decade. Computer-based learning systems provide benefits for students as wells as lecturers
by offering more flexibility about place, time and pace. In addition, these platforms often offer more
ways for interactions and motivation than traditional learning settings.
Many approaches exist to measure the learning progress of students in e-learning environments [13].
Practical comparisons of different learning approaches are still rare. To address this gap, this paper
provides a direct comparison of e-learning and classroom teaching in practice to check whether an e-
learning platform can potentially replace existing learning scenarios.
In this paper, we compare the learning progress of students who are taught in a traditional classroom
environment with students using an e-learning application. The experiment was conducted during the
preparatory mathematics course of RWTH Aachen University in Germany in fall 2015. The goal of the
study is to find out whether students who are taught using an e-learning application perform
significantly better or worse than students who are taught in a classroom setting.
We randomly divided students into an e-learning group and into a group consisting of face-to-face
learners. Both groups learned the same topics but in different settings. To measure the students’
present math skills, one assessment was conducted at the beginning of the experiment. During the
course of the experiment, five more assessments were made to measure the performance. In addition,
we measured the satisfaction of the students by a survey which was conducted after the course.
The remainder of this paper is structured as follows. In Section 2, we present related work. Section 3
describes our methodology and the execution of the experiment. In Section 4, we present the results
of the study. Section 5 concludes the paper.
2 RELATED WORK
Three types of interactions have been defined by Moore [4]: learner-content, learner-instructor, and
learner-learner. Learner-instructor interactions represent the traditional lecture formats in which an
expert prepares the study material and presents it to an audience. The learner-learner approach
features collaborative learning. In this study, we focus on the learner-content interaction which refers
to any activity between the learner and the instructional content in the e-learning environment.
In the following subsection, we first give an overview about the satisfaction and benefits of learners.
We then present similar research which has been done that also conducted studies to compare e-
learning with classroom scenarios.
2.1 Learner's Satisfaction and Benefits
One key factor of education is the motivation of learners. As we later show during our study, multiple
students reported a low level of motivation. Therefore, we give a brief overview of the related work.
Multiple experiments have been made to measure the satisfaction of students and its influence on
participation [5, 6]. Furthermore, multiple authors have conducted studies to compare the satisfaction
of learners using online resources with learners who are taught in a classroom [710]. Various
reasons for motivation and satisfaction have been named. Below we list the most important factors.
Ease of use of the application or software
Computer skill
Interaction with fellow students and instructors
Flexibility of the medium
Motivating aims
In addition to the satisfaction, the benefits of e-learning from the users' point of view have been
addressed multiple times [1013]. The most mentioned benefits are:
Time and location flexibility
Self-paced learning process
Unlimited access to materials
2.2 Prior studies
Multiple studies exist which compare the traditional classroom approach with learners who are taught
via an online platform. Although research and results exist, the findings from these studies have been
very mixed. In the following paragraphs, we present the studies to give an overview of the academic
research in the area.
Rivera and McAlister [14] performed a study in 2001 to compare the efficacy of three different class
formats. The traditional course was offered in a lecture format, a web based course was held almost
exclusively online, and a hybrid format was offered which used a mix of traditional and web based
materials. In total, 134 students took parts in the study. A statistical analysis of the exam results was
conducted and showed that there was no significant difference between the exam scores. In addition,
the students were asked if they were satisfied with the course. Students who participated in the online
course were less satisfied with the course than other students.
Another study was made by Zhang et al. in 2005 [13]. The team compares the classical classroom
teaching with an interactive e-learning platform. The platform contains videos of the lecture,
presentation slides and lecture nodes. The web application consists of two parts. First, students can
type in a question and the system tries to provided corresponding learning content. Second, the
system provides follow-up learning tips based on the history of each individual user. Two experiments
were made to evaluate the effectiveness of the system. In the first study, 34 students participated and
were divided into an e-learning and a classroom group. The second study consisted of 69 learners. To
measure the performance, a pre-course and a post-course test was conducted. The difference of the
tests was interpreted as the learner’s performance. The results indicate that both times, the scores of
the e-learning students were significantly higher than the scores of the classroom learners. In the post-
study questionnaires, most participants in the e-learning group reported that they liked the multimedia
presentations and were satisfied with the flexible learning process. However, the difference in
satisfaction levels between both groups was not significant.
Another empirical study was made by Lim et al in 2008 [15]. The researchers investigated the effects
of three different methods of teaching: online instruction, traditional face-to-face, and a combination of
both. Student performance and satisfaction levels were measured during the experiment. 153 students
completed the survey for the study. In addition, a written pre-test and post-test were conducted to
measure the performance of the individual students. The results of the study show that students in the
online learning group and the combined learning group had statistically significant higher levels of
achievement than students in the traditional learning group.
The literature with regards to e-learning and its effectiveness is somewhat contradictory. Multiple
studies exist which claim that online platforms benefit the learners, as well as studies that show the
opposite. The researchers Ungerleider and Burns [16] analyzed multiple of these studies in 2003. In
total they found 135 studies which claim to measure the effects of online learning in comparison to
traditional classroom learning. According to the team, most of the studies were methodologically
flawed. Problems were missing random assignments of learners, missing control groups, the lack of
experiment control, small sample sizes and others.
We adopted working methods from the presented studies including the modeling of the performance
and questioning techniques. We also adjusted our methodology to not make the same mistakes as
previous studies did. Our study was conducted unbiased with regards to the outcome of these other
studies.
3 METHODOLOGY
We compare the traditional classroom learning approach with an e-learning online platform. The
preparatory mathematics course in fall 2015 of RWTH Aachen University in Germany was used for the
study. The topics of the course include basic mathematics subject areas like linear equations, integral
calculus and analytic geometry. The course is offered yearly and regularly taken by about 1,800
prospective students from varying subjects of study. We restricted our experiment to the students of
the engineering studies to work with a homogeneous group. Of the students who registered to the
course, 131 agreed to take part in the study.
3.1 E-Learning Platform OMB+
The classical approach of classroom learning during the course was compared with the e-learning
platform Online Mathematik Brückenkurs OMB+
1
which offers an online bridge course for
mathematics. The concepts and contents have been developed by eleven German universities in
cooperation with the company integral-learning GmbH [17]. More than twenty German universities
recommend the usage of the web application as preparation for their studies [18]. The OMB+ is the
follow up of the Online Mathematical Bridge Course OMB, which was used over several years in
Germany [18, 19].
1
OMB+, https://www.ombplus.de/
(a)
(b)
(c)
(d)
Figure 1 Screen capture of different OMB+ features
2
Figure 1 shows screen captures of the OMB+ platform. At the top, the header of the page is located
containing the navigation bar with links to the home page, the course, a board and other pages.
Additionally, the different types of practice can be selected. To the left, the different topics can be
selected. In total, the platform offers ten different chapters. Each is divided into multiple sections. In
Figure 1 the fourth topic Fractional arithmetic (“Bruchrechnung”) of the first section ("Zahlen") in the
first chapter ("Elementares Rechnen") is selected. Figure 1a shows the starting page of the selected
topic. At the top the title of the topic is displayed followed by the different types of practice which can
be chosen, namely exercise ("Übung"), training and quiz. The starting page provides the knowledge of
the topic. The information is presented similarly as in a textbook. Multiple pages also contain
interactive elements like videos. Figure 1b shows the exercise. The reader is presented with multiple
tasks and can reveal the answer by a click. No input from the user is required in this step. The training
section as shown in Figure 1c presents other mathematics problems. The training is divided in multiple
categories, where each category contains a specific type of problem. The leaner is expected to fill in
the answers to the questions and can then check whether the answer is correct. Figure 1d shows the
final task for the topic, the quiz. On this page, the reader is confronted with multiple questions and
tasks. In contrast to the training section, the different types of problems are mixed. After the user
answers the questions, he can again check which answers are correct and in some cases is
presented with some information why the answer is wrong and hints how he can correct his answer.
One additional question type is the final exam (“Schlussprüfung”) question type. For each of the ten
chapters one final exam can be made which contains questions of all the sections of the chapter. If the
learner fails the exam, he is allowed to repeat the questions but will get new ones. As we describe in
the next paragraph, not the whole mathematics course was replaced by the OMB+. Therefore, the last
learning type was not used very frequently by the students.
2
Screen captures taken from the OMB+ page, https://www.ombplus.de/
To compare the e-learning course with the classical learning approach, the participating students were
randomly divided into two groups. The first group used the OMB+ during the practice lessons. The
control group learned by using the traditional classroom approach. It is notable, that not the whole
course was replaced by the online platform. As not all contents of the e-learning platform were
identical to the contents presented in the course, only parts of the lecture were replaced by the OMB+.
However, these parts did not build on previous units. For the remaining courses, the students learned
in a classroom scenario. The following five units were replaced with the online course.
Fractional arithmetic, percentage calculation and interest calculation
Solving of equations and inequations
Integral calculus
Differential calculus
Analytic geometry
To measure the performance of each student, multiple tests were conducted. The first test was
performed before the course started to assess the pre-existing knowledge of the students. This value
models the pre-existing performance of a student. Following, for each of the five topics one
assessment was made with questions related to the field. These tests were conducted after each
lesson with pen and paper. The average of these five assessments models the performance of the
student during the course. The difference between the pre-course test and the average represents the
learning effect. In addition to the measurement of the performance, we asked the students to fill out a
questionnaire about the study after the course. It contained questions about the perception of the
course, the satisfaction and the usage of the e-learning platform OMB+.
3.2 Limitations
As with any study, there are some limitations to our research that should be noted.
Initially more than 131 students agreed to take part in the study. Because of the low
participation, the data of only 51 students could be used. While the data is enough to make
the conclusions we did, the results should not be generalized.
In addition, we cannot guarantee that the control group and the e-learning group are
homogenous. The students were randomly distributed across the groups. However, because
of the small group of students, it is entirely possible that the groups did not represent a
homogenous cross-section of the students.
While all tutors were informed about the experiment, we were not able to monitor the tutors
when the lectures were given. The e-learning exercises were supervised as well as the
classroom courses. The behavior of the tutors as the human factor could of course have
influenced the results of the study.
One last factor is the motivation and participation of the students. Many of the students
reported a high degree of distraction during the study. This factor could have an influence on
the results but is of course strongly related to the concentration, motivation and distraction of
each individual student. Students might have been physical present but instead of using the
online course, could have just surfed the web or other things not related to the lecture.
4 RESULTS
Of the 131 students which were willing to participate in the study, 37 never attended the course. They
might have changed the course or dropped out of the program entirely. Many of the remaining
students only sporadically participated in the tests. As a results, the data records of only 51 students
could be analyzed. 27 were part of the e-learning group. The remaining 24 students participated in the
control group. All of these students participated in the initial test and in at least four of the five
assessments.
4.1 Performance of students
As described in Section 3, one assessment was conducted before the course and five more during the
course. The results of these are shown in Table 1 for both learning groups.
Performance
Students
Pre-course
Avg. Assessments
24
51.5%
60.5%
27
46.5%
46.1%
Table 1 Performance E-Learning vs. Classroom
The classroom learners improved their performance from 51.5% in the pre-course test to 60.5% in the
assessments. In comparison, the students in the e-learning scored 46.5% in the pre-course test and
reached 46.1% in the assessments. Therefore, the traditional classroom learners improved their
performance by 9% while the e-learning students did not improve their average performance.
Figure 2 Individual results
The results for each individual learner are visible in Figure 2. On the abscissa, the results of the pre-
course test are shown. The ordinate shows the averaged results of the five assessments. The blue
graph shows the trend line for the traditional classroom group. The orange graph represents the trend
for the e-learning students. Obviously, both graphs are ascending as this means that students who
performed better than others in the entry-level test are also more likely to get better results in the
remaining assessments and vice versa. The interesting result in the diagram is the distance between
the graphs. The graph of the face-to-face learners is significantly higher than the graph of the e-
learning students. This indicates that the classroom learners performed better than the e-learning
participants. However, it is notable that the dispersion of the e-learners is much higher. The correlation
coefficient of the upper graph (classroom) is 0.80, while the coefficient for the e-learning trend line is
0.63. Multiple outliers are visible, in both directions.
To test for statistical significance, we performed Welch’s t-test, which is appropriate to test a
hypothesis which contains samples with unequal variances and unequal sample sizes [20]. The result
shows statistical significance for our data (p = 0.018).
4.2 Satisfaction of students
Complementary to the measurement of the performance, we also conducted a questionnaire after the
course to measure the satisfaction of the students with the course and its contents. The questions
were originally asked in German and translated into English for this paper. 22 of the 27 e-learning
students answered the questions. As the answers were optional in some cases, less than answers
were submitted for some questions. The questionnaire contained nineteen questions with three item
choices and four free text questions. Some of the questions were not directly related to the study as
these questions were organizational questions or related to other parts of the course. In the following,
we show the results of the questions which are considered relevant to this study.
Question
Agree
Undecided
Do not agree
The interactive pictures were helpful.
50%
41%
9%
I would like to see more interactive pictures.
36%
46%
18%
I would like to see more explaining examples.
45%
50%
5%
I would like to see more embedded tasks in the
explaining text.
59%
27%
14%
The explaining texts were sufficient.
41%
41%
18%
Table 2 Post-course questionnaire about the course contents
Table 2 shows the questions and the results related to the contents of the course. It is notable that the
interactive elements of the course were well received by the learners. Overall, the students would
have liked to see more interactive elements like interactive pictures or embedded tasks. The
explaining texts which were presented earlier were considered to be enough with regards to the
scope.
The level of difficulty of …
too easy
appropriate
too hard
… the quiz and training tasks was …
0%
100%
0%
… the explaining texts was
5%
77%
18%
Table 3 Post-course questionnaire about the levels of difficulty
Three questions were asked to measure the satisfaction with regards to the level of difficulty. The
results are shown in Table 3. All of the students were satisfied with the difficulty of the quiz and the
training tasks. The level of difficulty of the explaining texts was considered appropriate by three-
quarter of the students. Only 18% stated that these were too hard.
Five more questions were asked to measure the satisfaction with the course in total. Table 4 shows
the questions and answers. The results indicate that only one third of the students thinks that the
OMB+ application prepares for the studies. About half of the students would not recommend the
OMB+ to others. Three-quarter of the participants claimed that the OMB+ did not sufficiently replace
the traditional classroom learning and that they would have learned more effectively in a traditional
course.
Table 4 Post-course questionnaire about the OMB+
In addition to the given questions, students were able to enter free text to provide individual feedback.
Thirteen students made use of it. In the following, we summarize relevant information which the
students filled in.
Three students reported a high level of distraction.
Three students criticized that the OMB+ did not tell why a submitted answer was wrong.
Two students considered the OMB+ helpful to refresh or deepen math knowledge, but not to
learn math in the first place.
Two students would have liked to use the OMB+ at different places.
One student reported that the switchover from computer to paper and vice versa was
complicated.
5 CONCLUSIONS
As stated in the previous section, the classroom learners performed significantly better than the
students using the e-learning platform OMB+. We argue that this outcome is strongly related to the
motivation of the students. Based on the results of the questionnaire, we know that most students in
the e-learning group preferred the traditional classroom learning approach in our setup. The free text
answers indicate that one of the reasons for this is the high level of distraction during the course.
Other reasons have added to the low motivation of the students. First, the students were not free to
choose their learning pace. As the preparatory course was only in parts replaced by the OMB+
platform, the students had to work on given topics in a given time span. As a consequence, the
advantage of self-paced learning was non-existent during the experiment. Furthermore, the students
were not able to choose the time and location themselves. As stated by students in the free text part of
the survey, they would have liked to use the OMB+ at a different place instead of being in the provided
room.
As we presented in Section 2, multiple authors [1013] claim that time and location flexibility as well as
a self-paced learning process are the key benefits for learners using an e-learning platform. As these
advantages of e-learning were missing during our study, multiple benefits of e-learning were less
present which might have contributed to the missing motivation among the students and to the worse
performance. We therefore conclude that the self-paced-learning as well as the option to choose the
time and location on their own is an important factor for the usage of e-learning.
This study was the first in a set of multiple studies at RWTH Aachen University to compare existing
classroom settings with e-learning approaches and to measure the success of e-learning in the
university. Our results indicate that the classroom learners performed better than the e-learning
students in this setup. We argue, that our results are not generally applicable as the usage of the e-
learning platform was restricted in our experiment as we explained in the previous paragraphs. Our
study shows the limits of e-learning. We conclude that self-paced-learning and the option to freely
choose time and location drastically influence the learners’ motivation.
Question
Agree
Undecided
Do not agree
OMB+ prepares for the studies.
32%
41%
27%
OMB+ is also useful during the studies.
36%
28%
36%
I will recommend the OMB+
27%
27%
46%
OMB+ sufficiently replaced the classroom exercises.
9%
18%
73%
I would have learned more effective in a classroom
setting.
77%
14%
9%
6 ACKNOWLEDGEMENTS
We thank Stifterverband für die Deutsche Wissenschaft and Heinz Nixdorf foundation for supporting
the study as part of the “AIX - Future teaching and learning” project of RWTH Aachen University. In
addition, we thank Prof. Dr. Seiler who is responsible for the development of the OMB+ online platform
and Prof. Dr. Stens who is responsible for the preparatory mathematics course of the university for the
close collaboration.
REFERENCES
[1] E. Duval, “Attention Please!: Learning Analytics for Visualization and Recommendation,” in
Proceedings of the 1st International Conference on Learning Analytics and Knowledge, New
York, NY, USA: ACM, 2011, pp. 917.
[2] H. Drachsler and W. Greller, “The Pulse of Learning Analytics Understandings and Expectations
from the Stakeholders,” in Proceedings of the 2Nd International Conference on Learning
Analytics and Knowledge, New York, NY, USA: ACM, 2012, pp. 120129.
[3] R. Ferguson and S. B. Shum, “Social Learning Analytics: Five Approaches,” in Proceedings of
the 2Nd International Conference on Learning Analytics and Knowledge, New York, NY, USA:
ACM, 2012, pp. 2333.
[4] M. G. Moore, “Editorial: Three types of interaction,” American Journal of Distance Education,
vol. 3, no. 2, pp. 17, http://dx.doi.org/10.1080/08923648909526659, 1989.
[5] I. Jung, S. Choi, C. Lim, and J. Leem, “Effects of Different Types of Interaction on Learning
Achievement, Satisfaction and Participation in Web-Based Instruction,” Innovations in Education
and Teaching International, vol. 39, no. 2, pp. 153162,
http://dx.doi.org/10.1080/14703290252934603, 2002.
[6] P.-C. Sun, R. J. Tsai, G. Finger, Y.-Y. Chen, and D. Yeh, “What drives a successful e-Learning?
An empirical investigation of the critical factors influencing learner satisfaction,” Computers &
Education, vol. 50, no. 4, pp. 11831202,
http://www.sciencedirect.com/science/article/pii/S0360131506001874, 2008.
[7] J.B Arbaugh, “Managing the on-line classroom: A study of technological and behavioral
characteristics of web-based 5MBA6 courses,” The Journal of High Technology Management
Research, vol. 13, no. 2, pp. 203223,
http://www.sciencedirect.com/science/article/pii/S1047831002000494, 2002.
[8] K.-S. Hong, “Relationships between students' and instructional variables with satisfaction and
learning from a Web-based course,” The Internet and Higher Education, vol. 5, no. 3, pp. 267
281, 2002.
[9] L. A. Díaz and F. B. Entonado, “Are the Functions of Teachers in e-Learning and Face-to-Face
Learning Environments Really Different?,” Educational Technology & Society, vol. 12, no. 4, pp.
331343, 2009.
[10] G. Piccoli, R. Ahmad, and B. Ives, “Web-Based Virtual Learning Environments: A Research
Framework and a Preliminary Assessment of Effectiveness in Basic IT Skills Training,” MIS
Quarterly, vol. 25, no. 4, pp. 401426, 2001.
[11] A. Kumar, P. Kumar, and S. C. Basu, “Student Perceptions of Virtual Education: An Exploratory
Study,” in Web-based instructional learning, M. Khosrowpour, Ed, Hershey, Pa.: IRM Press,
2002, pp. 132141.
[12] N. A. Baloian, J. A. Pino, and H. U. Hoppe, “A teaching/learning approach to CSCL,” in
HICSS33: Hawaii International Conference on System Sciences
[13] D. Zhang, J. L. Zhao, L. Zhou, and Nunamaker,Jr, Jay F, “Can e-Learning Replace Classroom
Learning?,” Commun. ACM, vol. 47, no. 5, pp. 7579,
http://doi.acm.org/10.1145/986213.986216, 2004.
[14] J. C. Rivera and M. L. Rice, “A comparison of student outcomes & satisfaction between
traditional & web based course offerings,” Online Journal of Distance Learning Administration,
vol. 5, no. 3, 2002.
[15] J. Lim, M. Kim, S. S. Chen, and C. E. Ryder, “An empirical investigation of student achievement
and satisfaction in different learning environments,” Journal of Instructional Psychology, vol. 35,
no. 2, pp. 113119, 2008.
[16] C. Ungerleider and T. Burns, “A systematic review of the effectiveness and efficiency of
networked ICT in education: A state of the art report to the Council of Ministers Canada and
Industry Canada,” 2003.
[17] integral-learning GmbH, Autoren - OMB+. [Online] Available:
https://www.ombplus.de/ombplus/public/authors.html. Accessed on: May 09 2016.
[18] R. Seiler et al, “Bridging Math-Gaps with the Learning Environment MUMIE. The European
Project S3M2 and the German Math-Bridge Course OMB+ for the Enhancement of Student
Mobility,” in EADTU 2014-Open and Flexible Higher Education Conference. New Technologies
and the Future of Teaching and Learning, 2014, pp. 367382.
[19] S. O. Krunke, K. Roegner, L. Schüler, R. Seiler, and R. L. Stens, “Der Online-Mathematik-
Brückenkurs OMB: Eine Chnace zur Lösung der Probleme der Schnittstelle
Schule/Hochschule,” Mitteilungen der Deutschen Mathematiker-Vereinigung, vol. 20, no. 2, pp.
115120, 2012.
[20] G. D. Ruxton, “The unequal variance t-test is an underused alternative to Student’s t-test and
the Mann–Whitney U test,” Behavioral Ecology, vol. 17, no. 4, pp. 688690,
http://beheco.oxfordjournals.org/content/17/4/688.short, 2006.
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