Exploring 3D-Virtual Learning Environments with Adaptive Repetitions

IOSR Journal of Mathematics (IOSR-JM)
e-ISSN: 2278-5728, p-ISSN: 2319-765X. Volume 13, Issue 1 Ver. IV (Jan. - Feb. 2017), PP 88-92
www.iosrjournals.org
DOI: 10.9790/5728-1301048892 www.iosrjournals.org 88 | Page
Exploring 3D-Virtual Learning Environments with
Adaptive Repetitions
1
Aftab Alam, 2
Sehat Ullah,3
Numan Ali
Department of Computer Science & IT, University of Malakand, Chakdara K.P.K, Pakistan
E-mail: 1
alam@uom.edu.pk, 2
sehatullah@uom.edu.pk, 3
numan@uom.edu.pk
Abstract: In spatial tasks, the use of cognitive aids reduce mental load and therefore being appealing to
trainers and trainees. However, these aids can act as shortcuts and prevents the trainees from active
exploration which is necessary to perform the task independently in non-supervised environment. In this paper
we used adaptive repetition as control strategy to explore the 3D- Virtual Learning environments. The
proposed approach enables the trainee to get the benefits of cognitive support while at the same time he is
actively involved in the learning process. Experimental results show the effectiveness of the proposed approach.
Keywords: Virtual Environments, Adaptive 3D-Virtual Learning environments, Cognitive aids, Multimodal
feedback, Multimodal Virtual laboratory (MMVL)
I. Background
3D-Virtual Learning Environments (3D-VLEs) are the representation of 3D space inside the computer
memory which enables students to navigate freely inside the virtual world and process different objects in real
time[1, 2]. Adaptive 3D-Virtual Learning Environments (3D-VLEs) change its contents at run time according to
the learning needs of individual student[3, 4]. These environments have many advantages. It display
personalized learning contents to each student, reduces the probability of astray navigation inside the virtual
environment and makes the distinction between education and entertainment which improve the learning
process of students [5-7] . In literature a lot of work has been done which support the use of cognitive aids
(Audio, Visual and textual etc) in virtual learning environments. For example, Nguyen et al.[8] used lighting
source , arrows, and a compass as navigational guiding aids which help trainees to find the target. Gavish
et al.[9] presented a 3D virtual puzzle in which verbal instructions plus mouse pointing are used to find the
target location on learner screen. Similarly, Ali et al. [3] developed Multimodal Virtual laboratory (MMVL in
which students were provided both audio and visual support during experiments which improved their
efficiency.
The use of these aids reduce mental load on learner as it provide a speciﬁc and easy to follow
instructions mechanism to perform the task [9]. However, these aids can act as shortcuts and prevents the
trainees from active exploration which is necessary to perform the task independently in non-supervised
environment [10-12]. Therefore, a mechanism is needed to use these aids while minimizing their negative
effects. Jorge, R. et al.[13] recommended the control use of cognitive aids and proved that it will not affect the
performance of the trainee in the real environment. Similarly, Rehman et al.[14], suggested the use of semantic
aids with direct aids to maximize active exploration in virtual environments. In this paper we used adaptive
repetition as a strategy to explore the 3D- Virtual Learning environments. The proposed approach enables the
trainee to get the benefits of cognitive support while at the same time he is actively involved to explore the
environment. We also tried to find the number of adaptive repetition required by the weak, average and good
students to actively explore the virtual environment and perform the task successfully in non-supervised
environment”. The remaining paper is organized as below.
In section II we discussed the proposed approach followed by implementation in section III. In section
IV we presented experimentations. Discussion is given in section V followed by conclusion in section VII.
II. The Proposed Approach
2.1 The Learning Decision Function (LDF)
With the help of Learning Decision Function (LDF), we can measure the learning skill of students in
3D-VLEs and declare it as weak, average and good learners [15]. The LDF takes time, no of errors and test
scores of a student as inputs, calculate the learning skill of a student in the range of 0 to 1 and displays the result.
If the function return value lies in the range [0, 0.3], the student is weak learner. If the return value lies in the
range of [0.3+ ε, 0.6], the system consider the student as average learner. For good learner the return value exists
in the range of [0.6+ ε, 0.1].

Exploring 3D-Virtual Learning Environments with Adaptive Repetition
2.2 Adaptive Repetition
In the initial stage the student is given Visual, audio, and textual support when he/she starts learning
module M in 3D-VLEs. The system measure the total time taken by the students to complete the module, counts
the number of errors he/she performs and calculates scores in the test given by the student at the end of the
module. Using these three variables, the LDF declared the student as weak, average or good. Now to repeat the
learning module M, the system provides customized support for different learners. Weak students will follow path
(X) which contains the same support of Visual, Audio, and textual aids. Average students will follow path (Y) for
repetition which is backed up by visual and audio aids. Similarly, good students follow path (Z) which contains
visual aid only or no support. In Figure 1 the proposed approach is presented graphically.
Figure 1 Adaptive Repetition
Path (X) reduces cognitive load on students and is specially designed for weak learners. Path (Y) facilitates
average learners. In Path (Z), the cognitive support is small and it enforces the student to actively explore the
environment during the learning process. It is designed for good learners.
III. Implementation
For the purpose of experimentation and analysis of the proposed approach we developed a Virtual
Class room as shown in Figure 2. The environment was developed in MS Visual C++ 2008. OpenGL were used
for rendering the graphics of the environment. Both Mouse and keyboard were used for interaction with the
virtual environment. Simple Virtual Hand (SVH) is used for selection and manipulation of objects. The students
get cognitive aids in the form of text, arrow and sound when he/she select objects within the environment. The
students can see their performance in the environment on a board when they complete the learning module.
Figure 2 Virtual Class Room

IV. Experimentations
In order to perform experiments, we randomly selected 90 students both male and female of class 10th
from 20 different schools and colleges. Based on their academic history and consultation with teachers, the
students were divided into three groups as weak, average and good students. Students were taught how they will
navigate and manipulate objects inside the virtual environment to perform their experiments. All students were
asked to perform the experiment “To prepare 100 cm3 of 0.01 M Oxalic Acid Solution from the given 0.1 M
Solution” repeatedly with the help of simulated environments. The process of repetition was stopped when a
student was declared as good learner by the system and he/she maintains this capability in the next repetition. In
Figure 3, two students are performing their experiments. The data was collected for weak, average and good
students and is summarized as below.
Figure 3 Students performing experiments
4.1 Results and analysis
 Average No. of Adaptive Repetitions
From the experimental data, it was observed that weak students performed 5 adaptive repetitions (on average) to
actively explore the environment while average and good students performed 3 adaptive repetitions. These
results are summarized in Figure 4 as below.
Figure 4 Average No. of Adaptive Repetitions
 Average No. of Errors
On average, Weak students performed 6 errors with standard deviation of 2.77 when they were
repeating the experiments using the proposed approach. Average students performed 4 errors with standard
deviation of 1.97. Similarly, 3 errors were performed by good students and their standard deviation was 0.91.
Figure 5 summarizes these results.

Figure 5 Average No. of Errors
V. Discussion
As mentioned before, the process of repetition was stopped when the student was declared as good
learner by the system and he/she maintains this capability in the next repetition. Weak students performed 5
adaptive repetitions while average and good students performed 3 adaptive repetitions to fulfill the above
criteria. In other words, students were able to perform the experiment without the help of cognitive aids. In their
final repetitions, they were less dependent on aids and performed the task successfully. Below we clarify these
statements.
The proposed approach gradually minimizes the support of aids and enforces the students to perform
the task without the help of cognitive aids. In the initial stage the student is not familiar with the virtual
environment. He/she performs more errors, take long time and get fewer score in the test. Therefore, the LDF
value is small for him/her. He/she is considered weak student by the system and follows (path X) for repetition
containing full support of aids which help him/her in performing their work. When the student repeats the
learning module, his/her knowledge is increased about the environment and experiment. He/she gets high value
of LDF which decreases the amount of aids for them. As a result students are forced to perform the work
without the help of these aids which enable them to explore the environment.
VI. Conclusion
In this study we proposed a strategy for active exploration of adaptive 3D-VLEs. The approach enables
the students to use cognitive aids while at the same time he/she is actively involved in the learning process and
exploration of the environment. From experimental results we concluded that weak students need 5 adaptive
repetitions while average and good students need 3 adaptive repetitions to actively explore the environment and
perform the task independently in non-supervised environment.
References
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