E-supporting Performance Styles based on Learning Analytics for Development of Teaching Practices in Teaching Science

International Journal on Integrating Technology in Education (IJITE) Vol.9, No.2/3, September 2020
DOI :10.5121/ijite.2020.9301 1
E-SUPPORTING PERFORMANCE STYLES BASED ON
LEARNING ANALYTICS FOR DEVELOPMENT OF
TEACHING PRACTICES IN TEACHING SCIENCE
Jielan Elsayed1
, Laila Maawad2
, Zeinab khalifa3
1
Department of Curriculum and Instructions, Ain shams University, Cairo, Egypt
2
Curriculum and Teaching Science, Ain shams University, Cairo, Egypt
3
Education technology, Ain shams University, Cairo, Egypt
ABSTRACT
This study aims to identify the effectiveness of delivering electronic supporting performance styles that are
based on learning analytics for the development of teaching practices in teaching science, moreover, the
Electronic and face to face supporting performance styles will deliver according to the data analytics that
extracted from observations, (participating rate- page views) data from platform, therefore, to determine
the effectiveness, the researchers design observation rubric based on teaching practices standard that
extract from (ASTE/NSTA, AITSL) to observe teaching practices of student science teachers. Regarding the
participants they were science students who enrolled in educational diplomas, researchers use the mixed
method in collected data and quantitative data, furthermore, they will study a supportive program of
considering data analyses to develop their teaching practices in teaching science, the results exposed that
providing a supporting program that considers learning analytics, helps increase teaching practices in
teaching science for student's science teachers.
KEYWORDS
E-supporting styles, Learning analytics, teaching practices.
1. INTRODUCTION
With regard to the obstacles of e-training environments, it is difficult for the instructor to follow
the learners during their learning in the electronic environment, and to overcome this, learning
analytics have appeared, which are collected and analyzed data in the electronic learning
environment during learning process , and in light of the analytics of this data, the rules and
regulation of learning are designed, and the learners were tracked , further, during improve the
learning environment through learning analytics, the performance of learners can be improved
also, by providing the help they need because they are to develop their performance and skills,
moreover, we can also analyze the advantages of the personal learning environment, where the
trainees can have effective educational experiences (Dietz-Uhler & Hurn 2013)
In addition, there are other ways work with data analytics to improve e-learning environment and
facilitate learning, that is delivering Support performance styles which considered one of the
instructional variables that learners need in the e-training environment, and face to face, the goal
of providing supporting performance is to help and mentor learners during learning until they
were able to learn individually(Kert & Kurt, 2012).
Furthermore, Supporting performance was depended on specific factors, such as time of support
immediately or delayed, and how to provide support in (videos-pictures-diagrams), even if the

2
support was a technical support belong to platforms such as navigations, or content cantered
support related to the content (Askar, 2018).
In addition, student science teachers are the most important part of the learning process, so
developing teaching practices for teachers leads to student academic performance and
personalities. (Gibbs & Coffey, 2004).
Moreover, developing teaching practices is linked to the procedures for applying theoretical
issues and to improving pre-service teachers before they go to school, therefore the development
of teaching practices is essential to teachers and it reflects on students too.(Taş & Karabay,
2016).
In terms of, this study, authors tried to identify and achieve the effectiveness of delivering
electronic supporting performance styles based on learning analyses for teaching practices
The structure of paper includes the following sections: literature review, methodology, results
and discussion, conclusions, recommendations and directions for future research, and references.
1.1. Study Purpose:
Authors sought to identify the effectiveness of design E-supporting performance styles based on
learning analyses for developing instructional practices in teaching science, and we are guided by
the following research questions:
1.2. Study Questions:
How we can design the E-supporting styles?
How we can use the data from learning analytics in supporting performance?
What is the effectiveness of using E-supporting styles based on learning analytics for
development of teaching practices for pre-service teachers?
1.3. Study Significance and Expected Outcomes
The study is important in terms of the following:
This study aims to deliver E-supporting performance styles based on learning analytics.
This study may inform science educators and researchers may use the findings of this research to
inform their practice and as a springboard for additional research into the influence of designing
E-supporting styles for development of in-service science teachers.
2. LITERATURE REVIEW:
2.1. Supporting Performance Styles:
Considering, performance supporting based on the social constructivism theory by Vygotsky,
and Vygotsky stresses that social interactions are essential for development learning process and
learner's performance, and the importance of the role of the teacher and learners in teaching each
other, in addition, the social constructivism theory assumes that firstly learner will build his own

3
cognition, after that he needs supporting and assistant to complete his/her building cognitive
construction process (Verenikina, 2010).
Regarding the definition of Electronic performance support, it refers to an Internet-based system
that aims to provide support and guidance to learners in order to improve their performance and
skills, and provide personal control for the learner in the learning environment (Kert & Kurt,
2012).
Moreover, Askar (2018) Define E-performance support as a suitable and easy-to-use education
aid design for e-learning, whether this educational aid is for educational content or non-
educational aid to create planned changes in learning and performance.
McManus and Rossett (2006) State that e-support performance is a set of tools that support
learner performance and interaction by providing information and resources during learning and
guidance.
In this study we can refer to e-support performance as types of support and guidance tools that
provide to diploma students such as: Support related to scientific content, navigation support,
that will help students achieve their tasks.
2.1.1. Principles of Supporting Performance Styles
There are mainly three core principles for a successful supporting performance, firstly: assessing
performance before any kind of support, secondly: There are differences in supporting, it might
be when student demand or without student demand, thirdly: provide support for bunch of
students or individually or it could be among students (Schaik, Pearson, & Barker, 2002).
Barker, et al (2007) Describe a set of principles as follows: Use a specific style of supporting
performance, it could be in shape of text, videos, images, further, offer a synchronous support,
and avoid delaying supporting, moreover, providing content support not just a technical support
related to using the platform, design the supporting styles whatever, were text, videos, images,
charts in a simple way far from complex and easy to use, and when present procedures of any
task it must be step by step.
As noted above, we can show that there is a critical principle we will follow and that principles
are: assessing student performance before providing support, analyzing students ' performance
before providing any support,
Describe ways to provide support (text-videos diagrams), identify the most important situations
students need support first, offer individual or group support, design support in a simple design.
2.2. Learning Analytics:
Learning analytics refers to the process of collecting, analyzing, measuring, and preparing a data
report related to student's interactions through e-learning environment and this report leads to
understanding the learning process and improving it.(Bronnimann, West, Huijser, & Heath,
2018; Sahin & Yurdugül, 2017).
In addition, (Clow, 2013; Marzouk et al., 2016) refer to learning analytics as reports and
visualizations aim to identify and summarize the learning activity during learning process in e-
learning.

4
The literature consent that learning analytics are collecting, measuring, and analyzing data and
performance of learners, after that prepare reports related to learner's performance in order to
improve learner's performance and the educational process.
In the context of this study, learning analytics is defined as collecting and analyzing data
belonging to student's learning performances, also this data will be inside or outside the
electronic training environment in order to provide support suitable for their needs.
2.2.1. Types of Learning Analytics:
Wise, Zhao, and Hausknecht (2014) Divided learning analyses into two categories: Blended
learning analyses occurs in e-learning and in real classrooms to track student learning
interactions, through this data we can guide and support learners, Secondly, External learning
analytics: include the learner's data for instance: direct learner's interactions with their teachers or
with each other, and with this data we can create personalized learning environments, and create
social relations between students and teachers
In this study we will depend on blended learning analysis whereby analyze learners' performance
during learning process ( face-to-face), and in e- training environment, because the supportive
content will be presented in face-to-face sessions that include discussions and applications on
practices and the other part will be through an e- training environment.
2.2.2. Types of Data:
(Dietz-Uhler & Hurn, 2013; Gregg, Wilson, & Parrish, 2018) State that there are two types of
data, Qualitative data: extracted by the interactions between teacher and learners directly, such
as: (the number and type of questions that learners ask in discussion groups - personal interviews
- learners ’past experiences - their personal experiences - the number of electronic messages that
are sent to the teacher via e-mail), Quantitative data that can collect and analyze during the
interaction of learners with the electronic learning environment such as: (exam degrees - final
grades - the number of times the content is available through the electronic system - the date and
time of the visit - the number of discussions on specific publications - the number of discussions
and publications are read - The type of resources that are displayed through the environment -
success rates - assignments).
This classification will be used to suit the educational content and general diploma students
(online) Science Division, and it will be data on the electronic training environment, namely:
entry rates on the electronic training environment, learning resources used by learners, the
number of visits to additional sources, comments on publications, as for the tools of the class
environment, will be the tasks, interactions with the teacher and with each other, and these tools
were used for their clarity, easy for analysis and extracting results from them.
2.3. Teaching Practices:
There are many institutions that have identified teaching practices, and include them in the
teaching development standards for teacher preparation, as the AITSL(Australian institution
standards for teachers and leadership) standards create set of standards for teaching practices, and
divided into 3 main domains (Hill, etal;2018)
A-The first domain is: the professional knowledge of teachers and it includes two indicators, the
first is for the teacher to know his students, and the appropriate way for their learning, the second
defines the educational content and methods of teaching.

5
B- The second domain, is teaching practices: includes three main indicators, the first is that
teacher can plan and implement teaching effectively, the second: providing support and a safe
learning environment to students, the third: evaluating students, delivering relevant feedback, and
writing reports about students behavior.
C- The third domain: teaching inclusion: it includes two indicators: the first teacher can engage
into a teaching educational environment, and the second is engaged with colleagues, parents, and
the whole society.
Furthermore, the ASTE (Association for Science Teacher Education) and NSTA (National
Science Teacher Association) where they prepared a list of criteria for teacher science
preparation, and consisted of six main criteria (Morrell, etal;2020):
A-The first standard: knowledge of the scientific content it describes an effective science teacher
as who can understand and clarify contemporary information and practices in science and
engineering and links the key ideas with common concepts and scientific and engineering
practices.
B - The second standard: Knowing the nature of science, it describes the effective science
teacher: Who can plan the learning process and for the units of study, spread equality and
acceptance values among all students, and understands the nature of students' learning as the
development of their scientific content, their skills and their own habits of mind.
C-Standard 3: Knowledge of learning environments, which includes planning to engage all
students while learning science, building a socially equitable learning environment, and
clarifying learning goals whose knowledge is later.
D-Standard 4: Safe, in which the science teacher clarifies classroom or laboratory chemical,
physical and biological safety rules, also clarifies ethical aspects when using living organisms
and chemicals.
E- Fifth Standard: The impact of learning on students, where the science teacher can give
evidence of student learning, and applying the main ideas, common concepts, scientific and
engineering practices.
F - The Sixth Standard: Professional Knowledge and Skills. The science teacher can constantly
strive for professional development, whether in his knowledge of the content or his teaching
methods and engage all students as part of the science learning community.
In this study we exclude our teaching practices list from those standards and modify regarding to
the natural of students, the aim of the study, and Egyptian context, so we classify the teaching
practices in three main aspects:
A - Planning an appropriate learning environment for all students, and it is defined as a set of
procedures that begin to write learning goals based on the scientific content and identify learning
resources and classroom activities.
B- Designing and establishing an effective learning environment for students, intended to focus
on developing, supporting, engaging students in social and responsibility relationships between
students during the learning process.
C-Assessment of student learning, defined as the process of observing and analyzing student
performance through formal evaluation as test scores or informal as teacher self-reflections

6
3. METHODOLOGY
This research used a mixed-methods design, which is a procedure for collecting, analyzing, and
“mixing” both quantitative and qualitative data during research to understand a research problem
more completely(Creswell, 2002).
This mixed-methods design allowed the researcher to capture both quantitative data and rich
qualitative descriptions that would not have otherwise been available by using one approach.
Moreover, when used in combination, quantitative and qualitative methods complement each
other and provide a more complete picture of the research problem (Creswell & Clark, 2017;
Greene, Caracelli, & Graham, 1989).
The participates were a student's science teachers who enrolled in educational diploma (16) they
received the supporting styles based on learning analytics that excluded from platform,
observation.
3.1. Data Collection Instruments:
This study used observations rubric: that excluded from (ASTE/NSTA, AITSL) standards, and it
contains 57 items describe the essential teaching practices for science teachers.
Data analytics from Canvas platform: it's provided by Canvas platform.
3.2. Data Collection Procedures:
Firstly we design the instruments and publish the content of the program and start the experiment
with the experimental group during two weeks we observe their learning and participations on
the platform and the most page they viewed and conduct a discussion session to discuss with
them the advantages and obstacles they faced during their learning , after that we modify the
content and the instruments according to data analysis.
Then we continue the rest of the program until we finished the number of the sessions were 14
session each session was 1 hour and half and it was face to face in first three sessions and just
uploading their activities on line via platform, and the rest of the sessions were online via
platform , zoom meeting cloud, WhatsApp group. As they recommended so, we adapt our
instruments according to students needs and point of weakness that need to support.
After finishing the sessions, we applied the observation rubric again to determine the differences
between the pre and post applying.
3.3. Data Analytics:
We applied the instruments and enrolled students' teachers in platform in terms of have a learning
analytics, to make decision about the aspects of modifying and editing the content and structure
of the instruments and platform.

7
Table 1. about the aspects of modifying and editing the content and structure
of the instruments and platform.
Aspects of
application
Before the data analytics after the data analytics (the final
edition)
Observation
rubric
Consists of 57 items describe 3 major
scales contain 14 subscales, and 57
performance indicators
Consists of 48 items describe 3 major
scales contain 12 subscales, and 41
performance indicators, also we
remove one subscale because the
participants didn't need any support
in it, also 16 indicators for the same
reason.
Program
content
Consist of 3 main topics divided into
14 topic and it is arranged by order as
the list of teaching practices for
science teachers, (Planning a suitable
learning environment for all students
and it is contain ( writing learning
objectives – understand the scientific
content-plan for classroom activity-
plan for using the educational aids-
plan for teaching strategies-time
management- writing lesson plan)),
Designing and establishing an
effective learning environment for
students which contain 6 subtopics
(arranging interactions and probing
questions- link learning process with
students' interests and experiences-
guidance students' learning and
modify teaching methods according to
it- enhance the social relationships
and responsibilities- establish safety
learning environment), Assessment of
student learning include one subskill
(applying different ways of students'
assessment)
It was the same in some aspects , it
consist of 3 major topics divided into
13 sub topics, the sub topic that
entitle writing lesson plan was
removed cause they have experience
in it , also some items in writing
learning objectives , further second
major topic entitled Designing and
establishing an effective learning
environment for students specifically
in arranging interactive and probing
questions, the part of probing
questions was also deleted cause they
studied before and don't need support
in it, moreover in subtopic enhance
the social relationships and
responsibilities among students ,
some of the items were removed for
the same reasons.
In terms of priority, we change the
order of presenting topics according
to students' teachers need support in,
so we start with understand scientific
content- writing learning objectives-
plan for teaching strategies- time
management- plan for classroom
activity-plan for using the
educational aids).
Canvas
platform
Before data analytics we deliver the
content and all tools that available on
canvas such as (discussions,
collaboration, syllabus-assignments-
announcement-files-people-rubrics-
conferences)
We deactivate some tools cause
students' teachers don't want to use or
feel disruption so instead of using
discussion on canvas we do it by
zoon cloud meeting, and WhatsApp
group, the assessment sessions, cause
they feel not comfortable to use quiz
tool on canvas.

8
The previous table exposed that the changes of structure of the observation rubric, and the
Canvas platform, and how the learning analytics contributed in modifying those aspects.
Moreover, there are aspects that we tried to improve in content, such as adding more videos,
more discussions to exchange ideas and opinions, and have a group reflection, rearrange topics
according to the priority of students’ needs.
Also, disable some tools that will be an obstacle for students to learn better, after each discussion
and reflective session, we have something to improve whether, in the content or performance or
platform.
In terms of deficiencies, we found that there are tools to consider an obstacle to students
(collaboration, conferences, discussion), and we discover that by trying these tools with students
and asking them about their needs and how they found using the tool, in fact most of their
responses conclude that there are no value to them and it was too slowly and didn’t fit them.
4. RESULTS AND DISCUSSIONS:
The section presents findings after quantitative data analysis. Moreover, it begins with the
descriptive statistics of learning analytics, then the pre/post experiment, Finally, a discussion of
the results is reviewed.
4.1. Data Analytics Statistics:
4.1.1. Data Analytics that Excluded from Canvas Platform:
Table 2. illustrate the proportions of page views and participations in analysis period for two weeks.
participation Page views Date
65 8.338 2/10–2/16
247 15.675 2/17–2/23
The table above shows the rate of participation and page views for two weeks of data analytics, it
exposes that the proportion increase gradually, and it is indicated that the participant feels
comfortable in learning via platform.
4.1.2. Data Analytics that Excluded from Observation Rubric
Table 3. descriptive statistics of observation rubric:
teaching practices Total
degree
Min of
performance
max min mean St-
deviation
percentage
Planning a suitable
learning environment
for all students
84 28 50 42 44.06 2.08 52.45%
Designing and
establishing an effective
for students
72 24 44 42 43.38 0.80 60.25%
Assessment of student
learning
15 5 5 5 5 0 100%
overall 171 57 97 90 92.44 1.89 54%

9
The table shows that the mean degree of responding about Assessment of student learning was
(5) and it doesn't even reach the minimum performance, it indicates that they need to support in
this aspect deeply, after that, Planning a suitable learning environment for all students became the
next aspect that need to support the percentage manifest that 52.45% of teaching practices can
apply in their classrooms, then Designing and establishing an effective learning environment for
students came lastly in order of practices that need to improve in classrooms. According to Data
we pointed above, we removed some performance indications from those practices that didn't
need to be supported as mentioned previously.
4.2. Data Analysis Quantities Statistics:
In this section we examine the hypothesis of the study, authors use (Mann- Whitney-U) equation
to identify the difference between the pre/post applying observation rubric after study a
supportive program based on learning analytics.
Table 4. the differences between the mean of students' science teachers' grads
in applying pre/post observation rubric.
teaching practices Experimental
group
total mean St-
deviation
Z
value
sig
Planning a suitable
for all students
pre 63 42.68 8.46 3.36 Sig
0.01
post
63 57.75 4.78
Designing and
establishing an effective
for students
pre 45 27.06 8.74 3.41 Sig
0.01
post
45 42.87 2.02
Assessment of student
learning
pre 15 12.00 2.50 2.36 Sig
0.05post 15 14.00 1.31
overall pre 123 88.56 19.89 3.31 Sig
0.01post 123 114.62 7.62
Table exposes that there is a significant difference between the pre and post observation rubric
for the post applying and z value that in table bigger the calculated value, so it is significant on
0.01 level for all aspects except Assessment of student learning aspect, the significant was on
0.05 level, and in the observation rubric overall.
Table 5. the rate of page views and participations after modifying platform, and
content topics during learning process.
participation Page views date
885 16.556 2/24–3/1
1.704 30.481 3/2–3/8
519 28.222 3/9–3/15
5.519 127.111 3/16–3/22
556 16.111 3/23–3/29
1.222 29.778 3/30–4/5
704 26.667 4/6–4/12
666 14.185 4/13–4/19
774 17.667 4/20–4/26
519 17.778 4/27–5/3
667 19.407 5/4–5/10
337 15.185 5/11–5/17

10
The table above shows the rate of participation and page views during learning via platform, it
indicate that there are a gradually increasing in the rate of participation and views until the week
from 4/6-4/12 the proportion start to decline in terms of using zoom cloud meetings in all
discussions and meetings and the participation prefer it , so the platform was just for up-loaded
assignments and view the content and videos.
5. CONCLUSIONS
In terms of data analysis, literature review, and findings, we conclude that deliver supporting
performance styles (in groups- immediately -briefly- simple) that based on learning analytics ,
further, how the learning analytics are a vital part in making-decision, and what are aspects that
need support specifically, moreover, learning analytics are a beneficial in designing platform and
use certain tools, also it gives us a predictable achievement if we consider it in establishing and
designing any course, eventually using supporting performance styles in the light of learning
analytics contributed in developing teaching practices in teaching science.
6. RECOMMENDATIONS AND DIRECTIONS FOR FUTURE RESEARCH:
In terms of the results and findings, we recommend using the results and build-up on to spread
the idea of supporting performance during learning process and how it changed the entire
environment in which became more social, personal, and more interaction with the content,
instructor, and among students' teachers.
Moreover, future studies should investigate about: 1) further studies on how to design and
support under-graduated students' performance, 2) research about enhancing different aspects
instead of teaching practices, 3) use specific theory in designing shapes of supporting and
determine the effectiveness of this combination, 4) how can human center design approach effect
on delivering supporting.
REFERENCES
[1] Askar, A. (2018). Mobile Electronic Performance Support System as a Learning and Performance
Solution: A Qualitative Study Examining Usage, Performance, and Attitudes. Turkish Online Journal
of Educational Technology-TOJET, 17(2), 76-88.
[2] Bronnimann, J., West, D., Huijser, H., & Heath, D. (2018). Applying learning analytics to the
scholarship of teaching and learning. Innovative Higher Education, 43(5), 353-367.
[3] Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695.
[4] Creswell, J. W. (2002). Educational research: Planning, conducting, and evaluating quantitative (pp.
146-166). Upper Saddle River, NJ: Prentice Hall.
[5] Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage
publications.
[6] Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student
success: A faculty perspective. Journal of interactive online learning, 12(1), 17-26.
[7] Gibbs, G., & Coffey, M. (2004). The impact of training of university teachers on their teaching skills,
their approach to teaching and the approach to learning of their students. Active learning in higher
education, 5(1), 87-100.
[8] Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-
method evaluation designs. Educational evaluation and policy analysis, 11(3), 255-274.

11
[9] Gregg, A., Wilson, B. G., & Parrish, P. (2018). Do No Harm: A Balanced Approach to Vendor
Relationships, Learning Analytics, and Higher Education. IDEA Paper# 72. IDEA Center, Inc.
[10] Hill, E., Keane, T., & Seemann, K. (2018). Pedagogies and practices for developing innovation
capability: Beyond the AITSL standards. Fostering design-led innovation capabilities.
[11] Kert, S. B., & Kurt, A. A. (2012). The effect of electronic performance support systems on self-
regulated learning skills. Interactive Learning Environments, 20(6), 485-500.
[12] Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J., ... & Nesbit, J. C.
(2016). What if learning analytics were based on learning science?. Australasian Journal of
Educational Technology, 32(6).
[13] McManus, P., & Rossett, A. (2006). Performance support tools. Performance Improvement, 45(2), 8.
[14] Morrell, P. D., Park Rogers, M. A., Pyle, E. J., Roehrig, G., & Veal, W. R. (2020). Preparing Teachers
of Science for 2020 and Beyond: Highlighting Changes to the NSTA/ASTE Standards for Science
Teacher Preparation.
[15] Sahin, M., & Yurdugül, H. (2017). The Framework of Intervention Engine Based on Learning
Analytics. International Association for Development of the Information Society.
[16] Schaik, P. V., Pearson, R., & Barker, P. (2002). Designing electronic performance support systems to
facilitate learning. Innovations in Education and Teaching International, 39(4), 289-306.
[17] Taş, M. A., & Karabay, A. (2016). Developing Teaching Skills through the School Practicum in
Turkey: A Metasynthesis Study. Journal of Education and Training Studies, 4(11), 87-99.
[18] Verenikina, I. (2010). Vygotsky in twenty-first-century research. Paper presented at the EdMedia:
World Conference on Educational Media and Technology.
[19] Wise, A., Zhao, Y., & Hausknecht, S. (2014). Learning analytics for online discussions: Embedded
and extracted approaches. Journal of Learning Analytics, 1(2), 48-71.
AUTHORS
Jielan Elsayed Kamel Hegazy, is a teaching assistant at faculty of education, Ain
Shams university, Egypt , her email address (jielan_elsayed@edu.asu.edu.eg), her
major areas of interests and expertise are: using technology in teaching science at
department of curriculum and instructions, training science teachers whatever there
were in service teachers or pre-service teachers.
Professor: Laila Ibrahim Maawad is a professor at faculty of education , Ain
shams university, Egypt, specialized in curriculum and teaching science,
(email:lailamoawed@edu.asu.edu.eg ), she have experiences in supervising Master
and PHD thesis, participating in several international projects.
Professor: Zainab Hassan KHalifa, is a professor at faculty of education , Ain
shams university, Egypt, specialized in educational technology (email:
zeinab107@hotmail.com) , her areas of experiences and interests are: training
teachers in how to integrate technologies in teaching and learning, participate in
TEVET project that conducted by ERASMUS+, have experiences in supervising
Master and PHD thesis.

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