Learning Analytics is an educational application of web analytics and it is used to analyze the learners’ activities on educational platforms, in order to predict their behaviour. Learning Analytics reflects a field at the intersection of numerous academic disciplines and is using different methods like: statistics, visualizations, data/web mining, and social network analysis. Social network analysis aim is to discover patterns in individual interactions. Network analysis is based on the intuitive notion that these patterns are important attributes of the individuals’ lives. The social network analysis can be made with different software applications and it’s very important to support networked learning. Learning Analytics tools for social network analysis are providing a framework for interpreting and developing an understanding of the observed patterns of exchanges that occur between social actors. The on-line / blended educational programs from the last decades have collected an enormous quantity of data. These data include initial evaluation information, data from the training process and results of the training program. These data can be used to validate the learning analytics theory. Since the definition and purpose of learning analytics is still disputed, comparing the predictions results obtained on the basis of initial data with the final results obtained by learners can contribute to the validation of such applications. This article present the findings and conclusions obtain after analyzing series of forum posts on the educational platforms used in teachers training programs.
Keywords: Learning analyticssocial network analysisteachers trainingeducational platforms
Learning analytics is an educational form of web analytics that aim to measure, collect, analyze and
report data about learners and their context in order to optimize the environment in which occur.
According to Ferguson (2012), “learning analytics is an emerging field in which sophisticated analytic
tools are used to improve learning and education. It draws from, and is closely tied to, a series of other
fields of study including business intelligence, web analytics, academic analytics, educational data
mining, and action analytics”.
Business Intelligence focuses on computational tools to improve organizational decision-making
through effective fusion of data collected via various systems. This term is well-known for
transforming big quantity of data into decision making capabilities.
Data Mining is the field concerned with employing large amounts of data to support the discovery
of novel and potentially useful information. From data mining was developed the field of Educational
Data Mining (EDM) “an emerging discipline, concerned with developing methods for exploring the
unique types of data that come from educational settings, and using those methods to better understand
Even if some authors are considering that
Siemens & Baker (2012) made a comparison of the LAK and EDM. EDM has strong origins in
educational software and student modelling, has a greater focus on automated adaption and use
technique and methods like: classification, clustering, Bayesian modelling, relationship mining,
discovery with models, visualization. LAK has stronger origins in semantic web, intelligent
curriculum, outcome prediction, systemic interventions, has a greater focus on informing and
empowering instructors and learners and use techniques and methods like: social network analysis,
sentiment analysis, influence analytics, discourse analysis, learner success prediction, concept analysis,
sense making models.
Dringus (2012) consider that Learning Analytics require a set of conditions when are applied in
online courses: must obtain meaningful data, must have transparency, must yield from good
algorithms, must lead to responsible assessment and effective use of the data trail, must inform process
Social Network Analysis
Networked learning involves the use of ICT to bring together learners, tutors, learning communities
and educational materials. “Social network analysis is a perspective that has been developed to
investigate the network processes and properties of ties, relations, roles and network formations, and
to understand how people develop and maintain these relations to support learning” (Haythornthwaite,
& de Laat, 2010) cited by Buckingham, & Ferguson (2012).
The results of a social network analysis might be used to: (a) identify the individuals and teams that
play an important role; (b) discover information disturbs, isolated individuals and teams; (c) emphasize
opportunities to accelerate knowledge flows across functional and organizational boundaries; (d)
strengthen the efficiency and effectiveness of existing, formal communication channels. (e) increase
awareness on the importance of informal networks. (f) mutual support; (g) improve innovation and
learning; (h) improvement strategies (Serrat, 2010).
Social network analysis is a useful tool for investigative online learning because of its focus on the
change of interpersonal relationships, and offers the potential to recognize interventions that are
possible to increase the potential of a network to support the learning of its actors by relating them to
contacts, resources and ideas.
The sources of analyzed data
The on-line / blended educational programs from the last decades have collected an enormous
quantity of data. These data include initial evaluation information, data from the training process and
results of the training program. These data can be used to test and validate many subjects of the
learning analytics theory.
Since the definition and purpose of learning analytics is still disputed, comparing the predictions
results obtained on the basis of initial data with the final results obtained by learners can contribute to
the validation of such applications.
Three educational projects implemented at national level in Romania were selected as source of
initial data. These projects have supported blended courses for teachers training. Table
name of the projects, their acronyms and the version of the educational platform used in online course.
The online courses were conducted during different number of months and in the frame of the courses,
the learners participated in discussion forums.
SNAPP (Social Networks Adapting Pedagogical Practice) is a social network analysis tools, that
renders a social network diagram from the extracted forum interactions. This software was developed
to work with forums from specific learning management system:
social network map provides an overview image of the users that communicate each other and the level
of their communications. The software represents each forum participant by a node, and interactions
between participants are characterized by a line. The number displayed on the line represent the
number of posts between two nodes.
According to Dowson et al. (2010), a network diagram of the students’ online conversation can
provide useful information such as: (a) highlights the disconnected students; (b) reveals students who
provide information to other students in the course; (c) show if it forms a learning community within
the course and how big is it; (d) allow students to benchmark their performance without the need for
marking; (e) allow identification of potentially high and low performing students in order to take action
before mark their work; (f) provide a snapshot before and after a teacher changed the learning activity
Results and discussions
SNAPP software was used to analyze several discussion forums from the educational platforms of
the three projects mentioned above. An attempt was made to identify disconnected learners, those with
outstanding performance, information brokers and internal groups. SNAPP statistics is displaying the
list of forum participants, beginning with the most active users and ending with the less active users of
the analyzed forum.
To identify the disconnected learners, most active learners and learning groups inside the course, it
was analyzed the social network diagram. The disconnected users are represented by nodes at the edge
of the chart. Central node represents the information-broker in that forum and the interconnecting lines
are marked with the number of posts of those users.
Using the same methodology, several discussion forums from the ForEdu, EduTic and ProWeb
projects were analyzed. For the two projects (ForEdu and EduTic), it was compared the list of the most
active users and the list of the less active users with their learning results in the frame of the teaching
program. It was noticed that the results do not show the real relationship between the data presented in
the social network diagram and the learners’ final course results. Although, it was confirmed the initial
theory that usually the disconnected users (from the diagram) are obtaining weaker results, there are
also contradictory situations.
Working with other software and data
The work presented above had as main purpose to find opportunities and solutions to implement
Equipping the Next Generation for Active Engagement in Science Project, that run online courses for
teacher training programs, particularly, the online course “
service science teachers, in 2015.
The Engage project (https://www.engagingscience.eu) has the main objective to make Science
lessons more attractive, taking into account the promotion and implementation in the classroom of
different interactive-participatory teaching strategies, based on involvement and reality investigation,
identifying and testing alternative solutions, which target to let young students to think and apply their
scientific knowledge, and consequently to make responsible decisions, in accordance to Responsible
Research and Innovation dimensions (Petrescu et al., 2015).
In the frame of the Engage project,
research. In this project, the online courses are hosted on a
Open edX provide some tools for learning analytics, but at the moment, do not offer a tool for social
network analysis. At the same time, the SNAPP tool is incompatible with the discussion format in the
open edX platform.
During the research, some challenges and issues were identified: technical issues on running the
SNAPP software, but also challenges for correct interpretation of the obtained data. Even if the used
educational platform was Moodle, the SNAPP software didn't work on the data recorded in the forums
of the ProWeb Project. The application is parsing the ProWeb discussion forums but it is reporting zero
Also, it was identified a number of difficulties in using application analysis software. In large
forums it is complicated to analyze the social network diagram. Using too many forums it will make
very difficult the analysis.
The results obtained are not very conclusive. In general, the active students in the forums obtained
good evaluation results and some inactive students didn’t finish the training program. There are also an
important number of students inactive in forums who obtained good results at final evaluations and
they completed the training program. This can be explained by the profile of the Romanian teachers.
They have to apply for teacher training programs in order to obtain the required number of credits and
they do not have time also for course additional activities. For these reasons, they are focused on
accomplishing the tasks and assignments, and not getting involved in forums’ discussions.
It can be noticed that in order to obtain better results, the social network analysis must be combined
with some other learning analytics techniques.
This work was funded through the Seventh Framework Programme Project “ENGAGE - Equipping the Next Generation for Active Engagement in Science Project” - a coordination and support action under FP7-SCIENCE-IN-SOCIETY-2013-1, ACTIVITY 5.2.2 “Young people and science” - Topic SiS.2013.2.2.1-1: Raising youth awareness to Responsible Research and Innovation through Inquiry Based Science Education. This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration, under grant agreement no 612269. The support offered by the European Commission, through the project mentioned above, is gratefully acknowledged.
- Baker, R.S.J.D., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17.
- Buckingham Shum, S., & Ferguson, R. (2012). Social Learning Analytics. Educational Technology & Society, 15(3), 3-26.
- Dawson, S., Bakharia, A., & Heathcote, E. (2010). SNAPP: Realising the affordances of real-time SNA within networked learning environments. Paper presented at the Networked Learning Conference 2010, Aalborg, Denmark.
- Dringus, L.P. (2012). Learning Analytics Considered Harmful. Journal of Asynchronous Learning Networks, 16(3), 87-100.
- Ferguson, R. (2012). The State of Learning Analytics in 2012: A Review and Future Challenges. Technical Report. Knowledge Media Institute: The Open University, UK. http://kmi.open.ac.uk/publications/pdf/kmi-12-01.pdf
- Haythornthwaite, C., & de Laat, M. (2010, May). Social networks and learning networks: Using social network perspectives to understand social learning. Paper presented at the 7th International Conference on Networked Learning, Aalborg, Denmark.
- Petrescu A.M.A., Gorghiu L.M., Dumitrescu, C. (2015). The ENGAGE Project - A Facilitator Context for Promoting Responsible Research and Innovation in Science Lessons. Central & Eastern European LUMEN Conference: LUMEN NASHS 2015: Chisinau, Republic of Moldova: 11-13 September 2015.
- Serrat, O. (2010). Social network analysis. Washington, DC: Asian Development Bank.
- Siemens, G., & Baker, R.S. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In: Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
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04 October 2016
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Communication, communication studies, social interaction, moral purpose of education, social purpose of education
Cite this article as:
Bîzoi, M., Suduc, A., & Gorghiu, G. (2016). Exploring Social Network Analysis in Blended Learning Teacher Training Programs. In A. Sandu, T. Ciulei, & A. Frunza (Eds.), Logos Universality Mentality Education Novelty, vol 15. European Proceedings of Social and Behavioural Sciences (pp. 122-127). Future Academy. https://doi.org/10.15405/epsbs.2016.09.15