The document summarizes research on emotions in connectivist learning experiences. It finds that the ratio of negative to positive emotions experienced was much higher than in previous studies. Both negative and positive emotions followed similar patterns across learning activities, though their intensity varied. The research also found that negative activating emotions can have positive effects on student performance if addressed properly by teachers through early intervention to avoid negative deactivating emotions. The work is part of a PhD project exploring emotions and learning in connectivist environments.
2. This work
Based on scientific article:
Aldahdouh, A. A. (2020). Emotions
among students engaging in
connectivist learning experiences.
The International Review of Research
in Open and Distributed Learning,
21(2), 98–117.
https://doi.org/10.19173/irrodl.v21i2.
4586
2
4. Achievement
emotions
• The most often reported by
students engaging in learning
context
• The most influential on the
students’ motivation and
academic performance
4
6. Connectivism
• An emergent learning theory in
the distance education field.
• It assumes that knowledge has a
structure and it is better be
conceived as a network.
• Students, books, Internet and
others are all nodes in the
knowledge network. 6
Read also our previous articles:
Aldahdouh,A.A., Osório, A. J., & Caires,
S. (2015). Understanding knowledge
network, learning and connectivism.
InternationalJournal of Instructional
Technology and Distance Learning,
12(10), 3–21. Retrieved from
https://papers.ssrn.com/sol3/papers.cfm
?abstract_id=3063495
7. To learn in
connectivism
• To traverse learning network.
• To aggregate connections.
• To remix and repurpose information.
• To share your knowledge with others.
7
Read also our previous articles:
Aldahdouh, A. A. (2018). Jumping
from one resource to another: how
do students navigate learning
networks? International Journal of
EducationalTechnology in Higher
Education.
https://doi.org/10.1186/s41239-018-
0126-x
8. Watch his
activity and
think-aloud
retrospectively
A participant
searched for a
solution freely
while recording
the activity
Each participant
received 10 tasks
individually, and
one by one task.
Method
8
Find our articles below:
Aldahdouh, A. A. (2020). Emotions
among students engaging in
connectivist learning experiences.
The International Review of Research in
Open and Distributed Learning, 21(2),
98–117.
https://doi.org/10.19173/irrodl.v21i2.45
86
15 participants signed the informed consent,
of whom 9 participants completed the 10
tasks of the experiment.
13. Results
13
The propose model to interpret
how negative emotions impact
students performance positively
Negative-
activating
emotions
Negative-
deactivating
emotions
Negative
effects
Frequent
failure
14. Discussion
14
Find our articles below:
Aldahdouh, A. A. (2020). Emotions
among students engaging in
connectivist learning experiences.
The International Review of Research in
Open and Distributed Learning, 21(2),
98–117.
https://doi.org/10.19173/irrodl.v21i2.45
86
• The overall negative-to-positive emotion ratio was
found to be 4.85:1, far higher than that of previously
reported ratios.
• Emotions shared the same pattern across all activities,
although the intensity of feelings differed significantly.
• Rather, the role of teacher is to keep one’s eyes open
for frequent failure by students and to intervene before
the negative-activating emotion develops to negative-
deactivating emotion.
• Teachers should inform the participants of the high
level of negative emotions they may feel.
15. This work is
prat of a PhD
project
15
• Aldahdouh, A. A. (2020). Emotions among students engaging in connectivist learning
experiences. The International Review of Research in Open and Distributed Learning, 21(2), 98–
117. https://doi.org/10.19173/irrodl.v21i2.4586
• Aldahdouh, A. A. (2019). Individual learning experience in connectivist environment: A
qualitative sequence analysis. International Journal of Research in Education and Science, 5(2),
488–509. Retrieved from https://www.ijres.net/index.php/ijres/article/view/536
• Aldahdouh, A. A. (2018). Visual Inspection of Sequential Data: A Research Instrument for
Qualitative Data Analysis. The Qualitative Report, 23(1631–1649). Retrieved from
https://nsuworks.nova.edu/tqr/vol23/iss7/10
• Aldahdouh, A. A. (2018). Jumping from one resource to another: how do students navigate
learning networks? International Journal of EducationalTechnology in Higher Education.
https://doi.org/10.1186/s41239-018-0126-x
• Aldahdouh, A. A. (2017). Does artificial neural network support connectivism’s
assumptions? International Journal of InstructionalTechnology and Distance Learning, 14(3), 3–
26. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3063496
• Aldahdouh, A. A., & Osório, A. J. (2016). Planning to design MOOC?Think first! The Online
Journal of Distance Education and E-Learning, 4(2), 47–57. Retrieved from
https://www.tojdel.net/journals/tojdel/articles/v04i02/v04i02-06.pdf
• Aldahdouh, A. A., Osório,A. J., & Caires, S. (2015). Understanding knowledge network,
learning and connectivism. International Journal of InstructionalTechnology and Distance
Learning, 12(10), 3–21. Retrieved from
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3063495
16. Thank you
Alaa AlDahdouh
PhD in Educational
Technology
Independent researcher
AlaaAldahdouh@gmail.com
ORCID, Google Scholar
LinkedIn, ResearchGate,
Twitter
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