Connectivism and emotions

Emotions in connectivist learning experiences

• Text-over presentation is a new approach to disseminate knowledge

• Emotion is a poorly addressed and rarely investigated in connectivism

Inspired by voice-over idea and crisis of coronavirus (COVID-19), I propose here a new approach to disseminate knowledge: text-over presentation. Our presentation today addresses an important topic that has been systematically neglected and roughly recognized on connectivism literature: emotion. The rest of the post will be organized as if I were speaking to audience in the conference. You will see the slide first and will read my speech next. Find a link for the full presentation at the end of this post.

Hello everybody! This is Alaa AlDahdouh, and today I am going to present a summary of our results published in the following article:

Article  Emotions Among Students Engaging in Connectivist Learning Experiences

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

Interestingly enough, human has developed contradicting views of emotion over years, grading from stigmatizing emotion of being the illness of mind and the capital of sins to view it more recently as indispensable part of human learning processes. Emotion has often seen messy and complicated because it intertwines with cognition and physiology. Even though, the features of emotion concept have began to unfold recently thanks for control-value theory. Control-value theory views emotions as sets of four interrelated psychological components: affection, cognition, motivation, and physiology. In this view, anxiety, for example, is conceived as a combination of worries (cognition); motives to escape from the situation (motivation); and an increase in blood pressure and brain activities (physiology).

According to Pekrun and colleagues’ qualitative studies, almost all human emotions were reported by the students in test-related context, but few emotions were the most often reported, and the most influential on the students’ motivation and academic performance. They called it, “Achievement emotions”.

Article  The control-value theory of achievement emotions

Pekrun, R., Frenzel, A. C., Goetz, T., & Perry, R. P. (2007). The control-value theory of achievement emotions. In R. Pekrun (Ed.), Emotion in Education (pp. 13–36). https://doi.org/10.1016/B978-012372545-5/50003-4

Article  The control-value theory of achievement emotions

Pekrun, R., & Perry, R. P. (2014). The control-value theory of achievement emotions. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 120–141). New York, NY: Routledge.

In a series of qualitative and quantitative studies, Pekrun and colleagues identified two crossing dimensions that classify nine emotions into four sets of interrelated emotion profiles, as shown. Each emotion profile has different effects on student goals, motivations, and academic performance. We know from previous studies that positive-activating and negative-deactivating emotions have adaptive and maladaptive consequences on student behaviors. The empirical findings of negative-activating emotions were, in contrast, mixed. For example, anger sometimes show negative and sometimes positive or no effect on students performance. Our study attempts to know when and how this specific category produce its positive effect on students.

On the other hand, our study counts on connectivism learning theory. Connectivism is an emergent learning theory with the distance education field. It assumes that knowledge has a structure and it is better be conceived as a network. Students, books, Internet, artificial intelligence agents and others are all nodes in the knowledge network.

Article  Understanding knowledge network, learning and connectivism

Aldahdouh, A. A., Osório, A. J., & Caires, S. (2015). Understanding knowledge network, learning and connectivism. International Journal of Instructional Technology and Distance Learning, 12(10), 3–21. Retrieved from https://t.co/4cHD4bhSql

To learn in connectivism is to traverse knowledge networks, aggregate connections, remix and repurpose information, and share knowledge with others. The aggregation phase has further investigated the results showed that students employed three criteria to connect nodes: self-efficacy, eligibility of resource and feasibility of resource. Connectivists recognized that online learning without explicit guidance from an instructor could be as frustrating as exploring unknown territories without a map. Yet, they argue that negative emotions do have a positive impact on learners’ performance in that they push learners out of their comfort zone. Thus, the results of our study serve them too.

Article  Jumping from one resource to another: how do students navigate learning networks?

Aldahdouh, A. A. (2018). Jumping from one resource to another: how do students navigate learning networks? International Journal of Educational Technology in Higher Education, 15(45). https://doi.org/10.1186/s41239-018-0126-x

We gathered data for this study using retrospective think aloud method. Fifteen of higher education students studying at Palestinian universities singed the informed consent, of whom 9 students completed the experiment. Each student was individually given 10 tasks in succession. Students were free to do what they wanted, but they had to record their activities (e.g., video recordings of laptop screen while searching the Internet). After completing the task, a student watched a his/her recorded activity and reported what was in his/her mind while doing the activity.

Throughout the course of the experiment, the participants engaged in a wide array of learning activities and contacted various resources, as shown below. The implications of the nodes’ distribution on theory and practice have been discussed in our previous works (Aldahdouh, 2018b, 2019).

Article  Individual Learning Experience in Connectivist Environment: A Qualitative Sequence Analysis

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

Article  Jumping from one resource to another: how do students navigate learning networks?

Aldahdouh, A. A. (2018). Jumping from one resource to another: how do students navigate learning networks? International Journal of Educational Technology in Higher Education, 15(45). https://doi.org/10.1186/s41239-018-0126-x

It is quite clear from the figure below that negative emotions were the dominant. The negative-activating category had the biggest share of all emotions reported (766 times; 53.83%). Other categories in descending order were: (a) negative-deactivating (402 times; 28.25%); (b) positive-activating (222 times; 15.6%); and (c) positive-deactivating (33 times; 2.32%). The top five emotions reported were purely negative.

We mixed the learning activities with emotions to figured out that there were a consistent pattern emotions experienced among almost all learning activities. It is evident, in addition, that Internet searching and online communication accounted for most of the emotional arousal in the experiment.

Some examples of emotions reported in the experiment revealed how negative emotions affected the behavior of the participants positively. In the figure below, you can find how anger, anxiety, and confusion induced the students to create new connections or to think of alternative options for solving the tasks.

The destructive effect of negative-activating emotions did occur, but only when the failure was constantly happening. More often than not, the negative effect manifested itself in transforming the negative-activating to negative-deactivating emotion. So, confusion becomes boredom and anger becomes hopelessness. It can thus be suggested that undesirable effects of negative-activating emotions are mediated by negative-deactivating emotions, where the continuous failure can be thought of as a moderator.

The yields of negative emotions in this investigation were high. The overall negative-to-positive emotion ratio was found to be 4.85:1, far higher than that of previously reported ratios. 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. Considering a possibly large number of learners in a regular connectivist learning environment (e.g., cMOOC), the teacher still has an option to inform the participants of the high level of negative emotions they may feel.

Thank you for reading 🙂

Find the presentation in full below:

References

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). Jumping from one resource to another: how do students navigate learning networks? International Journal of Educational Technology in Higher Education, 15(45). https://doi.org/10.1186/s41239-018-0126-x

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. (2017). Does artificial neural network support connectivism’s assumptions? International Journal of Instructional Technology 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 Instructional Technology and Distance Learning, 12(10), 3–21. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3063495

Pekrun, R., Frenzel, A. C., Goetz, T., & Perry, R. P. (2007). The control-value theory of achievement emotions. In R. Pekrun (Ed.), Emotion in Education (pp. 13–36). https://doi.org/10.1016/B978-012372545-5/50003-4

Pekrun, R., & Perry, R. P. (2014). The control-value theory of achievement emotions. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 120–141). New York, NY: Routledge.

Keywords:

• emotions

• connectivism

• control-value theory

• e-learning

• online learning

• higher education

• learning networks

• networked learning

• negative emotions

• achievement emotions