LET Conference 2018

LET CONFERENCE 2018

The 2018 conference on two core themes; Regulated Learning and  Collaborative learning, Socially Shared Regulation of Learning was very interesting as it highlighted the current trends or direction in collaborative learning  and regulation of learning as well as new possibility of studying these learning phenomena. Discussing the aim, background, foundation, results and application of the research done by expert researchers and PhD student in the LET unit. In this blog post, I write about the aim, background, foundation of research, methods, results, areas of application and a brief reflection of what I learned with recommendations. Enjoy .

KEYNOTE

 Multimodal Data to Understand Students’ Cognition, Metacognition, Motivation and Emotions in a Learning Process - Prof. Sanna Järvelä

Prof Sanna’s keynote lecture summaries the current areas of research for the Learning, Education, and Technology (LET) unit. The research of the LET unit focuses on learning sciences seeking to understand the complex process of learning through the Self-regulated Learning  (SRL) Theory. The research aims of the unit includes investigating regulatory process in collaborative learning, exploring multimodal data to understand critical SRL processes, and developing scaffolds and support of Socially Shared-Regulation of Learning (SSRL) in Computer Supported Collaborative Learning (CSCL). This research is based on the firm research on SSRL and SRL that have produced sound understanding of targets of regulation (motivation, cognition, emotion, behaviour), process of regulation (planning, goal setting, strategy adaptation, monitoring, evaluation and reflection), and the types of regulation that occurs in collaborative learning settings (self regulation, co-regulation, socially shared regulation). Also, from their research, the complexity involved in understanding learning process is known (Järvelä, Järvenoja, Malmberg, Isohätälä, & Sobocinski, 2016). Research into SRL has revealed that regulation of learning requires the agency of learners to actively monitor, evaluate, and adapt strategies to complete the task at hand (Winne & Hadwin, 1998). Regulating collaborative learning episode involves regulating oneself, regulating each other, and joint regulation as a group (Hadwin, Järvelä,  & Miller, 2017). Though there has been a lot of research into SRL and SSRL, more research is required to understand the metacognitive, cyclical adaptation of temporal progress of collaboration hence the need for multimodal data to understand such learning contexts.

The participants of the studies were high school students. The first study worked collaboratively to design a healthy breakfast. The lesson lasted for 75 mins. 36 students aged 15 participated and were put in groups of 3 students. The second study involved forty-three 16 year old students who participated in advanced physics learning tasks. 43 students participated in the collaborative lessons. Data were collected with 360 degree camera, audio, EdX logdata, questionnaires, evaluation forms, student products, eye tracking devices and Empatica E3 multisensor devices. With multimodal modal data, subjective and objective interpretation can be made, data from a channel complemented other channels providing data triangulation and evidence for justifying temporal, cyclical process, critical processes of (S)SRL and CoRL. Multimodal data is big and complex. The first step of making sense of this data is to visualize the data which calls for collaboration with experts from the computing sciences.

 A multidisciplinary approach was adopted to analyse the data. The video data were coded. Then it was synchronised with the EDA data collected. Also, facial data was analysed for micro and macro expressions and gestures among the groups. A critical part of the work matching the data from multiple channels which was done with time stamps (D'Mello, Dieterle, Duckworth, 2017). Each channel of data collection captured the dynamic context and the time-sensitive aspect of regulation. Triangulating the data from the multiple channels validated the research (Zusho, 2017). It is worth mentioning that multimodal data provided opportunities to text and experiment with different data to make meaning and strong evidence for understanding learning processes (Roll & Winne, 2015). 

 The results of this research suggest that regulation is critical as collaborative learning progress (Isohätälä, Järvenoja, & Järvelä,  2017) possibly due to the difficulty of collaborative tasks (Jonassen,1999) . Patterns and temporal progress in strategies used by students in different tasks were revealed, temporal and sequential patterns of regulation (SRL, SSRL, and CoRL) were observed during the collaborative learning tasks (Malmberg, Järvelä, & Järvenoja, 2017). Finally, there were agreements between self-reported data and the calculated physiological synchrony (from EDA data) (Dindar, Malmberg, Järvelä, Haataja & Kirschner, 2017). The success of this research is not without challenges like over/misinterpretation physiological data, laborious effort to clean multimodal data for triangulation, and minimising of this data. In future, the LET unit will explore the role of Artificial intelligence and machine learning.

Reflection

Research into learning sciences to understand learning process looks promising with the use of multi-disciplinary methods and multiple channels of data to grasp temporal and sequential progress and strategies. Although it is challenging to interpret the data, more applied research and repeating this research in other context may provide stronger evidence to support theories of SRL. 

  

References 

Dindar, M., Malmberg, J., Jarvela, S., Kirschner, P. (2017, April). Currents trends in LET research. Retrieved from LET Master's Degree Programme: https://letmaster.files.wordpress.com/2018/03/muhterem_dindar_earli_presentation.pdf

Hadwin, A. F., Järvelä, S., & Miller, M. (2017). Self-regulation, co-regulation and shared regulation in collaborative learning environments. In D. Schunk, & J. Greene, (Eds.). Handbook of Self-Regulation of Learning and Performance (2nd Ed.). New York, NY: Routledge.

Isohätälä, J., Järvenoja, H., & Järvelä, S. (2017). Socially shared regulation of learning and participation in social interaction in collaborative learning. International Journal of Educational Research, 81, 11-24.

Jonassen, D. H. (1999). Designing constructivist learning environments. Instructional design theories and models: A new paradigm of instructional theory, 2, 215-239.

Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J. & Sobocinski, M. (2016). How do types of interaction and phases of self-regulated learning set a stage for collaborative engagement? Learning and Instruction 43, 39-51. doi:10.1016/j.learninstruc.2016.01.005

Malmberg, J., Järvelä, S. & Järvenoja, H. (2017, in press). Capturing temporal and sequential patterns of self-, co- and socially shared regulation in the context of collaborative learning. Contemporary Journal of Educational. Psychology

Roll, I., & Winne, P. H. (2015). Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2(1), 7-12.

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. Metacognition in educational theory and practice, 93, 27-30.

 Zusho, A. (2017). Toward an integrated model of student learning in the college classroom. Educational Psychology Review, 29, 301–324. https://doi.org/10.1007/s10648-017-9408-4

 D'Mello, S., Dieterle, E., & Duckworth, A. (2017). Advanced, analytic, automated (AAA) measurement of engagement during learning. Educational psychologist, 52(2), 104-123.

Paper Session 1 (Regulated Learning) 

Jonna Mamberg’s research on “Are we together or not? Sequential interplay of monitoring and physiological synchrony during a collaborative exam” focussed on possibility of a relationship between physiological synchrony and monitoring during collaborative learning episode. Physiological synchrony is the tendency for humans to fall into rhythm with each other at the same time. Typical examples include an emotional feeling of happiness or anger with someone close to you at the same time. Physiological synchrony is measured through heart rate, skin conductance and other biomarkers of arousal. It measures physiological states which are uncontrollable. It has been used to measure intergroup similarity. In regulated learning, learners continuously monitor their progress and strategies in order to complete learning task. She used electrodermal activity (EDA) to measure physiological states as students worked collaboratively. Research shows that when individual work together in a group on the same activity, they are attuned mentally (Popov, van Leeuwen & Buis, 2017). Also when people work on activities that fosters higher levels of social interaction, a typical feature of collaborative learning, they produce similar patterns of physiological signals (Ahonen, Cowley, Torniainen, Ukkonen, & Vihavainen, 2016). On the basics of researches on physiological synchrony and physiological signals, shared monitoring of collaborative learning tasks was examined to identify potential relationship monitoring and physiological synchrony.

Using EDA data and video observations, incidents of monitoring and its equivalent EDA measures were extracted. Her finding suggest that there is relationship between monitoring and EDA peaks. Though physiological states were measured and correlated with EDA peaks, the relationship between physiological synchrony among collaborators as they monitor their progress is not clear.

From her research, it was obvious that EDA measured the cognition of learner when they focus their attention on the collaborative task. This possibly explains the reason why there is no clear relationship between physiological synchrony and monitoring. When attention is focussed on a task, it does not imply monitoring during a collaborative task. It could be something else such as task understanding. There is a possibility of physiological synchrony when share the same feeling.

The presentation by Muhterem Dindar on “Interplay of temporal changes in self-regulation, academic success and physiological synchrony” focuses on measuring SRL with physiological synchrony. Many research in the field of (S)SRL use surveys (self-reports) to collect data on how learners regulate themselves. Some researchers are of the opinion that surveys often reflect what learners perceive not the actual happening. As result digital learning traces, thinking aloud procedures, physiological signals among others, which records the process(activities or happenings during learning) are a reliable methods of collecting data from learners (Hadwin, Nesbit, Jamieson-Noel, Code, & Winne, 2007; Bannert & Mengelkamp, 2008; Azevedo, Taub, Mudrick, Farnsworth, & Martin, 2016). Together, they provide a holistic overview of SRL during learning. In this research, the relationship between academic scores of  learners and the changes is variables of SRL (cognition, behaviour, etc) were explored.

31 high school students participated in this experiment. 3 sets of data was collected on academic achievement (written exams, group task and final score), the EDA of some students while they worked collaboratively and self-report which measures their self-regulation. Session Synchrony Index (SSI) was calculated based on changes in self-reported SRL components. Also, a correlation analysis of SRL components and Academic achievements, and SSIs were conducted.

The results significant but weak relationship between academic achievement scores (specifically written exams and final score) and SSI. The results also shows the strong relationship and significant between SSI and change in Cognition. There were also fairly good relationship between SSI and Emotion and Motivation change. The research results suggest that some variables of SRL (measured with self report before and after collaborative task) directly correlated with academic scores of students.

From the table above, the relationship between Behavioural change and Cognitive change is significant but weak. Only the relationship between Motivation and Emotional change is fairly strong indicating similarity in the changes measured. Another major finding was the relationship between the cognitive change of participants and their physiological synchrony. Synchrony recorded can be attribute to many cognitive attributes including attention.

The outcome of this research shows the relationship between some variables. A visualization dashboard can be developed to give feedback to learners as they work collaboratively. This way they can reconcile their perceived performance and physiological traces and subsequently make the necessary adaptation.

The presentation on “Measuring motivation and emotion regulation on-line”  by Hanna Järvenoja. This research is based on 4 claims; motivation is in most learning situations or context (Jaakkola, Liukkonen, Laakso, & Ommundsen, 2008), motivation occurs at individual and social levels, the effects motivation and emotions are multilayered (Järvenoja, & Järvelä, 2009),  and they both are necessary for successful learning. Due to the complex nature of the interaction among the claims, there is to gather data from multiple channels, and analyse in attempt to understand the impact motivation and emotions have in learning situations. This research aims to analyse emotions and motivation during the process of collaborative learning taking into account situational variations, social nature, progress of learning in real time using multiple methods thus using questionnaires and physiological data

Video and audio data, EdX log data, questionnaires, evaluations forms, and learning products, mobile eye tracking and EDA values were collected during the learning process. Process-oriented approaches were used to analyse the data. An interview discussion of video processes explained SSRL processes during the task. Regulation episodes from the video data collected were coded. Shared and co-regulation of learning were identified as learners interacted with them. Also there were changes in the emotional valence of the participants during interaction.

Two key issues emerged. The data gathered is very rich but an in depth analysis is need to understand how regulation progresses with time. Another issues is how learners can get feedback from all the analysis of the data. In this research, dashboards were used for prompting learners to regulate themselves.

The idea of dashboard can be extended to visualize the physiological data together with the questionnaire in a meaningful that is interpretable and relevant to the learner.

Reflection

Regulation of learning occurs through 3 core process; planning, performing, and evaluating and reflecting. In each learning task, learners goes through this cyclical processes, hence a form of iteration that enables learners to improve their learning strategies to better regulate themselves. Also, these processes are not isolated from each other, rather a process such as planning can recur in performing while the learner monitor their progress and see the need to familiarize or update some plans or strategies. Monitoring involves learners thinking about strategy(style of work). Strategies may change. In collaborative learning which features complex tasks calls for learners to continuously monitor their understanding of task as well as their strategy. A way of identifying how learners are working collaboratively is physiological synchrony. Physiological synchrony is a measure of the physiological state of learners at a point in time. Traditionally, (S)SRL was measured as an aptitude. Now focus is on (S)SRL the process to understand and support learners. With current advances in technology and its usefulness to support learning, some aspects of motivation and emotions can be captured in real time, analysed to enhance learning. Technologies such as EDA, mobile tracking provides huge amount of data that can be harnessed to support learning. The challenge with such data is how to make meaning out of it. For learning situations making sense out of the data with the existing theory is crucial. I agree with the researchers that analysing in depth physiological synchrony and questionnaires provide more understanding of learning processes.  Solid understanding of EDA measurements in collaborative settings will enable the development of applications, prompts, scripts and other materials to predict and support challenging situations to enhance collaborative learning.

References

Ahonen, L., Cowley, B., Torniainen, J., Ukkonen, A., Vihavainen, A., & Puolamäki, K. (2016). Cognitive collaboration found in cardiac physiology: Study in classroom environment. PloS one, 11(7), e0159178.

Azevedo, R., Taub, M., Mudrick, N., Farnsworth, J., & Martin, S. A. (2016). Interdisciplinary research methods used to investigate emotions with advanced learning technologies. In Methodological advances in research on emotion and education (pp. 231-243). Springer International Publishing.

Bannert, M., & Mengelkamp, C. (2008). Assessment of metacognitive skills by means of instruction to think aloud and reflect when prompted. Does the verbalization method affect learning? Metacognition and Learning, 3(1), 39–58.

Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2(2- 3), 107–124.

Jaakkola, T., Liukkonen, J., Laakso, T., & Ommundsen, Y. (2008). The relationship between situational and contextual self-determined motivation and physical activity intensity as measured by heart rates during ninth grade students' physical education classes. European Physical Education Review, 14(1), 13-31.

Järvenoja, H., & Järvelä, S. (2009). Emotion control in collaborative learning situations: Do students regulate emotions evoked by social challenges. British Journal of Educational Psychology, 79(3), 463-481.

Popov, V., Leeuwen, A., & Buis, S. C. A. (2017). Are you with me or not? Temporal synchronicity and transactivity during CSCL. Journal of Computer Assisted Learning, 33(5), 424-442.

Paper Session 2 (Collaborative learning, socially shared regulation of learning)

Monitoring in collaborative learning and physiological synchrony

The aim of this research is to make visible situational characteristics that determine the success of individual, collaborative, and collective learning. This research combines self-regulation and collaborative learning. Monitoring occurs during regulation and enables students to make strategy decisions (Hadwin, Järvelä, & Miller, 2011). It can be said that learners regulate their individual learning and when they work in groups. The frequency and the periods of monitoring is often temporary hence the need to explore other ways other questionnaires, interviews, think alouds, and computer log file. This research explores monitoring and physiological synchrony to identify any connections.

During the collaborative tasks, 360 degree cameras recorded the learners. Monitoring ws coded. Also, the physiological responses of learners were recorded for physiological synchrony calculation. Cross-correlation time series analysis was performed using the coded monitoring episodes and physiological synchrony of participants.

This research revealed a weak relationship between physiological synchrony and monitoring. However, there was physiological synchrony during the entire collaborative learning It also suggest physiological synchrony has a huge potential and hence more research is needed

During collaborative learning, learners are often put into groups. These teams undergo the process of team formation (forming, norming, storming, and performing). These stages of team formation are crucial for the success of the task especially if it spans over a period of time. Physiological synchrony can be used to put people into groups. This may impact the learning process and reveal certain patterns of interaction.

Exploring small-scale adaptation in adaptation in socially shared regulation of learning

The aim of this research was to investigate metacognitive monitoring and its impact on strategic adaptations during collaborative learning. This research is founded on metacognitive monitoring of the (S)SSR  (Hadwin, Järvelä, & Miller, 2011), adaptation,  and Physiological Synchrony, a measure of group cohesion (Hove & Risen, 2009). In collaborative learning, not only do learners monitor their work but interact as they work. Monitoring involves learners thinking about strategy(style of work) and change their strategy (Järvelä, Järvenoja, Malmberg, & Hadwin, 2013).

This study involved 12 high school who participated in a collaborative physics task which lasted for 75 minutes. Their collaborative work was video recorded and the monitoring aspects of their group work were coded and analysed. The reaction of the learners were also recorded after each monitoring session. Aspects of monitoring such as behaviour and cognition were coded at different phases of work. It was key to know the reaction of the collaborators after each monitoring event. Also Physiological synchrony were calculated using physiological responses recorded with Empatica S4. Lag sequence analysis was performed focusing on events after reaction (preceded by monitoring).

The results of this research suggests that learners monitor their behaviour and cognition. Also after monitoring episodes, there were instances of reaction and no reaction. When reaction were traced further, learners either monitored their progress or changed their strategies which often resulted in synchrony. Hence, physiological synchrony occurred while learners monitored their collaborative task. Also the research revealed that learners adapted often when they defined the task, goals and planned their work.

From this research, we know that learner(s) can monitor their learning and change strategies if necessary. This is already being used by expert learner but novice learners need guidance to aware of (S)SRL process and phases in learner and control their learning. Physiological synchrony can be method of allocating groups to teams. In this case people with similar synchrony may be work effectively when put in team.

Investigating collaborative learning success with physiological coupling indices-based on electrodermal activity

The aim of this research is to conduct an inquiry into collaborative learning using physiological coupling. This research was based on two key words; Collaborative learning and Physiological afforded by advances in technology to understand humans responses to stimulus. Physiological responses between two or more interacting individuals as couple is an established phenomena.  (Levenson, & Gottman,1983). Collaborative learning is known to be challenging and demands cognitive, socio-emotional resources and requires interaction among participants. On the bases of these two theoretical foundations, collaborative learning was investigated.

An experiment was conducted with 48 high-school participants. These students were given a collaborative task. Data was collected through questionnaire, test(pre and post), group report and Empatica S3. Collaborative learning was evaluated as will, learning product, and learning gain. Collaborative will was calculated using scores obtained by administering MSLQ. Collaborative product obtained by scoring the group report. The dual learning gain was calculated using the pre and post test administered to test the prior and after knowledge of participants. These 3 variables measure collaborative learning. With the physiological measures obtained by EDA, the difference, rate of change, direction, relationship and weight of the relationship in EDA(Physiological coupling indices) values of participants were calculated. Quantitative analysis such as means and standard deviations were calculated. Finally, a regression analysis was performed to identify the predictive power the PCIs in collaborative learning (Pijeira-Díaz et al., 2016).

The results of the research indicated a positive gain in learning for the collaborators as  Directional Agreement (DA) had the highest correlation after the regression analysis. DA is known to to be the most sensitive measure of PCI (Elkins, Muth, Hoover, Walker,  Carpenter, & Switzer, 2009)

At the individual level, the EDA measures of a student can response to a learning difficult and can indicate when learners are stuck which will in turn help teachers to support their students.When the physiological responses of learners are known, the right interventions can be provided to support their learning.

Reflection

Motivation often manifests itself through finding reason to learn or do something. This reasons may be arise from the inside of an individual or from the environment. Emotions are expressed by people in different ways. It can be detected from the demeanor of the person. In learning, emotions and motivations have the tendency to have an impact on the learning process. In learning situations being it collaborative or individual, motivation and emotional issues emerge and change overtime during task enactment.

Personally, my motivation to learn varies but interest is critical for me to engage in a task. One thing that I often ignored was the emotions that emerge at the onset of task. My perceived understanding of a task and its difficulty often makes me anxious or excited. The knowledge I have acquired in SRL has made me aware and conscious of my emotions especially when learning.

The new trends of detecting and understanding (S)SRL (using physiological signals, log data, etc) is very important. However, more research is needed to conduct in-depth analysis of the physiological data establish the connection between traditional SRL measures (surveys) and physiological data. Probably, it will be worth collecting visual data of the learning products as learners perform the task may provide an in depth understanding of regulatory process in collaborative learning.

References

Elkins, A. N., Muth, E. R., Hoover, A. W., Walker, A. D., Carpenter, T. L., & Switzer, F. S. (2009). Physiological compliance and team performance. Applied ergonomics, 40(6), 997-1003.

Hadwin, A. F., Järvelä, S.,  Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. Handbook of self-regulation of learning and performance, 30, 65-84

Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2(2- 3), 107–124.

Hove, M. J., & Risen, J. L. (2009). It's all in the timing: Interpersonal synchrony increases affiliation. Social Cognition, 27(6), 949-960.

Hove, M. J., & Risen, J. L. (2009). It's all in the timing: Interpersonal synchrony increases affiliation. Social Cognition, 27(6), 949-960.

Järvelä, S., Järvenoja, H., Malmberg, J., & Hadwin, A. F. (2013). Exploring socially shared regulation in the context of collaboration. Journal of Cognitive Education and Psychology, 12(3), 267.

Levenson, R. W., & Gottman, J. M. (1983). Marital interaction: physiological linkage and affective exchange. Journal of personality and social psychology, 45(3), 587.

Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016, April). Investigating collaborative learning success with physiological coupling indices based on electrodermal activity. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 64-73). ACM.

Paper Session 3

This session comprised of presentations from Pirkko Siklander, and Tiina Törmänen.

Playfulness as teachers and educators competence

The aim of this research is to inquire about the playfulness of educators and students focussing on how educators assess their playfulness, the general characteristics of playfulness among educators, and to identify if they are any differences in playfulness among educators when age and genders are taken into consideration. This research based on the concept of playfulness as a personality trait which originates from childhood play (Banaji & Burn, 2010). How then do playful teachers incorporate playfulness in their teaching? Hyvönen in 2011 describes that playful teaching includes role taking, designing playful learning lessons or curriculum, seeks to develop and improve creativity and enables students to have fun while studying. The need for this research is necessitated by the stress of playfulness in the new Finnish curricula,  how to integrate play in teaching and learning (Hyvönen 2008; 2011), and how to reverse the decreasing trends of play among children (Bergen, 2002).

There were 123 participants who took part survey mainly teachers, educators, and retired teachers and educators. The Finnish version of the Adult Playfulness Scale (APS) was used to determine the playfulness of participants. A reliability analysis was conducted to evaluate the internal consistency of the APS scale as a good consistent measure of adult playfulness. The results of this analysis indicated that the data collected as reliabilities were greater than 0.75. A Principal Component Analysis (PCA)  conducted to identify the underlying factors of playfulness in the data collected showed similar factors as the APS scale.

The results of this research indicated that when participants evaluated their playfulness as high, they enjoyed being with other people, were curious to learn and know more. Also they were very flexible. Though six statement were likely to depict adult playfulness, most participants selected  “I like to make people laugh” to indicate their playfulness. Inquisitiveness, curiosity, flexibility and creativity described playfulness in this research. Furthermore, a multivariate analysis revealed that there is a statistically significant difference in playfulness among genders. On the other hand, playfulness was the same for age.

Considering the urgent need to include playfulness in teaching and learning,  the key characteristics of playfulness identified thus inquisitiveness, curiosity, flexibility and creativity should be taken into account when designing lessons, textbooks, and other learning materials. When playfulness is embedded in materials, teachers may not struggle or be burdened to include playfulness as an “extra task”. Through playful teaching and learning, collaboration and self-regulation can also be developed among students (Bateson & Martin, 2013). Also, if play is incorporated in studies, it may enhance the interest of students and engage them to play more.

Exploring collaborative groups’ emotional states with video and physiological data

The aim of this research is to explore the relationship between individual affective states, how emotion regulation occurs within groups, and the role of emotion regulation in collaborative learning. The researchers investigated the interconnection between emotional states and emotion regulation in specific situations as well as the possibility of using physiological states to evaluate the emotional states. This is research is based on the work done by Boekaerts & Pekrun in 2016 about the multifaceted nature of emotions. According to Boekaerts and Pekrun, emotions consist of many physiological process such affective, cognitive, and motivation. Since this research was to take place in an academic setting, then its worth backing the research with academic emotions which is related to the academic setting (thus learning, instruction, etc). The emotional or affective states occurs in two dimensions; valence and activation. Valence indicates whether affective states is positive or negative while activation considers factors that cause emotion arousal (Pekrun, 2016). Skin Conductance Response (SCR) measures the rapid changes in physiological arousal and its a measure of EDA (Braithwaite et al. 2013).  EDA which has been used by (Pijeira-Díaz, Drachsler, Järvelä, & Kirschner, 2016) to show that there is relationship between cognitive and emotional processes. In this research, the researcher seeks to establish a relationship between emotional states while students interact, communicate, negotiate and work collaboratively. As evident in Hanna Järvelä and Jonna Malmberg work on socially shared regulation, a phenomena that occurs in collaborative learning, learners, go through challenges such emotional and motivational ones. Positive and negative emotions

Forty-one 6th graders took part in this research. They were given a collaborative task which included both individual work and collaborative working. The participants went through four phases of work (individual, brainstorming, planning, building). Video and physiological data were recorded while learners worked collaboratively. Segments of socio-emotional segments were coded from the video data. Also, the physiological data were observed for valence and physiological arousal of the individual participant and their team.

This research revealed that the emotional state of the group varied often per session. The groups exhibited negative deactivating emotional states. Also the social emotional states shown by the different groups varied from one another. The researcher seeks to analyse the potential relationship between the EDA data collected and the video data in the near future.

Research into physiological physiological arousal has a potential to unearth or explain challenges that learners go through while learning collaboratively. When this knowledge is well established interventions and scaffolds can be designed to support learners to gain more from collaborative learning and prepare them to work collaboratively in whichever area they find themselves after their studies.

Reflection

I believe it's a good idea to incorporate playfulness in learning. Play affects the emotional states or the emotional states of a person affects the way a person play. Either way emotional valence and activation are produced. To maximise the benefit of playful learning, learners need to know how to regulate their emotions while playing as individual, in  a group, or in teams. I can see the potential link between of the research into physiological arousal and emotion, and playful learning. Supporting learners in playful learning environments in particular. Perhaps it is worth researching physiological synchrony in playful learning. 

References

Banaji, S., & Burn, A. (2007). Creativity through a rhetorical lens: Implications for schooling, literacy and media education. Literacy, 41(2), 62-70.

Boekaerts, M., & Pekrun, R. (2016). Emotions and Emotion Regulation in Academic Settings. In L. Corno & E. M. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 76–90). New York, NY: Routledge. https://doi.org/10.1017/S0954579400006301

Braithwaite, J. J., Watson, D. G., Jones, R., & Rowe, M. (2013). A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. Psychophysiology, 49(1), 1017-1034.

Hyvönen, P. (2008). Affordances of playful learning environment for tutoring playing and learning. University of Lapland.

Hyvonen, P. T. (2011). Play in the school context?: The perspectives of Finnish teachers. Australian Journal of Teacher Education (Online), 36(8), 49.

Pekrun, R. (2016). Academic Emotions. In K. R. Wentzel & D. B. Miele (Eds.), Handbook of motivation at school (2nd ed., pp. 120–144). New York, NY: Routledge.

Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016). Investigating collaborative learning success with physiological coupling indices based on electrodermal activity. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16, 64–73. https://doi.org/10.1145/2883851.2883897

Paper Session 4

In this session are presentations from Pirkko Siklander, Arttu Mykkänen, and Kristiina Kurki

Hiking in the Nature to Promote Learner’s Agency and Competences - Pirkko Siklander

The aim of this research was to investigate how nature can enable students to exercise their agency and improve their competences in an outdoor learning environment. The research took place during a hiking course and the researchers sought to identify the ways in which the agency of students emerged and what kind of competences needed for hiking emerged. This research is based on research done in the fields of outdoor learning and agency. Agency and other competencies are examples of competences embedded implicitly or explicitly in the new curricula and outdoor learning environment is an environment that affords adventure. Adventure in learning equips students with challenging, unpredictable contexts and enable them to take responsibilities for themselves and their learning (curiosity) (Brown, 2008). Also learning in an outdoor environment occurs in the outdoor activities itself (hiking), through the activity and around the activity (Beames, Atencio, & Ross, 2009).

The participants of this research were 21 eighth graders and 2 subject teachers. The participant hiked for 25 kilometers in total. The data used of this research comprised of student digital diaries, and participant observation (audio-recorded field notes and photos, interviews with students and teachers). The data collected were analysed qualitatively using inductive content analysis.

The result of this research suggests that student agency emerged as responsibility, resilience, collaboration and feeling of success. The students co-operated and helped one another, they were responsible for their own needs, persistent and proud of their achievement.  This research also revealed that the students used social and negotiation skills during the hike, they solved problems and showed responsibility for themselves and their colleagues. This research has provided evidence to show that outdoor learning environment is an affordance for developing and exercising agency. The researchers concluded that teacher’s pedagogical thinking can give room for student’s agency and autonomy.

This research can be a stepping stone to developing more outdoor courses focusing on other competencies or specific activities. Guides and learning materials can be designed for teachers who want to use this methodology in their teaching.

Exploring regulatory interactions among young children and their teachers in day-care context focus on teachers’ monitoring - Kristiina Kurki 

The aim of this research is to investigate the kinds of strategies used by children to regulate their emotion and behaviour, and how the monitoring level of teachers impact the strategy adopted by student to regulate their emotions and behaviour. This research is underpinned by the work done in the field of emotion and behaviour regulation. Its an established fact that emotions emerge during academic work which may positively or negatively affect the success of a learner hence the need to regulate the emotions. Regulation of emotion in no particular order includes identifying the emotion, monitoring, evaluating and modifying (Phillips, & Power, 2007). The skill of regulation is learned and developed from the interaction a child has with the family and school. In the 21st century with career parents, many kids spend more time in school hence the teacher then plays an important role in shaping the regulatory skills of a child. The need to equip children with good regulatory skills and strategies for regulation are important. Childhood regulatory activities occur through co-regulation where the “regulatee” gradually internalizes regulatory skills from the regulator towards self-regulator. The concept of co regulation  reemphasizes the role of teachers, parents, and friends and people in the environment where the child grows.

30 children with the ages of 2 and 5 participated in this research. The research was conducted in a day facility designed for research purposes hence data was collected from cameras and microphones. An important point to note about this research is its authentic nature. The data used were extracted from videos taken from the natural environment the kids study in without providing any additional conditions that occurs in other researches. The video analysis was performed with 3 core variables from existing literature in mind. Namely Children’s emotion and behaviour regulation strategies, Teacher monitoring and Adaptation of emotion and behaviour regulation strategies. During the video analysis, emotionally challenging episodes were located and selected. The strategies of emotion regulation used by the children and how they adapted,  events where teachers monitored emotion regulation were all coded. The researcher then explored the connection between teacher’s support and monitoring activities and the the use of strategies by the children using the Chi-square test. This test show the dependency or association between  variables (in this case teacher’s support and children’s strategies, active and weak monitoring, and children’s strategies and their adaptation strategies) but I am not sure how exactly chi test can be performed on qualitative data.

This research revealed response situation selection,  situation modification and response modulation were the independent strategies used by the children. Also, the association between the independent strategies used by the children and the teachers supported strategies were found to be significant. Redirecting activity was the most used teacher supported strategy. No statistically significant difference in the strategies used in teacher’s monitoring and non-monitoring episodes. The research shows that children adapted their strategies often when teachers were actively monitoring. Concluding the research, the researchers stated that teachers support made a difference in the use and adaptation of strategies by children.

This research clearly shows that monitoring is important for shaping the regulatory skills of learners. In discussion that occured after the presentation, most of the people agreed that the student-teacher ratio need to assessed to teachers the opportunity to attend to a good number of students as it may have impact on the regulatory skills of learners.

Students’ interpretations of a group awareness tool in a collaborative learning setting- Arttu Mykkänen

During collaborative learning challenges such dysfunctional communication, insufficient regulation and unequal participation. Collaboration occurs in teams or group of people. For a group of people to participate and  work well together, there is a need for group awareness(Chávez, & Romero, 2014). This research is based on two concepts; collaborative learning and group awareness. The aim of this research is identify and examine the advantages and disadvantages of group awareness tools in collaborative learning.

44 teacher education students participated in this research. The participants were divided into 11 groups of 3 to 5 members. The collaborative task assigned to the participants were in 2 phases. In the phase they attended teacher led sessions and were given a math problem to solve collaboratively. In the second phase they created a mid-term plan for primary schools on  a math topic. A group awareness tool was used to collect the opinions of learners as they worked collaboratively. Students were also interviewed. The S-Reg awareness tool used assess the group awareness comprised of 3 phases; namely awareness phase, reflection phase and regulation phase. In the awareness phase each team member chose 3 values thus cognitive, motivational and emotional. Their responses were synthesized and visualised to the participant and this led to reflection phase where the entire group discussed the reason for the result and agreed on one reason and which later was regulated by the team. After using the group awareness tool, the participants were interviewed to evaluate the usefulness of the group awareness to for their collaborative task. The interview was analysed.

The results of this research shows that the group awareness tool had both positive and negative impacts on the collaborative learning. Positive impact of the tool included aiding the team to understand the state of mind of group members and themselves, helps in task understanding, prompted further discussions. On the other hand, some groups claimed they did not benefit from using the group awareness tool. Some reasons given are the tendency of the tool to increase frustration, the awareness questions and prompts were filled in hastily hence no benefit from using it. The presence of positive and negative sentiments of the group awareness tool calls for the need to improve the tool because its usefulness is evident. To improve the tool, the participants revealed aspects such as timing of the prompts and technological features. The concept of content space (focusing on problem-solving) and relational space (focus on establishing good climate for effective collaborative task) emerged.

Obviously, collaborative learning requires attention for the task (complex of task is important) and group communication. It will be great if the idea of content and relational spaces are balanced or well represented in designing a group awareness tool in future. The researchers also emphasized the need to embed group awareness tool in the collaborative task in such a way that it usable for the learners.

Reflection

It is exciting to know the affordances of outdoor learning. A learning environment that allows interaction with nature and construction of meaning. Regulation of learning and Collaboration of learning requires the agency of individuals to take responsibility of their learning. This implies their actions and interactions have impact on their achievement. Outdoor learning can provide the practice and experience that cannot happen within the space of classroom. There are of course challenges with planning such lessons, the outcomes maybe worth putting in much effort.

During the course of the outdoor learning, emotional challenges are phenomena will naturally emerge as students had to set their tent, carry their backpacks. The responsibility of regulating behavior and emotions lies on the teachers and student themselves. Though they are not very young, their ability to regulate themselves count and when necessary, teacher's support. Also, for smooth learning to take place, the teachers are responsible for creating a conducive atmosphere for all the learners which could possible enhance regulation of oneself.

Before embarking on such a trip, the interest, motivation, learning outcomes are vital for the learners to be aware of the possibilities. In some situations, there is a need for the entire group of learners to be aware of themselves. If learners do not know each, other, then activities such as ice breaks are good starting point to know one another. Using well designed group awareness tool is a step in the right direction to prepare teams ahead of collaborative tasks.

References

Beames, S., Atencio, M., & Ross, H. (2009). Taking excellence outdoors. Scottish Educational Review, 41(2), 32-45.

Brown, M. (2008). Outdoor education: Opportunities for a place-based approach. New Zealand Journal of Outdoor Education, 2(3), 7-25.

Chávez, J., & Romero, M. (2014, September). The Relationship between Group Awareness and Participation in a Computer-Supported Collaborative Environment. In International Workshop on Learning Technology for Education in Cloud (pp. 82-94). Springer, Cham.). The Relationship between Group Awareness and Participation in a Computer-Supported Collaborative Environment. In International Workshop on Learning Technology for Education in Cloud (pp. 82-94). Springer, Cham.

Phillips, K. F. V., & Power, M. J. (2007). A new self‐report measure of emotion regulation in adolescents: The Regulation of Emotions Questionnaire. Clinical Psychology & Psychotherapy, 14(2), 145-156.