Current trends | ouluexperience

Thesis work

Current trends in Learning Sciences research

Background information

Learning Sciences are concerned with understanding how people learn in order to provide scientific bases for designing learning environments and experiences that enable deeper and more effective learning, thus making people and organisations better equipped to deal with the challenges presented by the 21st century (Sawyer, 2014). It is an interdisciplinary field that brings together researchers and professionals from educational psychology, anthropology, computer science, neuroscience, instructional design, information sciences, design studies and sociology among others.

Recently, research has moved on from focusing on individual learners to explore how individuals learn in collaboration, bringing intersubjectivity and other interpersonal elements into the scope of the investigation. Also, the research environment more and more often resembles authentic learning environments, or classrooms, with genuine, real-life learning tasks. The third aspect that needs to be mentioned is that the emphasis is shifting on how we (should) define knowledge - from knowledge being a collection of facts that one either knows and is able to recall or not, to being understood as the processes and outcomes of idea improvement (Bereiter & Scardamalia, 2014).

There are several obstacles in the way of examining learning processes and knowledge co-creation, as most of them are invisible, happening inside people's minds and so are not available for direct observation. Current research thus seeks solutions to overcome these obstacles by employing advanced technologies for data collection, such as eye movement tracking, facial recognition, biosignal processing (such as electroencephalography, heart rate monitoring or electrodermal activity) and gathering digital data.

The following section will introduce recent studies that were and are being conducted by the Learning and Educational Technology Research Unit (LET) at the University of Oulu, Finland.

Regulation of learning

27 March 2018

Malmberg, J. (2018, March)

Are we together or not? Sequential interplay of monitoring and physiological synchrony during a collaborative exam

Paper session presented at the LET2018 Conference, Oulu

Physiological synchrony refers to an event where physiological signs (resulting from autonomic nervous system activity such as heart rate, level of perspiration, etc) from two or more people appear to be changing simultaneously. Previously it has been found to indicate 'connectedness', and has been mostly researched in the field of psychology, for example in parent-infant (Feldman, Magori-Cohen, Galili, Singer, & Louzoun, 2011), parent-adolescent (Papp, Pendry, & Adam, 2009) or patient-therapist relationships (Marci, Ham, Moran & Orr, 2007). Recently, learning sciences (as well as economics, see Mønster, Håkonsson, Eskildsen, & Wallot, 2016) have started experimenting with involving this concept in research, as the scope of learning as we understand it now incorporates interpersonal elements to a great extent.

As the title suggests, this study was conducted during a collaborative exam situation, where students had to solve a physics problem in groups of 3. Monitoring (the meta level functioning of continuously following and evaluating progress) has been identified as a key process leading to success during both individual and collaborative learning episodes or events (Griffin, Wiley & Salas, 2013; Näykki, Järvenoja, Järvelä, & Kirschner, 2015).

Researchers wanted to find out a) if it is possible to determine if and when a monitoring activity occurs by looking at physiological data produced and b) whether individuals in a group exhibit non-verbal signs of attunement during phases of monitoring. The underlying assumption is that individual monitoring can lead to shared monitoring, which is an intense type of social interaction, therefore, it should manifest through physiological synchrony.

Data collection methods included video observation and gathering biosignals, namely electrodermal signals (EDA) by using Empatica wristbands. Data analysis was done in several phases as in identifying episodes of monitoring from the video data for each individual, checking for peaks in EDA and calculating the single session index (SSI) of synchronicity then triangulating these three.

Results from this study suggest, that the number of an individual's EDA peaks correlate with the time spent on monitoring activities, indicating that an individual was or wasn't active mentally, but the process analysis hasn't revealed any clear relation between monitoring and physiological synchrony.

Dindar, M. (2018, March)

Interplay of temporal changes in self-regulation, academic success and physiological synchrony

Paper session presented at the LET2018 Conference, Oulu

I found this presentation harder to comprehend. There are a lot of things being included in this research, both content as well as methodology wise. Researchers were interested to see if there is a relation between a) self-reported regulative activities and academic success, b) academic success and physiological synchrony and c) physiological synchrony and the self-reported regulative activities respectively. Examining all these and the relations between them makes data analysis very complex and interpreting the findings rather difficult.

Data were gathered through several collaborative learning sessions in advanced physics, using the Empatica wristbands and through pre- and post-session surveys where students reflected on motivational, emotional, cognitive and behavioral aspects. Academic achievement score was recorded at the end. The EDA data were combined to form the SSI. There seemed to be a focus on gathering as much and as diverse data as possible, meaning there was no real focus. This leads to issues concerning instruments designed for data gathering. In this case, the simple and very limited amount of questions used in the surveys makes it rather difficult to form connections with the theoretical concepts of SRL, which are highly complex and multifaceted. It is unclear how a regulatory process can be presented through such umbrella terms as 'motivational change' or 'cognitive change', using questions that indicate an individual's state pre and post, but does not include reference to actual regulation. I am uncertain how to interpret this wording, and consequently, the results of the correlational analysis including this aspect, that is explored in questions a) and c). I haven't been able to find a rationale for examining the relationship between an individual's different states of motivation, etc and a combined group session index for physiological synchrony. Regarding the third question, the study found no indication that academic success and physiological synchrony are related.

There were several methodological issues discussed after the presentation, including the view that task design strongly influences what variables will be predominantly available for observation. Meaning that a task that for the most part requires cognitive involvement will be less likely to evoke socio-emotional reactions. Also, there are several indicators that can be used to measure physiological synchrony or compliance (PC) (Montague, Xu & Chiou, 2014), such as directional agreement (DA), instantaneous derivative matching (IDM), signal matching (SM) (Elkins, et al., 2009), cross correlation (CC), or weighted coherence (WC) (Henning, Boucsein, & Gil, 2001) and it is yet to be seen which one would be a good fit for learning research.

Järvenoja, H. (2018, March)

Measuring motivation and emotion regulation on-line

Paper session presented at the LET2018 Conference, Oulu

This presentation was not introducing an empirical study as such, but rather provided overall background information regarding the current trend in researching motivation and emotion as a process ('on-line' referring to real-time, on the fly data collection, not online or web-based methods). Again, it is important to remember that the aim is to make invisible mental processes observable.

Motivation is crucial for successful learning (Wolters, 2003; Schunk & Zimmerman, 2012), and looking at it as something that can be regulated, directed or changed is in contrast to the view that motivation is a static construct which one might or might not have. The former view presents possibilities for both the individual and the social surrounding to have an effect on one's motivation both in a positive as well as in a negative way during one learning episode or throughout several episodes. But how can it be objectively measured? In a collaborative environment, there are of course ways of expression or externalizing thoughts and feelings, but that is through interpretation that we make sense of it - we can analyze video data, stimulated recall interviews, surveys, log files, etc. On top of this can physiological data tell us anything? It certainly can reveal something about a person's affective state, which I believe is more related to emotions, than motivation. Even if it is not possible to identify what kind of emotion an individual has experienced, the effect of that experience carries importance for learning (Boekaerts & Pekrun, 2015). Is the specific emotion in that specific moment activating or deactivating for learning efforts?

There are several issues that researchers of motivation and emotion regulation need to find solutions for. Like, how to combine findings that focus on the individual with those that examine group processes, or how to blend objective and subjective data sources, or how to ensure generalizability.

Further thoughts

1. One thing I pondered a lot about during this session was related to the question of basic research. I am very pragmatic and usually look for value and usefulness for real-time application. However, this type of research using multimodal data is in its infancy. It was hard to contemplate that it might not lead to anything useful or tangible, or even if it does, implications from the findings will not have an effect on practice for a long time still. At this stage, questions are still being asked regarding the methodology, like what the valid measures could be, how much information is needed, how reliable models look, etc. Is this actually 'a right' path to follow in terms of gaining a deeper understanding of learning? It could be, but there needs to be more research done. I will try to follow the development in the coming years.

2. Another question is related to the contextual and situated nature of learning. Researchers need to interpret and make sense of the collected data without having the knowledge necessary to frame the learning. Knowledge regarding individual students' personal traits or the social dynamics within the given class. Teachers know their students. How could a technological device/software that would be developed along the way help in the classroom? What is it that the teachers cannot know or feel? How will physiological data be collected? Will every student be required to wear an electronic device for real-time data gathering? Yes, I know, we do willingly provide a lot of information about ourselves already, but will students have the chance to opt out, even if they know it would be for their own benefit? Could it be useful for online based learning? With these questions we get to the next issue.

3. Data protection and security. The European Union is about to introduce the General Data Protection Regulation, as well as recent international events concerning the handling of personal data make the following questions rather important: who is able to see the data, how is it stored, can it be connected to the real persona or is it anonymised, how long is it kept, etc... Answering these is not the most urgent task, as current research might not even lead to this point. On the other hand, even if it won't be biosignal, some other kind of data will be collected at some stage, otherwise what is the point of trying to make the invisible visible? Surely, once a certain type of data collection for research proves to be beneficial, the same techniques will be applied in real-life settings. At what level will it become unethical? Who personally will be able to decide that the gains outweigh the risks? Are we heading towards a learning utopia or a learning dystopia?

References

Boekaerts, M. & Pekrun, R. (2015). Emotions and emotion regulation in academic settings
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. doi:10.1016/j.apergo.2009.02.002
Feldman, R., Magori-Cohen, R., Galili, G., Singer, M., & Louzoun, Y. (2011). Mother and infant coordinate heart rhythms through episodes of interaction synchrony. Infant Behavior and Development, 34(4), 569-577. doi:10.1016/j.infbeh.2011.06.008
Griffin T.D., Wiley J., Salas C.R. (2013) Supporting Effective Self-Regulated Learning: The Critical Role of Monitoring. In: Azevedo R., Aleven V. (eds) International Handbook of Metacognition and Learning Technologies. Springer International Handbooks of Education, vol 28. Springer, New York, NY
Henning, R. A., Boucsein, W., & Gil, M. C. (2001). Social–physiological compliance as a determinant of team performance. International Journal of Psychophysiology, 40(3), 221-232. doi:10.1016/s0167-8760(00)00190-2
Marci, C. D., Ham, J., Moran, E., & Orr, S. P. (2007). Physiologic correlates of perceived therapist empathy and social-emotional process during psychotherapy. The Journal of Nervous and Mental Disease, 195(2), 103–111.
Mønster, D., Håkonsson, D. D., Eskildsen, J. K., & Wallot, S. (2016). Physiological evidence of interpersonal dynamics in a cooperative production task. Physiology & Behavior, 156, 24-34. doi:10.1016/j.physbeh.2016.01.004
Montague, E., Xu, J., & Chiou, E. (2014). Shared experiences of technology and trust: An experimental study of physiological compliance between active and passive users in technology-mediated collaborative encounters. IEEE Transactions on Human-Machine Systems, 44(5), 614–624.
Näykki, P., Järvenoja, H., Järvelä, S., & Kirschner, P. (2015). Monitoring makes a difference: Quality and temporal variation in teacher education students’ collaborative learning. Scandinavian Journal of Educational Research, 61(1), 31-46. doi:10.1080/00313831.2015.1066440
Papp, L. M., Pendry, P., & Adam, E. K. (2009). Mother-adolescent physiological synchrony in naturalistic settings: Within-family cortisol associations and moderators. Journal of Family Psychology, 23(6), 882-894. doi:10.1037/a0017147
Schunk, D. H., & Zimmerman, B. J. (2012). Motivation and self-regulated learning: Theory, research, and applications. New York: Routledge.
Wolters, C. A. (2003). Regulation of Motivation: Evaluating an Underemphasized Aspect of Self-Regulated Learning. Educational Psychologist, 38 (4), pp. 189-205

Paper session 2

29 March 2018

Sobocinski, M. (2018, March)

Exploring small-scale adaptation in socially shared regulation of learning

Poster session presented at the LET2018 Conference, Oulu

Adaptation means the process of making something suitable for a new use or purpose, modifying, or becoming adjusted to new conditions. This is in essence what regulated learning is about. Individuals' or groups' strategic adaptation of beliefs, thoughts or actions oriented towards a future goal in light of present and past experiences (Hadwin, Järvelä & Miller, 2011). Small-scale adaptation refers to the actions taken during the ongoing learning session, as opposed to between sessions or over a longer period of time (Järvelä, Hadwin, Malmberg, & Miller, 2018). Socially shared regulation of learning (SSRL) indicates that the study was conducted during collaborative learning events.

The aim of the study was to investigate the process of monitoring and its relation to 'on the fly' adaptation by using multimodal data, namely video data and in this instance heart rate measurements were used for examining physiological synchrony (PS). Data were collected during an advanced physics course (20 sessions, 75min each) with 43 participants (aged between 16-17). During data analysis adaptation was coded if a monitoring event was followed by reactions from the group, resulting in the collective monitoring of an earlier learning phase (Hadwin, Järvelä, & Miller 2017). An interesting finding of the study was the lack of reaction to monitoring events related to motivation or emotions while monitoring related to cognition or behavior were followed by some kind of reaction in 80.49% of the cases. We can hypothesize that this might be due to lack of time or interest in dealing with socio-emotional issues, or simply because they occur very rarely, since the learning tasks are predominantly cognitive, as discussed earlier.

To see how PS appears during these events, a sequential lag analysis was performed. It seems that PS, based on heart rate measurements, can be a sign of mental attunement as a reaction from another group member had to take place prior to the development of PS. PS did not appear if there was no reaction.

Haataja, E. (2018, March)

Monitoring in collaborative learning and physiological synchrony – How they co-occur?

Poster session presented at the LET2018 Conference, Oulu

The aim of this study was to examine the temporal relation between monitoring and PS, building on the assumption that a group's joint attention is related to both of these constructs. The study used the same data sources as the one mentioned above, 3 groups of high school students (with the most accurate data) were analyzed in detail, based on video data and EDA signals. The PS measure, in this case, was the physiological concordance index (Marci et al., 2007) that was later combined to form an SSI. Cross-correlational time series analysis was used to explore the temporal relation.

According to the results, the connection was highest if all monitoring activity was taken into account, regardless of the target of the monitoring. Furthermore, PS appears to be a possible measure to further investigate regulation in collaborative learning in more detail.

While it is impossible to control all the variables affecting an individual's motivation, emotion or cognition and thus her or his physiological signals, it would be beneficial to identify the ones that might have a stronger effect than others. Also, it can be stated that EDA data alone will not be sufficient and needs contextual information to help with the interpretation of the reasons behind the changes in physiological signals.

Pijeira Diaz, H. (2018, March)

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

Paper session presented at the LET2018 Conference, Oulu

The aim of this study was to compare different PS measures (here, physiological coupling indices), that were listed earlier, to determine which one relates most to different aspects of collaborative learning in order to inform the design of digital feedback tools that make use of learning analytics based on physiological data.

The experiment for data collection was the same as above. The methods, however, incorporated the use of pair learning related elements of the MSLQ questionnaire (Pintrich, 1990), individuals' pre- and post-tests and group reports in addition to EDA. The aspects of collaborative learning included in the analysis were collaborative will (CW), collaborative learning product (CLP) and dual learning gain (DLG), which is a mean of two pre- and post-test results. Table1 represents the results of the regression analyses.

Table1, adapted from Pijeira-Díaz, Drachsler, Järvelä, & Kirschner, 2016

Directional Agreement (DA) shows a strong correlation with learning gain, which is an individual measure, while Instantaneous Derivative Matching (IDM) can be the most informative regarding collaborative will and the outcome of the collaboration, having a moderate to strong correlation with those variables.

I believe it is quite valuable to compare the different indices with different constructs within the same study, as it will provide much needed information as to what measures are best suited to certain research questions, and so enabling other researchers to conduct meaningful and reliable analyses.

Further thoughts

1. We discussed the importance of comparing individual differences in EDA data, since the same stimuli can result in varying degrees of physiological response in different people. It is rather interesting how to make these physiological signals comparable through standardization/normalization.

2. At some point during the discussion, there was a mention of 'good' data, referring to the possibility, that technology cannot be relied upon 100 per cent of the time. There can be issues with connectivity, software or hardware malfunctioning, etc. Since these wearable devices have been around mostly for health monitoring, or more recently for fitness promotion purposes, I find it interesting that manufacturers are not yet more involved in research that would open up more markets for their products. Wearable technologies made a strong debut at CES (Consumer Electronics Show) in 2014. It would be rather interesting to know what value these manufacturers see in supporting learning research. We also discussed the (un)obstrusive nature of these technologies, which reminded me of other types of 'things' with inbuilt sensors, smart fabrics. The merger of textile and information industries have brought about innovations at a grand scale, that were made possible by the advances in nanotechnology, microelectronics, biotechnology or microeletromechanics (Tao, 2001). Many of the inventions came about because of the needs of the military, emergency services or law&order enforcement. The common point with current learning research, other than collecting objective data through sensors, is the requirement that reactions are initiated by situational and contextual needs. "Smart clothing can constantly track our heart rate, monitor our emotions and even pay for your morning coffee..." (Sawh, 2018). If we are heading towards a future where machine learning is embedded in learning and teaching, then experimenting with smart fibers or fabrics, either passive or active, is rather a fascinating route to take.

Paper session 3

10 April 2018

Törmänen, T. (2018, April)

Exploring collaborative groups’ emotional states with video and physiological data

Paper session presented at the LET2018 Conference, Oulu

The analysis of this study is still ongoing, so the presentation did not offer results nor a discussion of the results. Nonetheless, as it was conducted under a different project, we had the chance to expand our understanding of the field, and look at SRL constructs from a different perspective. The aim of the project is to investigate groups' emotional states during collaboration and to explore how groups regulate emotions, and how regulating emotions affects collaboration. Within this project, the researcher in this study wants to look into the variations of emotional states that groups display as well as to examine if EDA data can be associated with those states. Furthermore, to identify situations in which there is PS between group members. Would these situations require regulative actions to be taken?

Here we are talking about academic emotions (Pekrun & Schutz, 2007) which are fundamental to motivation and academic success through learning that is taking place in schools, which are settings laden with social and emotional features (Pekrun, 2016). Why is it important to study emotions in schools? According to Pekrun, Goetz, Titz, & Perry (2002) emotions can predict academic engagement and attainment, but considering negative emotions to be bad and positive emotions to be good is too simplistic for understanding SRL or SSRL. There is a need to differentiate between their 'usefulness' in allowing SRL skills to develop or to be applied (activating or de-activating emotions). This can inform the design of learning and teaching environments and different types of learning support.

Data for this study were collected during a collaborative learning task which involved 41 6th grade students working on a science project. Data sources include video recordings and EDA measurements.

Preliminary results show a big variation in the amount of socio-emotional segments (verbal or non-verbal signs of positive or negative emotions, or negatively or positively charged interaction) in different groups, ranging from 22 to 94% of the time being coded as such. The most common emotions observed were negative activating. Further analysis of the results is ongoing.

It was important to discuss the impact of group formation on the collaborative experience. As explained during the presentation, students filled in an individual task interest questionnaire prior to the group work that served as a basis to create as heterogeneous groups as possible.

Another question that was raised concerned the interpretation of data that come from different sources and do not seem to be showing a match. How can one go beyond the data that is recorded? Clearly, there are some rather important methodological questions waiting to be answered.

Siklander, P. (2018, April)

I like to make people laugh: Adult playfulness among educators

Paper session presented at the LET2018 Conference, Oulu

Playfulness is not a new concept, it was researched already by Lieberman in the seventies (Lieberman, 1977). What makes it topical is the growing tendency that children spend less and less time being engaged in play both in the school as well as during their free time. Jarvis, Newman, & Swiniarski have examined this phenomenon in Anglo-American societies (2014), while Wyver, et al. (2010) observed how excessive fear of risk or surplus safety unnecessarily limits children's experiences.

Why is it important for education, or specifically for teachers? “Playful people are uniquely able to transform virtually any environment to make it more stimulating, enjoyable and entertaining” (Barnett, 2007). The researchers in this study wanted to explore how educators conceive their own playfulness, using an online questionnaire based on the Adult Playfulness Scale (APS), involving 123 participants.

Results showed that educators consider themselves to be creative, enjoy being with other people, want to know and learn more, and are flexible. There was no indication of differences in perception about playfulness based on age, but there was a difference based on gender, with male educators appearing to be slightly less playful than females.

As a mother, I believe in the importance of creating an environment that allows children to develop a range of abilities and skills, competencies. And as a social being in general, I enjoy and strive to create opportunities for shared experiences and shared fun, which in my view are essential for not just cognitive, but also social and emotional development.

I was surprised to discover that there is a scientific journal dedicated to play related matters, the International Journal of Play, which was established in 2012. I will certainly spend some time browsing amongst the volumes.

Further thoughts

1. If playfulness is linked to creativity and innovation (Bateson & Martin, 2015), does it make it a 21st-century skill?

2. Thinking about play also made me think about gamification. It has been a hot topic in educational settings for increasing motivation and engagement. A couple years ago, I read a book on how an English teacher in a secondary school has transformed his practice by adopting and combining the concepts of personalised learning and gamification (Prievara, 2015). Gamification means ‘‘the use of game design elements in non-game contexts’’ (Deterding, Dixon, Khaled, & Nacke, 2011, p. 10). It is about making a process more engaging so that participants have more motivation to complete it, by adding four components from established games (Gamification vs. Game-Based Learning, 2017), such as

mechanics: points, levels, progress bars, leaderboards, badges, constant feedback, etc,
rewards: a trophy and the symbolism behind it,
measurement: evaluation of the results and
behavior: users become loyal and that helps them enjoy a process.

We all know, that academic learning is not always engaging, so finding ways to support learners is always welcome. However, recently I came to know that gamification and gameful learning/design are not interchangeable concepts. Gameful design refers to the use of "games as inspiration for changes to the type and structure of tasks given to learners, with the goal of better supporting intrinsic motivation" (Aguilar, Holman, & Fishman, 2015, p. 45). Holden, et al., (2014) defines gameful learning as a framework that encourages improvisation, playfulness, and social interaction. It would be interesting to compare 'playful learning' and 'gameful learning' and explore how the ideas behind each could be blended to create a state of the art learning and teaching environment.

3. Based on my personal interest in workplace learning, I did a little research regarding playfulness at work. With creativity, innovation and collaboration being high on the skills agenda for being successful at work (and life), incorporating play into workplace learning carries a lot of potential. Results from a study by Yu, Wu, Chen, & Lin (2007) indicates that play is beneficial for work from several aspects, such as job satisfaction, innovative behavior and job performance. West (2015; also West, Hoff, & Carlsson, 2017) explored playfulness and its role in enhancing creativity in an organizational setting.

References

Aguilar, S. J., Holman, C., & Fishman, B. J. (2015). Game-Inspired Design: Empirical Evidence in Support of Gameful Learning Environments. Games and Culture. doi:10.1177/1555412015600305
Barnett, L. (2007). The nature of playfulness in young adults. Personality and Individual Differences,43(4), 949-958. doi:10.1016/j.paid.2007.02.018
Bateson, P. P., & Martin, P. R. (2015). Play, playfulness, creativity and innovation. Cambridge: Cambridge University Press.
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining ‘‘gamification.’’ In Proceedings of the 15th International Academic MindTrek Conference: Envisioning future media environments (pp. 9–15). New York, NY: ACM. Retrieved from http://doi.org/10.1145/2181037.2181040
Gamification vs. Game-Based Learning. (2017, September 28).
Retrieved from https://www.ispringsolutions.com/blog/gamification-vs-game-based-learning/
Holden, J. I., Kupperman, J., Dorfman, A., Saunders, T., Pratt, A., & Mackay, P. (2014). Gameful learning as a way of being. International Journal of Learning Technology, 9(2), 181. doi:10.1504/ijlt.2014.064492
Jarvis, P., Newman, S., & Swiniarski, L. (2014). On ‘becoming social’: The importance of collaborative free play in childhood. International Journal of Play, 3(1), 53-68. doi:10.1080/21594937.2013.863440
Lieberman, J. N. (1977). Playfulness: Its relationship to imagination and creativity. New York, NY: Academic Press
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic Emotions in Students’ Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research. Educational Psychologist, 37(2), 91– 106. https://doi.org/10.1207/S15326985EP3702
Pekrun, R. (2016) Academic emotions. In Wentzel, K. R., & Miele, D. B. (Eds.) Handbook of motivation at school (pp. 120-144). New York, NY: Routledge.
Pekrun, R., & Schutz, P. A. (2007). Emotion in education. Amsterdam: Elsevier.
Prievara, T. (2015) A 21. századi tanár: Egy pedagógiai szemléletváltás személyes története (A 21st-century teacher: a personal story of the change of a pedagogical view). Budapest: Neteducatio
West, S. (2015). Playing at Work: Organizational Play as a Facilitator of Creativity, Department of Psychology, Lund University
West, S., Hoff, E., & Carlsson, I. (2017). Enhancing team creativity with playful improvisation theater: A controlled intervention field study. International Journal of Play, 6(3), 283-293. doi:10.1080/21594937.2017.1383000
Wyver, S., Tranter, P., Naughton, G., Little, H., Sandseter, E. B., & Bundy, A. (2010). Ten Ways to Restrict Childrens Freedom to Play: The Problem of Surplus Safety. Contemporary Issues in Early Childhood, 11(3), 263-277. doi:10.2304/ciec.2010.11.3.263
Yu, P., Wu, J. J., Chen, I. H., & Lin, Y. T. (2007). Is playfulness a benefit to work? Empirical evidence of professionals in Taiwan. International Journal of Technology Management, 39(3/4), 412. doi:10.1504/ijtm.2007.013503

PS3

PS4

Paper session 4

12 April 2018

Mykkänen, A (2018, April)

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

Paper session presented at the LET2018 Conference, Oulu

Group awareness tools are aimed at either prompting externalization of thoughts and feelings or helping to visualize them for others (or both) for aiding cognitive processes and/or socially shared regulation of learning (Bodemer & Dehler, 2011; Janssen, Erkens, & Kirschner, 2011; Kim & Ryu, 2013). For a separate course, we were tasked to design such tools for an existing learning platform. Our team chose Claned as a base for our work. So I was quite interested to see how the tool used in the study compared to ours as well as to evaluate our tool based on the findings of this research.

The study explored user experiences by asking questions on the perceived advantages and disadvantages of using the tool. Data were collected from 44 second-year teacher education students, who worked on a collaborative learning task in groups of three-five for seven weeks. Interviews were conducted after the course to find out how beneficial the students found the tool and whether and how it contributed to their group's performance. Results were obtained through data-driven content analysis.

The most often noted positive outcome of using the tool was the increased awareness of own and other's state of mind. The most common negative outcome was that the tool did not provide benefits for the group, which is interesting when we compare it with the second most common negative outcome that the tool was used hastily or was completely ignored. An ignored tool will surely not bring any benefits.

As collaborative learning designs are not so commonly applied, it is important to educate students about the advantages or affordances of such type of learning (Vuopala, ????). For this reason, in our design, we included an intro session that would increase students' knowledge about collaborative learning and would introduce the features of the tool and reasons for using them.

One interesting question was raised during the discussion. It was related to the capacity of the users to exercise their agency for regulation themselves. What if they had the chance to initiate the prompts as the need arises? This, of course, would require that students are able to identify challenge episodes as they are developing. However, there needs to be enough data for research purposes, which cannot be ensured if it's up to the participants to generate it.

Kurki, K. (2018, April)

Exploring regulatory interactions among young children and their teachers – focus on teachers’ monitoring

Paper session presented at the LET2018 Conference, Oulu

Emotion regulation amongst small children is an issue I deal with day-in day-out, so the presentation was very topical. The study was focusing on the effects of teacher involvement on small children's emotion and behavior regulation. Monitoring here refers to a teacher's action regarding observation of the classroom environment, which has an impact on how the teacher is able to adapt the support that s/he provides based on situational circumstances.

The aim of the study was to investigate what kinds of emotion and behavior regulation strategy types children use independently or with teacher support and to explore how teachers’ level of monitoring contributes to children’s strategy use. Data were collected by observing 30 participating children (2-5 years old) and 8 teachers working in a daycare facility, that is built for research purposes and is equipped with videos and microphones.

Kurki, Järvenoja, Järvelä, & Mykkänen (2017) categorize emotion and behavior regulation strategies as a) situation modification (SM), b) situation selection (SS), c) providing information about one’s own will or situation (PI), d) redirecting one’s own activity/attention (RA) and e) response modulation (RE). Results from the video analysis show that there is a significant difference between strategies used independently by children (SM being most common) and strategy choices affected by teacher involvement (RA were most common). Strategy adaptation is much more likely under teacher guidance.

Emotions are essential in understanding and managing classrooms. Meyer & Turner (2006) introduce the term 'emotional scaffolding' "as temporary but reliable teacher-initiated interactions that support students’ positive emotional experiences to achieve a variety of classroom goals" such as "increasing student achievement and autonomy in a particular developmental competency" (p. 236), in this case the competency to regulate emotions and behavior. Scaffolding is a balance between teacher support and student autonomy, where support is provided when needed while the student gradually assumes more and more responsibility. The idea is very similar to the concept of the zone of proximal development (ZPD) developed by Vygotsky (1978).

After the presentation we discussed the importance of support that parents are able to provide or not able to provide. Would educating parents about the importance of regulation and modeling strategies make sense? Of course, it would be beneficial, but what would be the best way to make it happen. How to involve parents in a way that is also educational?

As children spend a lot of time in daycare nowadays, it would be interesting to see whether they able to transfer these regulation skills from daycare setting to home and vica versa.

We also talked about the implications for practice, amongst others the student-teacher ratio in daycares and schools, and how the chances for development decrease for each student the more they are in relation to the number of teachers, for the reasons mentioned above. What is the ideal ratio at different stages of schooling taking into account social as well as economic factors?

Siklander, P. (2018, April)

Nature as a setting and resource to promote learners’ agency and competences in education

Paper session presented at the LET2018 Conference, Oulu

This study was also very topical, as I was involved with a nature education project around the same time. Also, my background is in community based learning, including informal and non-formal education and youth work. Based on my personal experience, outdoor learning has been regarded as a tool to engage at-risk children or those who need an alternative style of education. Combining physical activity and abstract thinking fuels the learning process in different ways than traditional classroom activities (Mygind, 2007) and so it should not be reserved for a few. Especially, if we take into account that children with poor personal and social skills tend to benefit the most from outdoor learning, but they usually come from less well-off families and so are most likely to miss out on such opportunities (Scrutton, 2014).

The analysis of the results of this study is still ongoing, so the findings are only tentative. Data were collected during a 3-day hiking trip in a national park in Finland, involving 21 8th grade students and 2 teachers. Students were asked to keep digital diaries (with photos and reflective notes). The researcher kept audio-recorded field notes and photos as part of the participant observation and conducted interviews with both students and teachers. The study wanted to find out in what ways student agency emerges during the course and how the competencies needed in the activities manifest. Qualitative inductive content analysis was used to dissect the findings, according to which student agency emerged through collaboration, resilience, by taking responsibility and the experience of shared successes.

One of the implications of these preliminary findings is the need to include nature and other out-of-classroom settings as potential learning contexts for teachers to train in to be able to discover the possibilities that nature provides (Maynard & Waters, 2007) and to have the chance to develop alternative curriculum-based learning programs, highlighting the need to focus on skills and competence development, rather than on grades. That way students can become creators of the learning process, and develop a positive attitude to learning.

There is a need for such programs also from outside agencies, such as Metsähallitus (Parks & Wildlife Finland), who established the Eräkummi or 'wildlife tutor' project to aid schools and teachers with knowledge and resources in implementing nature-based learning as well as learning about nature.

I was pleased to discover the Journal of Adventure Education and Outdoor Learning.

References

Bodemer, D., & Dehler, J. (2011). Group awareness in CSCL environments. Computers in Human Behavior, 27(3), 1043-1045. doi:10.1016/j.chb.2010.07.014
Janssen, J., Erkens, G., & Kirschner, P. A. (2011). Group awareness tools: It’s what you do with it that matters. Computers in Human Behavior, 27(3), 1046-1058. doi:10.1016/j.chb.2010.06.002
Kim, M., & Ryu, J. (2013). The development and implementation of a web-based formative peer assessment system for enhancing students’ metacognitive awareness and performance in ill-structured tasks. Educational Technology Research and Development, 61(4), 549-561. doi:10.1007/s11423-012-9266-1
Kurki, K., Järvenoja, H., Järvelä, S., & Mykkänen, A. (2017). Young children’s use of emotion and behaviour regulation strategies in socio-emotionally challenging day-care situations. Early Childhood Research Quarterly, 41, 50-62. doi:10.1016/j.ecresq.2017.06.002
Maynard, T., & Waters, J. (2007). Learning in the outdoor environment: A missed opportunity? Early Years, 27(3), 255-265. doi:10.1080/09575140701594400
Meyer, D. & Turner, J.C. (2006) Scaffolding Emotions in Classrooms. In Pekrun, R., & Schutz, P. A. (Eds.). Emotion in education (p. 235-250). Amsterdam: Elsevier.
Mygind, E. (2007). A comparison between childrens physical activity levels at school and learning in an outdoor environment. Journal of Adventure Education & Outdoor Learning, 7(2), 161-176. doi:10.1080/14729670701717580
Scrutton, R. A. (2014). Outdoor adventure education for children in Scotland: Quantifying the benefits. Journal of Adventure Education and Outdoor Learning, 15(2), 123-137. doi:10.1080/14729679.2013.867813
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. (M. Cole, V. JohnSteiner, S. Scribner, E. Souberman, Eds.). Cambridge, MA: Harvard University Press

Key-note

24 April 2018

Järvelä, S (2018, April)

Multimodal data to understand students’ cognition, metacognition, motivation and emotions in a learning process

The key-note speech took place at the end of the course, as the last presentation, which made it more like a summary as opposed to an introduction to the ideas and concepts that were previously discussed in detail.

For this reason, I also use this space to provide a summary.

Learning is a complex process with elements that are hard to directly observe. The aim of the learning sciences is to understand this process in order to design learning events and better support for successful learning, be it individual or in collaboration.

Research at LET heavily relies on self-regulated learning (SRL) theory, in combination with collaborative learning (CL) and technology-enhanced learning. The aims of current research are to investigate regulatory processes in authentic collaborative learning situations, to explore what multimodal data can tell about critical SRL processes and to develop scaffolds and support for socially shared regulation of learning (SSRL) in computer-supported collaborative learning (CSSL).

Technological evolution forces and enables the field to progress and experiment with new data collection tools and methods, that comes with new challenges regarding research design and data analysis, making learning research a highly collaborative multidisciplinary endeavor. Multimodal data channels include 360-video and audio, physiological data through multisensor devices, log data, questionnaires, reports, mobile eye-tracking, and artifacts.

Main challenges result from the immense amount of data being generated through these channels.

Over-/mis- interpretation of physiological data
Data triangulation
Ideal sampling rates of each technique and data granularity
Minimizing the costs of multimodal data collection and handling

However, the possibility to revolutionalize learning is around the corner. Advances in machine learning and artificial intelligence combined with our improved understanding of the learning process we have the chance to increase human capacity fit for the 21st century.

RESEARCH PROPOSAL

Prepared for the course on Qualitative Methodology (408517S-02) 2018

INTRODUCTION

THE ISSUE - Workplace learning

The societal and technological developments of recent decades have brought about the idea of lifelong learning, relating to the realisation that education is not something that prepares people for life, but that learning is something that continues throughout one’s lifetime, requiring individuals to take a more active role in shaping their own paths. A paradigm shift is therefore required regarding both adult learning as well as the ways how adult learning is supported in the workplace (Hiemstra, Carré 2013). An organisation’s most important assets are the skills and knowledge of its employees. As workplaces evolve both socially and technologically, training for soft skills is becoming the no1 priority for talent managers (LinkedIn, 2018).

THE CONTEXT - The information technology (IT) industry

The information technology industry, within the broader technology field, is the driver and enabler of change in all other industries. Tools and solutions of the trade get all the time updated, with many of them becoming obsolete in a very short time. Innovation is constant. People working in such environments need to be able to learn fast and efficiently. Demand for tech talent is outstripping supply (in 2017 there were 1000 jobseekers and 8000 job openings in Finland alone; IT-alalla yliopistotutkinnon suorittaneissa vähiten työttömiä, 2017) and is likely to increase as more and more organisations are consciously planning for a digital future. According to IT trend analysts, there is a need to rethink “ approaches to recruiting, training, and talent management. Additionally, questions surrounding skills gaps, diversity, alternative education/career paths and the future of work will demand more meaningful attention and resources” (Comptia, 2018).

THE THEORY - Self-directed learning and self-regulated learning

Self-directed learning (SDL) is a theoretical concept that refers to any form of intended learning that is initiated by the learner, and further encompasses the planning, implementation, and evaluation of the learning activity and efforts exerted (Rothwell, Sensenig 1999). In a study with ICT workers Gijbels, Raemdonck, Vervecken, Herck (2012) found that SDL orientation was significantly and positively linked to work-related learning behaviour.

Self-regulated learning (SRL) is a similar concept to SDL, as both assume personal agency and a goal-oriented behaviour to learning (Zimmermann, 2000), but they are not interchangeable. Merriënboer & Kirschner (2018) allows a distinction to be made as SDL referring to instructional-sequence level, while SRL referring to the task or topic level. Both SDL and SRL include the subprocesses of monitoring and control, that are required for effective learning. SRL has been gaining prominence outside of the formal educational settings (Littlejohn, Margaryan, & Milligan 2009) and so it is chosen as a theoretical concept to underpin this study.

THE GAP

While interest is growing, SRL has been scarcely studied empirically in the workplace, as it has been more the domain of formal education and academic learning, with an extensive literature ranging from early childhood education to tertiary education. One example study by Milligan, Fontana, Littlejohn, & Margaryan (2014) focused on discovering relationships between the learning context, SRL processes and actual learning undertaken and found that SRL behaviour has a mediating role on actual learning uptake within the learning context.

As the beliefs about learning and the landscape of education are shifting, people will adopt more individual paths and seek uncommon solutions for gaining skills and competence, therefore, there is a need for mapping out how practitioners in charge of learning in the field see the issues studied by learning scientists. As argued earlier, the IT field is a suitable context in which to conduct such investigation. Thus far, no other research located has taken this view.

OBJECTIVES OF THE STUDY

AIMS

The proposed study aims to explore human resource development (HRD) professionals’ beliefs concerning effective individual learning in the workplace in order to discover ways for developing more efficient instructional design practices.

RESEARCH QUESTIONS

Q1.1: How do current corporate learning professionals understand what constitutes effective individual learning?
Q1.2: How do current corporate learning professionals enable effective individual learning at the workplace?
Q1.3: Does SRL skill development feature in it in any way?
Q2.1: What are the biggest challenges in corporate learning design and implementation?

Q2.2: Could addressing SRL skill development be playing a role in overcoming (some of) them?

METHODOLOGY

DESIGN

Both deductive and inductive approaches were considered for this study and in order to take advantage of the strengths of both qualitative and quantitative inquiry, a pragmatic view of methodological pluralism was adopted ( Teddlie & Tashakkori, 2010; David, 2011) resulting in the use of mixed methods for data collection purposes that are in essence qualitatively driven (Morse & Niehaus, 2016), focusing more on exploration. The purposes of using different methods can be identified as 1) developmental, as results from one method can inform other methods and measurements, 2) complementary, as it allows the investigation of different aspects of the research questions in order to elaborate, enhance or clarify findings, as well as 3) triangulation, to provide the possibility to corroborate the findings of each method (David, 2011).

Potential participants will have a job role that includes elements of one or a combination of the following: human resource management, human resource development, recruitment, talent acquisition, talent management, learning development, coaching, career advising, training or employee engagement and work for companies or organisations that employ a dedicated software development team.

The need for identifying and gaining access to potential participants requires the use of one or more purposive sampling techniques. Contacts will be sought through personal and professional networks by seeking recommendations and personal referrals.

DATA COLLECTION

There will be two stages of data collection. The first stage, which is the qualitative core component of the study, will involve gathering data from participants through in-depth interviews. Connections will be looked for until a minimum of 5-7 people with relevant insight have consented to take part in the study. In case of not reaching this number, people with less specific job roles will be considered (like project managers).

In order to elicit rich, illuminating data I plan to conduct semi-structured or unstructured interviews that are more suited to discovering participants perceptions in detail and allow the emergence of views, concepts or themes that were not anticipated prior to the interview (Basit, 2010). Participants who consent to be interviewed will be informed a priori about a loose framework regarding the topics that I propose to discuss, highlighting that it is only intended as a guide for the conversation.

The interviews will take about forty-five minutes with the possibility to extend the timeframe if both parties agree that it would be beneficial, and permission will be sought from each participant to use a tape-recorder to record the interview. Some of the interviews will take place at the participant’s place of work, while others through online video chat. The possibilities of building rapport online need to be carefully considered in order to have the chance to exploit all the benefits of the more informal, conversational style of interviewing.

The second stage of the data collection will take place after the initial data has been analysed and themes have appeared. A small scale survey will be developed and previous interview participants will be invited to pre-test the survey instrument. Feedback sought will include questions regarding completion time, easy of interpretation, relevant questions that need to be added and open comments (Bell, 1999). Changes to the survey instrument will then be made on the basis of this feedback.

The survey will be conducted online for ease of access and distribution. Professional networks will be used to disseminate the survey, expecting a minimum of 20 responses. In the event of not reaching this number, people from companies employing IT workforce without dedicated software teams, or people in job roles other than the ones described will be contacted (like CEOs or managers of IT consultancies).

DATA ANALYSIS

Since the purpose of this study is to develop themes regarding the beliefs of HRD professionals on effective individual learning, it is important to treat the data thoroughly and so analysis will be done in an iterative fashion. An inductive analysis will be followed to reduce researcher bias as much as possible. The method of qualitative content analysis is best suited for this purpose as it makes it possible to explore and categorize large amounts of textual data with the intention of discovering themes and patterns, their frequency and the relationships between them (Basit, 2010).

The first level of coding will serve the purpose of breaking up the data to identify concepts. In order to do that, audio data will be transcribed and imported into computer programs that can assist in the analysis, such as nVivo or other. The second level of coding will be theoretically driven and will seek to find connections and other logical links with the proposed theoretical concepts.

The aim of using a survey in this study is not generalizability. The low number of respondents does not allow statistical analysis. Data generated through this method will be used to expand upon the findings from the interviews.

A joint display technique will be used to integrate data from both the qualitative and quantitative methods (from which findings have been analysed separately beforehand), which will enable the two data sets to be studied together (Miles & Huberman, 1994; Ocathain, Murphy, & Nicholl, 2010). This can be done for example by following the steps of the Pillar Integration Process that were developed by Johnson, Grove, & Clarke (2017).

VALIDITY AND RELIABILITY

In qualitative research, validity can be addressed through ensuring that the data collected is honest, rich and in-depth. These issues were considered when choosing the interview method and the type of interviewing. Furthermore, the sampling technique proposed also contributes to reaching a higher level of validity. Employing another data collection method serves the purpose of triangulation, by enabling the corroboration of the findings contributing to concurrent validity. External validity is likely to be lower, resulting from the small-scale nature of the study. The perceived significance of the research topic for the participants is expected to contribute to a high level of reliability, alongside the honest and precise data collection and analysis procedures followed.

REFERENCES

Basit, T. N. (2010). Conducting research in educational contexts . New York: Continuum International Pub. Group.
Bell, J. (1999). Doing your research project: A guide for first-time researchers in education and social science . Buckingham: Open University Press.
Comptia (2018) IT industry outlook 2018. Retrieved from https://www.comptia.org/resources/it-industry-trends-analysis
David, M. (2011). Social research: An introduction . London: Sage Pubn.
Gijbels, D., Raemdonck, I., Vervecken, D., & Herck, J. V. (2012). Understanding work-related learning: The case of ICT workers. Journal of Workplace Learning, 24 (6), 416-429. doi:10.1108/13665621211250315
Hiemstra, R., & Carré, P. (2013). A feast of learning: International perspectives on adult learning and change . Charlotte, NC: Information Age Publishing
IT-alalla yliopistotutkinnon suorittaneissa vähiten työttömiä. (2017, July 13). Retrieved from https://www.jyu.fi/ajankohtaista/arkisto/2017/07/tiedote-2017-07-06-10-55-32-008894
Johnson, R. E., Grove, A. L., & Clarke, A. (2017). Pillar Integration Process: A Joint Display Technique to Integrate Data in Mixed Methods Research. Journal of Mixed Methods Research, 155868981774310. doi:10.1177/1558689817743108
Merriënboer, J. J., & Kirschner, P. A. (2018). Ten steps to complex learning: A systematic approach to four-component instructional design . New York, NY: Routledge.
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook . Thousand Oaks: Sage Publications.
Milligan, C., Fontana, R. P., Littlejohn, A., & Margaryan, A. (2015). Self-regulated learning behaviour in the finance industry. Journal of Workplace Learning, 27 (5), 387-402. doi:10.1108/jwl-02-2014-0011
Morse, J. M., & Niehaus, L. (2016). Mixed Method Design: Principles and Procedures . Milton, Abingdon: Taylor and Francis.
LinkedIn (2018) 2018 Workplace Learning Report - The Rise and Responsibility of Talent Development in the New Labor Market. Retrieved from https://learning.linkedin.com/elearning-solutions-guides/workplace-learning-report-2018
Littlejohn, A., Margaryan, A., & Milligan, C. (2009). Charting Collective Knowledge: Supporting Self-Regulated Learning in the Workplace. 2009 Ninth IEEE International Conference on Advanced Learning Technologies . doi:10.1109/icalt.2009.14
Ocathain, A., Murphy, E., & Nicholl, J. (2010). Three techniques for integrating data in mixed methods studies. Bmj, 341 (Sep17 1), C4587-C4587. doi:10.1136/bmj.c4587
Rothwell, W. J., & Sensenig, K. J. (1999). The sourcebook for self-directed learning . Amherst, MA: HRD Press.
Teddlie, C., & Tashakkori, A. (2010). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences . Los Angeles: SAGE Publ.
Zimmermann, B. J. (2000) Attaining Self-Regulation: A Social Cognitive Perspective. In Pintrich , P., Boekaerts, M., Zeidner, M. (Eds.) Handbook of Self-Regulation (pp 13–39). Academic Press