Cross-age peer tutoring in a technology-enhanced STEAM proj

刊名: International Journal of Technology and Design Education 作者:Satu Tenhovirta1 · Tiina Korhonen2 · Pirita Seitamaa-Hakkarainen1  · Kai Hakkarainen1 来源:International Journal of Technology and Design Education 发布时间:2021-07-07 10:30
Keywords Advice size Co-invention Cognitive centrality Cross-age tutoring Maker- centred learning STEAM Peer tutoring * Pirita Seitamaa-Hakkarainen Satu Tenhovirta Tiina Kor
Keywords Advice size · Co-invention · Cognitive centrality · Cross-age tutoring · Maker- centred learning · STEAM · Peer tutoring
* Pirita Seitamaa-Hakkarainen Satu Tenhovirta Tiina Korhonen Kai Hakkarainen 1 Faculty of Educational Sciences, University of Helsinki, Helsinki, Finland 2 Innokas Network, Faculty of Educational Sciences, University of Helsinki, Helsinki, Finland
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The purpose of the present investigation was to examine cross-age peer tutoring in the context of maker-centred learning (Clapp et al., 2016) at a lower secondary school in the capital region of Finland. By cross-age peer tutoring, we refer to engaging technologically proficient older students in supporting their younger peers’ collaborative making pro- cesses. Together with traditional tools, emerging digital fabrication technologies enable the engagement of young students in inventing, designing, and making complex artefacts, sparking engineering and technological challenges. Educational maker activities can be productively connected with integrated Science, Technology, Engineering, Arts, and Math- ematics (STEAM) studies, which also play a central role in the Finnish curriculum. Mak- ing projects provide multi-faceted technological (tools) and social (community) resources that enable young people to participate in socially creative practices of invention (Blik- stein, 2013; Clapp et al., 2016; Halverson & Sheridan, 2014; Hatch, 2014; Petrich et al., 2013; Riikonen et al., 2020), which appear crucial for cultivating 21st century competences (Binkley et al., 2012). Due to drastic changes in society and working life that digitalisation is bringing about, educational institutions have had to renew their practices of learning and teaching in terms of engaging students in project-based structured activities that involve the creative use of sociodigital technologies. This concept refers to the recently emerged integrated system of mobile and wireless technologies, social media, digital fabrication tools, and internet (Hakkarainen et al., 2015).
Investigators are concerned about the creative participation gap (Jenkins et al., 2006) in terms of only advantaged students having access to the creative and academic practices of using digital technologies (Barron, 2004). Many students who have developed consider- able digital competences through informal activity feel that their out-of-school digital crea- tive competences are not at all acknowledged at school; consequently, they may become increasingly alienated and cynical at school and lose their motivation (Hietajärvi et al., 2020). Thus, education systems should embrace young people’s sociodigital competences learned outside classrooms and provide legitimate contexts for their productive use, nur- ture, and refinement, as emphasised by the connected learning framework (Ito et al., 2013).
As some students are already skilful, and often more so than their teachers, authorities must begin to capitalise on peer-to-peer social learning resources, such as cross-age peer tutoring, when initiating challenging STEAM projects in schools. Peer tutoring is not a new approach, and its importance has already been mentioned, for instance, in the newest Finnish curriculum (NCCBE, 2014).
Nevertheless, the prevailing educational practices have not yet integrated the systematic use of cross-age peer tutoring in different school subjects, such as design and technology education. Cross-age peer tutoring involves engaging older students (one grade above) in systematically assisting and helping their younger peers (lower graders) in pursuing long- standing maker-centred learning projects. It follows that cross-age peer tutoring does not merely mean collaborative learning in mixed-age groups, but also provides older tech- nologically proficient students with a legitimate role in tutoring young peers’ efforts of designing, inventing, and making artefacts (Duran & Topping, 2017, p. 66). The present study focused on developing a cross-age peer tutoring system by training 7th graders to take a productive part in collaborative maker-centred learning through assisting 8th grad- ers (Clapp et al., 2016). Because teachers are often not as fluent technology users and only have limited time for refining their digital competences, peer tutors may play a crucial role in integrating the advanced use of digital technologies with school practices. Cross-age
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peer tutoring has not yet been extensively studied in the emerging context of maker-centred learning in education. The present investigation contributes to both filling the research gap and sharing experiences of developing associated tutoring pedagogies and practices rel- evant for improving STEAM education.
Maker-centred learning cultivating creativity in schools
The term maker-centred learning has been developed to define learning processes where the aim is to foster creativity through intensive learning by making processes within a shared interactive space (Clapp et al., 2016). For learning to function productively in the emerging innovation-driven knowledge society, young people have to be socialised, early on, to innovative practices working with knowledge, media, and artefacts (Hakkarainen, 2009). The present investigators examine such practices in terms of knowledge-creating learning, which involves collaborative efforts of advancing shared tangible objects of enquiry (Paavola & Hakkarainen, 2014; Ritella & Hakkarainen, 2012). Maker-centred learning is based on the idea of constructionism: Active hands-on and participatory learn- ing designed to help pupils to engage in a creative process under the guidance of instruc- tors. Although many investigators from Piaget to Papert (1980) have emphasised the importance of learning by constructing and inventing artefacts, the present digital fabrica- tion technology allows practices of a “maker culture” to be brought to schools in terms of feasible projects with hitherto unforeseen complexity and intellectual challenges (Papav- lasopoulou et al., 2017; Schad & Jones, 2020). The present study focused on bringing ele- ments of a maker culture to a school in terms of engaging teams of 7th grade students in pursuing co-invention projects that called for the use of digital fabrication and traditional technologies in designing and making artefacts (Blikstein, 2013; Riikonen et al., 2020).
Thus, maker-centred learning has sparked interest in primary and secondary education due to its emphasis on STEAM-related themes (Petrich et al., 2013; Hsu et al., 2017). By rely- ing on visions of integrated STEAM projects as well as on the extensive Finnish tradition of craft and technology education (Riikonen et al., 2020), the present investigators have been working on integrating longitudinal maker-centred projects as an integral part of ele- mentary and secondary education.
Towards that end, it is important that the newest Finnish National Core Curriculum for Basic Education (NCCBE, 2014) involves phenomenon-based, i.e. integrative thematic, studies that provide opportunities to engage students in cross-disciplinary maker projects (Silander et al., in press). Such projects focus on open-ended challenges and complex problems, the successful investigation of which usually requires integrating several school subjects, including craft education. Rather than relying on linear pedagogy characterised by pre-given knowledge, tasks, stages, and outcomes, such maker-centred projects rely on nonlinear pedagogy in terms of pursuing open-ended innovation challenges, emerging objects, unanticipated tasks, indeterminate stages, and unforeseen outcomes (Härkki et al., 2020). It follows that the projects may require multi-faceted and unforeseen skills when using digital instruments, such as coding platforms, microprocessors, robotics, e-textiles, and 3D printing. The digital methods and practices necessary for completing a team pro- ject may not only be new for students but also for many teachers. Because the orchestra- tion of such projects is extremely challenging for teachers, we engage multi-disciplinary teams of teachers in organising them (Härkki et al., 2020). Practices of cross-age peer tutoring provide significant support for teachers, allow them to focus on flexible pedagogic
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orchestration, and prevent them from becoming overwhelmed by the technological chal- lenges of the diverse digital instruments employed.
Science and technology studies indicate that rather than arising from mere logical argu- ments, knowledge creation is distributed and stretched over scientific concepts and instru- ments, methods, and procedures; embodied arrangements of laboratory spaces; and net- works of peers and experts (Knorr Cetina, 1999; Nersessian, 2006; Ritella & Hakkarainen, 2012). Accordingly, the actionable implementation of STEAM practices and maker-cen- tred learning requires special learning spaces called makerspaces (Papavlasopoulouet et al., 2017; Schad & Jones, 2020). Makerspaces are dynamic, loft-like spaces where children come with their teachers (and sometimes parents) to pursue their interest-driven making projects, share their design challenges, and work individually or collaboratively—often supported by adult facilitators (Gutwill et al., 2015). To that end, makerspaces provide a wide variety of traditional and digital fabrication tools, materials, and resources for sup- porting maker-centred learning (Gutwill et al., 2015). The emergence of a maker culture has resulted in an increase in makerspaces in schools, libraries, and other informal learning environments (Halverson & Sheridan, 2014; Jaatinen & Lindfors, 2020). In many coun- tries, maker-centred learning usually takes place during afterschool clubs rather than in school (Halverson & Sheridan, 2014; Kafai & Peppler, 2011), but an educational maker culture has resulted in makerspaces being established in schools or school libraries. Fin- land has, however, a long tradition of craft education; it is a compulsory school subject for students in grades 1–7 and an optional one in grades 8–9. Consequently, our country has its own educational maker culture in terms of all schools having makerspaces in the form of craft classrooms (Jaatinen & Lindfors, 2020), where various multi-material (metal, textile, wood) maker activities have been pursued for almost 150 years. The emergence of digital fabrication has expanded the scope of such innovation activities considerably (Riikonen et al., 2020; Sinervo et al., 2020). Craft education covers various textile and technical skills and techniques, together with emphasising designing, problolving, and craft expression.
Maker-centred learning engages students in externalising their ideas through conceptual (spoken or written ideas), visual (drawings, sketches), or material (3D prototypes and models) artefacts, creating an opportunity for themselves and their peers to build on these ideas, discuss and elaborate upon them, and embody such ideas in more advanced artefacts (Mehto et al., 2020). Besides traditional tools and techniques, the use of microcontrollers (e.g. Picaxe; Micro:bit), sensors, robotics (e.g. Lego EV3), 3D printing, and, recently, also e-textiles has become a part of craft education in Finnish schools (Korhonen & Lavonen, 2017; Seitamaa-Hakkarainen & Hakkarainen, 2017).
The cross-age peer tutoring approach
Willis and colleagues (2012) defined tutoring as a platform for providing young people with social support, giving goal-oriented academic assistance, and, when appropriate, pro- moting positive identity development. The rationale of peer tutoring is often anchored in the concept of the zone of proximal development (ZPD; Vygotsky, 1978), representing the distance between what a learner (tutee) can do independently and what he/she can do with the help of more knowledgeable others (tutor). Accordingly, it is critical to challenge learn- ers to go beyond their prevailing knowledge and competences and to provide facilitating coaching and support through more knowledgeable and skilful peers and beyond that of teachers. After having themselves recently gone through similar learning challenges, the
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tutors are likely to relate to the tutees’ cognitive challenges; this increases the pedagogic value of peer tutoring. As an adult expert may be too distant in terms of authority and knowledge, peer tutoring may enhance opportunities for mutual interaction between tutor and tutee, resulting in a more active role of the students (Willis et al., 2012). Same-age peer tutoring (e.g. more skilled students tutoring others at a certain grade level) is distinguished from cross-age peer tutoring, where older students tutor their younger peers (Duran & Top- ping, 2017; Karcher, 2008; Topping et al., 2017). The competence gap should not be too extreme between the tutor and the tutee, suggesting that the optimum age gap in cross-age peer tutoring should not be more than two or three years (Karcher, 2008); in the present case, the age difference was only one year. Although peer tutoring is much less practised and studied at the elementary and secondary levels than in tertiary education (Morrison et al., 2000; Topping et al., 2017; Willis et al., 2012), it has a long history in school coun- selling (Karcher, 2005), and some tutoring programmes have been initiated in primary and secondary education (Karcher, 2008; Morrison et al., 2000). Further, peer tutoring has often focused on transmitting basic skills, such as reading, numeracy, and motoric perfor- mance, and on promoting positive educational attitudes (Topping et al., 2017). Neverthe- less, very little is known about cross-age peer tutoring in the context of STEAM education in general and maker-centred learning in particular; the present investigation aims to fill this research gap.
Many successful cross-age peer tutoring programmes are highly structured and pre- scriptive in nature, in terms of relying on pre-planned learning activities and aiming at pre- specified learning outcomes (Karcher, 2005), in accordance with the basic-skill focus of peer-tutoring pedagogies. In the present case, in contrast, cross-age peer tutoring took place in the context of an open-ended maker-centred learning project based on nonlinear peda- gogy and emergent technology-mediated maker activities, which were novel for the tutees, tutors, and their teachers. Yet, students are often more familiar with the practical aspects of emerging digital technologies than their teachers; this challenges teachers’ traditional authoritative role as the most knowledgeable members of a community and highlights the epistemic value of peer tutors. Although a tutor student may not have adult competences, he/she may function in an expert role (Mieg, 2013; Olson & Bruner, 1996), answering questions and explaining various issues. Peer tutors may be seen as “experts by experi- ence,” who share their cultivated know-how with peers while simultaneously stretching their capabilities and learning novel skills and competences (Willis et al., 2012). Sustained participation in complex problem solving when seeking solutions to open-ended invention challenges encountered in guided maker projects is likely to facilitate the further develop- ment of a tutor student’s knowledge and competence and allow for the sharing of valuable know-how with peers. “Learning by teaching” (Duran & Topping, 2017) makes peer tutor- ing educationally valuable; it requires a significantly deeper level of understanding than mere individual learning. Accordingly, peer tutors are not only challenged by mastering technological aspects of making processes, but also have to learn basic instructional skills involved in teaching their peers and socially organising tutees’ learning activities.
Moreover, peer tutoring entails adopting a new social role in a community and becoming a student expert who is able to provide relevant guidance for younger peers. Investigations by Barron and her colleagues (2009) indicated that students who become exceptionally skilful in using sociodigital technologies often have very strong informal social networks, ranging from supportive parents to extended networks of like-minded people on the internet. To effectively support younger students, peer tutors have to be able to function as a network and share and pool, advance, and apply their heterogeneously distributed knowledge and competences. When peer tutors become aware of their distributed epistemic resources and put deliberate effort into
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employing these resources, the network is likely to provide social capital (Lin, 2002) in terms of mutual trust, proactive peer assistance, and the integration of personal efforts. Hence, effec- tive participation in peer tutoring requires the active building of personal social networks, which provide access to relevant knowledge, tools, and competences beyond a student’s immediate friends (Nardi et al., 2000). As far as one’s own knowledge and competence are fragile, functioning in a tutor’s role is a socio-emotionally (Willis et al., 2012) and existentially (Packer & Goicoechea, 2000) challenging experience; this highlights the importance of social networking support. Further, young people build their sense of capability by having their con- tributions socially recognised by the community (Honneth, 1995). Hence, participation in peer tutoring may not only foster competence development, but also strengthen a sense of belong- ing and of contributing to the community, thereby leading to enhanced self-efficacy (Bandura, 2006; Barron, 2004). Agentic efforts of learning, the building of new skills, and developing tutoring practices are likely to provide some actors with more central network positions than others. In the current study, we define “key tutors” as those who have a cognitively central role in providing advice to their peers and a brokering role in the overall peer-tutoring net- work. Productive participation in prosocial peer assistance and assuming “collective cognitive responsibility” (Scardamalia, 2002) in a joint activity, as well as developing trusted relation- ships with teachers, are also likely to provide a central network position. As far as we know, cross-age peer tutoring has not been formerly studied from the social networking perspective.
Research questions
The present investigation focused on examining cross-age peer tutoring in the context of inte- grative STEAM projects wherein digital fabrication and traditional technologies mediated stu- dents’ collaborative team efforts of inventing artefacts. Students from a technology-focused class tutored peers in the grade below on the topics of coding and robotics. We were interested in how tutors experienced cross-age peer tutoring, what kinds of challenges they encountered while tutoring student teams, and how networks mediated the key tutors’ cognitively central roles among peer tutors. The research questions are as follows: (1) What kinds of skills, motivations, and challenges did students providing cross-age peer tutoring encounter? We were interested in both the personal resources and obstacles encountered by the peer tutors during the nonlinear making process. (2) What kinds of mutual advice networks did peer tutors build to support their efforts of guiding learning-by-making processes? We wanted to examine how the participants shared their knowledge and competences and learned to function as a network. (3) What kinds of personal learning networks did cognitively central “key tutors” have beyond school? Such networks appear to be critical for explaining the expert role they achieved in the tutoring of the maker process.
Methods Participants and setting
The participants of the present investigation were grade 8 students (n= 1 5) from a technol- ogy-focused class who were taking part in tutoring their younger peers in the context of a
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maker-centred STEAM project. The investigation took place in a lower secondary school in the capital region of Finland. The school collaborated with academic investigators and a par- ticipatory teacher training network (link omitted for blind review) to implement a maker pro- ject in grade 7 classes (n = 70) for students aged 13–14 years old. Three of the tutee classes were standard, and one was a technology-focused class for which students were selected through an entrance examination. The project was the first implementation of a 3-year edu- cational design experiment (Collins et al., 2004) aimed at iteratively developing pedagogies of learning by making (see Riikonen et al., 2020 for details). The school had developed some practices in relation to using older students as tutors. By taking part in the present study, the school aimed at creating a more systematic practice of cross-age peer tutoring.
Teams of 7th grade students were engaged in co-inventing complex artefacts by using digi- tal fabrication and traditional technologies in an integrative study project. The co-invention challenge, co-configured between teachers and researchers, was as follows: “Invent a smart product or a smart garment by relying on traditional and digital fabrication technologies or other programmable devices or 3D CAD.” The projects were started in February and involved eight to nine weekly sessions (90–135 min per session) during March, April, and May 2017.
The students worked in co-invention teams throughout the project. As described by Riikonen et al. (2020), Riikonen et al. (2020)), the student teams developed the following co-inventions: (1) a three-wheel bike containing smart technologies, such as an environment responsive, rechargeable LED lighting system; (2) an MGG (mobile gaming grip)—a pair of handles that improves the ergonomics of a mobile phone while playing games; (3) a smart outfit for sports, including an environment-responsive lighting system to improve safety; and (4) a smart insole for sport shoes, including an automatic warming system for winter sports. Most teams devel- oped well-articulated design ideas, produced visualisations and prototypes, and tested and refined their co-inventions.
The present study began in fall 2016 when 6 student tutors were sent to a university to learn about the co-invention projects and start planning the implementation of peer tutoring.
In February 2017, researchers gave all the tutors 2 h of training in using the GoGo Board programming tool. This programming tool is an affordable and multi-faceted digital fabrica- tion instrument based on a visual programming language; it involves a microprocessor and numerous robotic elements, such as sensors and actuators (e.g. electronic engines) (Sipitakiat et al., 2004). The GoGo Board was intended for use in several co-invention projects, and the tutors were encouraged to further explore the instrument. After the training, three student tutors (referred to by using the pseudonyms Joona, Lauri, and Elias) who indicated excep- tional agency were asked to co-plan GoGo Board training for 7th grade students with slides and activating tasks. In February, one training session was organised for each of the four 7th grade classrooms. After the training sessions, the craft teacher invited a few tutors at a time to support the 7th graders with their co-invention projects. During the training sessions, 12 tutors worked in pairs supporting the student teams, while Joona, Lauri, and Elias functioned as organisers of the whole class activity. Four subject teachers representing craft and technol- ogy education, computer science, chemistry, and physics took part in the project at the target school. These teachers and two researchers, whose support was mentioned by students, were included in the present research data.
Methods of data acquisition
The data acquisition involved semi-structured interviews with the 15 peer tutors (Kvale & Brikmann, 2009). The themes related to their tutoring experiences, personal interests,
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and experiences of developing sociodigital competences to help answer the first research question. To answer the second question, egocentric network interviews (Crossley et al., 2015; McCarty et al., 2019) were performed with the name generator method. Each tutor was asked to put his/her name (ego) in the middle of a large sheet of paper and, around his/ her name, indicate those networking partners (alters, i.e. other tutors, teachers, researchers) from whom they obtained assistance during the peer tutoring process. The third research question, in turn, was answered by asking each tutor to complement his/her personal net- work drawing with various kinds of knowledge, support, and resources that flowed across family, schoolmates, friends outside of school, and hobbies to support the development of digital competences. Although such data gathering involved all the tutors, we will only present two case studies in relation to the cognitively central key tutors. The interview and egocentric network data were collected after piloting in fall 2017. At the time of the inter- views, the student tutors had delivered their training sessions to the 7th graders and had started supporting the co-invention project along with teachers. Table 1 presents a sum- mary of the research data.
Methods of data analysis
The interviews addressed multi-faceted themes; for the purposes of the present study, those aspects of the interviews that were relevant for answering the present research ques- tions were identified for qualitative analysis. The first research question on tutors’ skills, motivations, and challenges was analysed through the qualitative analysis of the interview data (Saldaña, 2015) using Atlas.ti and by relying on a theory informed and data-driven approach. Guided by recent theories of learning and (student) expertise (Bransford et al., 2006; Hakkarainen et al., 2004; Markauskaite & Goodyear, 2017), we focused on the inter- view data for identifying text segments wherein students’ knowledge, competences, and agency involved in overcoming challenges were mentioned. The units of analysis consisted of thematically defined meaningful ideas (the smallest coherent meaningful idea), which varied from one to several sentences in length. During the first stage of analysis, we identi- fied two main themes occurring in the tutors’ interview talk: tutors’ skills and motivation as superordinate categories. In the second stage, we clustered the identified text segments under the superordinate categories. The competences required by the tutoring experience were categorised into technological skills, teaching skills, social competences, self-regula- tive skills, and reflective skills; these skills were revealed as tutors talked about overcoming the various challenges encountered. Such expressions were included, for instance, in the application of technology and group management. An example of categorising the text seg- ments with data excerpts relating to tutors’ skills is presented in Table 2. The superordinate theme of motivation involved expressions in which tutors talked about their motivating and inspiring experiences with regard to tutoring. Under this subordinate theme, we identified several factors that fostered motivation and commitment, such as interest, social contribu- tion, and peer activity. Interest was further subdivided into technology and teaching inter- ests. Social contribution indicates a participant’s pride in his/her independent role while being involved in the experience of helping others as separate from teacher guidance.
To answer the second research question, we examined tutors’ advice networks by summarising the mentions that he/she had received in the egocentric network inter- views regarding advice provided during the peer tutoring process; the teachers were also mentioned as having offered advice. In social networking terms, the advice size (Hakkarainen et al., 2004) represents Freeman’s in-degree value of the advice network
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(the number of links coming from community members who acknowledge receiving pieces of advice from participants). Consequently, it is not based on self-reporting but instead the social recognition of the whole community regarding a person’s role in pro- viding valuable advice. Hence, it provides a socially validated measure of a tutor’s cog- nitive centrality in the peer tutoring process. We created an Excel matrix regarding in- degrees of the provided advice network and used the CytoScape program (https:// cytos cape. org/) to construct a network map of the tutor network.
This stays in the method section of the third research question, we examined two key tutors’ egocentric networks, in which Lauri acted more as a technology expert and Joona occupied more of a social organiser role. They had exceptionally large advice sizes as well as important network brokerage roles in the peer tutoring process. The interview data assisted in examining their networking activities as well as the ecolo- gies of sociodigital learning extending beyond the school. Both the semi-structured
Table 1  A summary of the participants and the research data
a  denotes the advice size Advice size indicates the number of students who reported asking for advice from the given participant; advice size socially validated the key tutoring role Teachers and researchers did not construct an egocentric network map, so their advice size was determined by relying on in-degree values based on peer tutors’ nominations
Name Gender Advice  sizea Role in tutoring Length of inter- view (min.) Length of interview (words) Aulis Male 0 Tutor 37 4605 Elias Male 5 Key tutor 52 6807 Janne Male 0 Tutor 43 4547 Joona Male 10 Key tutor 43 6320 Juuso Male 0 Tutor 37 2766 Lauri Male 11 Key tutor 45 4286 Leo Male 0 Tutor 29 2757 Luka Male 1 Tutor 34 3867 Mikko Male 0 Tutor 36 3809 Otto Male 1 Tutor 46 6274 Petri Male 0 Tutor 40 3071 Sami Male 0 Tutor 34 3701 Santeri Male 1 Tutor 26 3796 Katri Female 1 Tutor 36 3922 Minna Female 1 Tutor 38 5211 Paula Female 5 Computer Science teacher – – Leila Female 5 Craft teacher – – Jouko Male 3 Craft teacher – – Hannu Male 2 Physics teacher – – Seija Female 2 Researcher – – Karita Female 1 Researcher – –
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Table 2  An example of the qualitative categorisation of the data Theme Analytic category Superordinate category Subordinate category Data excerpts Competence Skills Digital skills Basic use “Well, what I learned was that, because there were all kinds of different sensors in the pack- age, I learned most about their different uses.” (Lauri) Application “And it’s just an application that you have to … even if you know how to do one thing, then the same skills could like … they should be used to create something else …” (Aulis) Teaching Explaining “… You have to try to make it clear. So that everyone understands it and so on.” (Minna) Motivating “There was a couple of groups of two or three who refused to do these things—if you didn’t pressure them to do them, or motivate them, then they didn’t do anything.” (Joona) Social skills Collaborative skills “Well it was a little bit like you had to get to know yourself alongside the seventh graders.
So that if you [as a tutor] tended to be shy, then you needed, anyway, to introduce your- selves to them [tutees], and then start giving them directions.” (Minna) Group management “Perhaps more like directing and guiding these students. … I didn’t realise I was capable of directing such a big crowd.” (Aulis) Self-regulative skills Taking responsibility “Well, it required quite a lot of precision; you have to focus on that one thing. You can’t do something else at the same time … well, precision and focus.” (Katri) Self-control “In fact, it made me think about pulling them all out of the class and … So that was the kind of situation where we … we had a four-hour teaching session, with two classes … Well, it was two hours [with one class] and then two hours [with the other]. The latter was really, it was a kind of chaos classroom. At that stage, I had the urge to just push them all out of there. It felt like nothing would come out of all that chaos.” (Joona) Reflective skills – “… We wondered how the session should go and what we should show, in what order. And after that, usually after the session, we discussed with Joona how the session went and what I could have done better. There were conversations … of what we had learned in the last session, and it always improved a little.” (Lauri)
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interviews and egocentric network data helped create rich descriptions of the key tutors’ learning networks.
Peer tutors’ experienced competences and motivation
The first research question focused on the competences and motivation that peer tutors experienced as necessary for overcoming challenges encountered in the tutoring process.
In the present project, the tutors’ role was, first, to help tutees learn to productively uti- lise the GoGo Board in their co-invention projects. Second, some of the tutors were also later asked to guide the tutees in their co-invention projects by helping them with problem solving, troubleshooting, and further developing their ideas. Nevertheless, the tutors only received a two-hour training session themselves, which, reportedly, made their work very challenging. Despite this, the tutors described many skills that were learned and compe- tences that were cultivated to overcome these challenges. These skills were categorised as technical skills, teaching skills, social skills, self-regulatory skills, and reflective skills.
One of the challenges experienced by tutors was uncertainty and confusion concerning the student teams’ co-invention projects. The tutors were expected to guide the projects by not only providing tutees with technical guidance but also motivating them to start their first trials with the GoGo Board. Many of the tutors described their own trials of learning more with regard to using the tool; some felt frustrated because their technical compe- tences were inadequate. Tutors talked about both basic skills in terms of understanding how the GoGo Board functions and advanced skills related to integrating several functions to design something that works with the GoGo Board. The tutors also appeared to have a realistic view of their own skill levels and knew whom in the tutor group they could ask for help. However, the varying skill levels within the tutor group sometimes led to
Fig. 1  Advice network of the present peer tutoring project. The network map includes tutors (T), teachers (O), and researchers (C).
The arrows represent the rela- tionships between actors related to giving and receiving help (the direction of an arrow goes from the advisor to the person asking for advice). These relations were either one-directional or bidirec- tional. When interpreting the fig- ure, the fact that information was only collected from tutors (who mentioned teachers and research- ers as providers of help) should be taken into consideration
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pressure being put on the more skilled tutors, who were rushed while assisting the many tutee groups. Further, the tutors experienced the need to have broader peer teaching skills, such as (a) abilities in terms of explaining how the GoGo Board works and (b) the capabil- ity to motivate the tutees to persist until any problems were solved.
And then just encourage, so like you can do it, maybe it will become something … sometimes it doesn’t work, in which case you have to keep encouraging so that they keep trying and don’t give up. (Aulis).
The tutors worked as a group and, therefore, needed to cooperate with their fellow tutors. They also had to find ways to get to know their tutees because they did not yet know them personally. The tutors had to cooperate with different personalities and ways of work- ing. Further, some of the tutors described the social challenges of tutoring and “leading” a co-invention group. While teaching, the tutors had the freedom to establish the activities and behavioural boundaries of situations; this was often challenging because the tutees did not want to obey fellow students. Some tutors were positively surprised that they managed to lead a group. Joona, however, described the stress of leading a tutor group where some of the tutees were disruptive or did not want to put their mobile phones away.
At one point, we had to threaten as a big group that we would be moving some of them to Leila’s [the teacher’s] class. We were thus able to get them to calm down, which was great. (Joona).
Some of the tutors described self-regulatory skills, which were clustered and named as taking responsibility and self-control. Elias, for example, described the importance of concentration and responsibility in allowing him to complete all his tasks. Self-control was necessary in situations that aroused emotions like frustration, and Petri, for example, described his way of solving a problem as “just walking away from the situation.” Craft teacher Leila encouraged tutors to reflect on their experiences by writing notes after tutor- ing sessions. In these reflections, the tutors went through the teaching sessions, developed training tasks and evaluated them, and considered new ways of using the GoGo Board. In the following excerpt, Janne described a situation wherein he realised that their planned exercise was not working in a real-life situation: We had had discussions about whether we could hold teaching moments, where we would personally teach them… for example, by giving them a mystery to solve, like how to get an instrument to work, and then put a timer on the screen with gradually accelerating beeping. So, at one point, we had a big teaching session with two classes of seventh graders and our own class so that … there just were not enough people to guide everyone … We also tried to keep some kind of guessing session. (Janne).
The data revealed that tutors who were interested and skilful in using technology also appeared to find tutoring interesting. Experience of gaming or programming was also described as a good basis for learning to use and understand the functions and possibilities of the GoGo Board. Those who lacked technological skill, but were interested in teach- ing, had more motivational challenges. However, almost all the tutors could name at least one positive, motivational, or rewarding experience regarding tutoring. Some of the tutors even shared that they became more interested in teaching during tutoring. Some tutors also had previous experiences of teaching others, and Janne, in turn, had an existing interest in a teaching career before tutoring. However, Joona mentioned that the challenging experi- ences of trying to make tutee groups work made him prefer teaching older age groups.

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