The nature of communication structures of university students in an international web-based computer-supported collaborative learning (CSCL) course

Communication, the flow of ideas and information between individuals in a social context, is the heart of educational experience. Constructivism and constructivist theories form the foundation for the collaborative learning processes of creating and sharing meaning in online educational contexts. The Learning and Collaboration in Technology-enhanced Contexts (LeCoTec) course comprised of 66 participants drawn from four European universities (Oulu, Turku, Ghent and Ramon Llull). These participants were split into 15 groups with the express aim of learning about computer-supported collaborative learning (CSCL). The Community of Inquiry model (social, cognitive and teaching presences) provided the content and tools for learning and researching the collaborative interactions in this environment. The sampled comments from the collaborative phase were collected and analyzed at chain-level and group-level, with the aim of identifying the various message types that sustained high learning outcomes. Furthermore, the Social Network Analysis helped to view the density of whole group interactions, as well as the popular and active members within the highly collaborating groups. It was observed that long chains occur in groups having high quality outcomes. These chains were also characterized by Social, Interactivity, Administrative and Content comment-types. In addition, high outcomes were realized from the high interactive cases and high-density groups. In low interactive groups, commenting patterned around the one or two central group members. In conclusion, future online environments should support high-order learning and develop greater metacognition and self-regulation. Moreover, such an environment, with a wide variety of problem solving tools, would enhance interactivity.

Emotions in a Web-based Learning Environment

The aim of this thesis was to examine emotions in a web-based learning environment (WBLE). Theoretically, the thesis was grounded on the dimensional model of emotions. Four empirical studies were conducted. Study I focused on students’ anxiety and their self-efficacy in computer-using situations. Studies II and III examined the influence of experienced emotions on students’ collaborative visible and non-collaborative invisible activities and lurking in a WBLE. Study II also focused on the antecedents of the emotions students experience in a web-based learning environment. Study IV concentrated on clarifying the differences between emotions experienced in face-to-face and web-based collaborative learning. The results of these studies are reported in four original research articles published in scientific journals. The present studies demonstrate that emotions are important determinants of student behaviour in a web-based learning, and justify the conclusion that interactions on the web can and do have an emotional content. Based on the results of these empirical studies, it can be concluded that the emotions students experience during the web-based learning result mostly from the social interactions rather than from the technological context. The studies indicate that the technology itself is not the only antecedent of students’ emotional reactions in the collaborative web-based learning situations. However, the technology itself also exerted an influence on students’ behaviour. It was found that students’ computer anxiety was associated with their negative expectations of the consequences of using technology-based learning environments in their studies. Moreover, the results also indicated that student behaviours in a WBLE can be divided into three partially overlapping classes: i) collaborative visible ii) non-collaborative invisible activities, and iii) lurking. What is more, students’ emotions experienced during the web-based learning affected how actively they participated in such activities in the environment. Especially lurkers, i.e. students who seldom participated in discussions but frequently visited the online environment, experienced more negatively valenced emotions during the courses than did the other students. This result indicates that such negatively toned emotional experiences can make the lurking individuals less eager to participate in other WBLE courses in the future. Therefore, future research should also focus more precisely on the reasons that cause individuals to lurk in online learning groups, and the development of learning tasks that do not encourage or permit lurking or inactivity. Finally, the results from the study comparing emotional reactions in web-based and face-to-face collaborative learning indicated that the learning by means of web-based communication resulted in more affective reactivity when compared to learning in a face-to-face situation. The results imply that the students in the web-based learning group experienced more intense emotions than the students in the face-to-face learning group.The interpretations of this result are that the lack of means for expressing emotional reactions and perceiving others’ emotions increased the affectivity in the web-based learning groups. Such increased affective reactivity could, for example, debilitate individual’s learning performance, especially in complex learning tasks. Therefore, it is recommended that in the future more studies should be focused on the possibilities to express emotions in a text-based web environment to ensure better means for communicating emotions, and subsequently, possibly decrease the high level of affectivity. However, we do not yet know whether the use of means for communicating emotional expressions via the web (for example, “smileys” or “emoticons”) would be beneficial or disadvantageous in formal learning situations. Therefore, future studies should also focus on assessing how the use of such symbols as a means for expressing emotions in a text-based web environment would affect students’ and teachers’ behaviour and emotional state in web-based learning environments.

Promoting Programming Learning. Engagement, Automatic Assessment with Immediate Feedback in Visualizations

The skill of programming is a key asset for every computer science student. Many studies have shown that this is a hard skill to learn and the outcomes of programming courses have often been substandard. Thus, a range of methods and tools have been developed to assist students’ learning processes. One of the biggest fields in computer science education is the use of visualizations as a learning aid and many visualization based tools have been developed to aid the learning process during last few decades. Studies conducted in this thesis focus on two different visualizationbased tools TRAKLA2 and ViLLE. This thesis includes results from multiple empirical studies about what kind of effects the introduction and usage of these tools have on students’ opinions and performance, and what kind of implications there are from a teacher’s point of view. The results from studies in this thesis show that students preferred to do web-based exercises, and felt that those exercises contributed to their learning. The usage of the tool motivated students to work harder during their course, which was shown in overall course performance and drop-out statistics. We have also shown that visualization-based tools can be used to enhance the learning process, and one of the key factors is the higher and active level of engagement (see. Engagement Taxonomy by Naps et al., 2002). The automatic grading accompanied with immediate feedback helps students to overcome obstacles during the learning process, and to grasp the key element in the learning task. These kinds of tools can help us to cope with the fact that many programming courses are overcrowded with limited teaching resources. These tools allows us to tackle this problem by utilizing automatic assessment in exercises that are most suitable to be done in the web (like tracing and simulation) since its supports students’ independent learning regardless of time and place. In summary, we can use our course’s resources more efficiently to increase the quality of the learning experience of the students and the teaching experience of the teacher, and even increase performance of the students. There are also methodological results from this thesis which contribute to developing insight into the conduct of empirical evaluations of new tools or techniques. When we evaluate a new tool, especially one accompanied with visualization, we need to give a proper introduction to it and to the graphical notation used by tool. The standard procedure should also include capturing the screen with audio to confirm that the participants of the experiment are doing what they are supposed to do. By taken such measures in the study of the learning impact of visualization support for learning, we can avoid drawing false conclusion from our experiments. As computer science educators, we face two important challenges. Firstly, we need to start to deliver the message in our own institution and all over the world about the new – scientifically proven – innovations in teaching like TRAKLA2 and ViLLE. Secondly, we have the relevant experience of conducting teaching related experiment, and thus we can support our colleagues to learn essential know-how of the research based improvement of their teaching. This change can transform academic teaching into publications and by utilizing this approach we can significantly increase the adoption of the new tools and techniques, and overall increase the knowledge of best-practices. In future, we need to combine our forces and tackle these universal and common problems together by creating multi-national and multiinstitutional research projects. We need to create a community and a platform in which we can share these best practices and at the same time conduct multi-national research projects easily.

Focusing on the quality of learning processes

S. 327-333

Clinical Learning Environment and Supervision. Development and Validation of the CLES Evaluation Scale

The purpose of the study is: (1) to describe how nursing students' experienced their clinical learning environment and the supervision given by staff nurses working in hospital settings; and (2) to develop and test an evaluation scale of Clinical Learning Environment and Supervision (CLES). The study has been carried out in different phases. The pilot study (n=163) explored the association between the characteristics of a ward and its evaluation as a learning environment by students. The second version of research instrument (which was developed by the results of this pilot study) were tested by an expert panel (n=9 nurse teachers) and test-retest group formed by student nurses (n=38). After this evaluative phase, the CLES was formed as the basic research instrument for this study and it was tested with the Finnish main sample (n=416). In this phase, a concurrent validity instrument (Dunn & Burnett 1995) was used to confirm the validation process of CLES. The international comparative study was made by comparing the Finnish main sample with a British sample (n=142). The international comparative study was necessary for two reasons. In the instrument developing process, there is a need to test the new instrument in some other nursing culture. Other reason for comparative international study is the reflecting the impact of open employment markets in the European Union (EU) on the need to evaluate and to integrate EU health care educational systems. The results showed that the individualised supervision system is the most used supervision model and the supervisory relationship with personal mentor is the most meaningful single element of supervision evaluated by nursing students. The ward atmosphere and the management style of ward manager are the most important environmental factors of the clinical ward. The study integrates two theoretical elements - learning environment and supervision - in developing a preliminary theoretical model. The comparative international study showed that, Finnish students were more satisfied and evaluated their clinical placements and supervision with higher scores than students in the United Kingdom (UK). The difference between groups was statistical highly significant (p= 0.000). In the UK, clinical placements were longer but students met their nurse teachers less frequently than students in Finland. Arrangements for supervision were similar. This research process has produced the evaluation scale (CLES), which can be used in research and quality assessments of clinical learning environment and supervision in Finland and in the UK. CLES consists of 27 items and it is sub-divided into five sub-dimensions. Cronbach's alpha coefficient varied from high 0.94 to marginal 0.73. CLES is a compact evaluation scale and user-friendliness makes it suitable for continuing evaluation.

Blended learning and student time-use in engineering education

Aiheen laajempi artikkeli on julkaistu konferenssi-CD:llä.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Knowledge Sharing in a Virtual Learning Community

The focus of this Master’s Thesis is on knowledge sharing in a virtual Learning community. The theoretical part of this study aims at presenting the theory of knowledge sharing, competence development and learning in virtual teams. The features of successful learning organizations as well as enablers of effective knowledge sharing in virtual communities are also introduced to the reader in the theoretical framework. The empirical research for this study was realized in a global ICT company, specifically in its Human Resources business unit. The research consisted of two rounds of online questionnaires, which were conducted among all the members of the virtual Learning community. The research aim was to find shared opinions concerning the features of a successful virtual Learning community. The analysis of the data in this study was conducted using a qualitative research methodology. The empirical research showed that the main important features of a successful virtual Learning community are members’ passion towards the community way of working as well as the relevance of the content in the virtual community. In general, it was found that knowledge sharing and competence development are important matters in dynamic organizations as well as virtual communities as method and tool for sharing knowledge and hence increasing both individual and organizational knowledge. This is proved by theoretical and by empirical research in this study.

Towards adoption of eLearning technologies at Lappeenranta University of Technology: Understanding Teachers‟ Adoption of Blackboard Learning System

The electronic learning has become crucial in higher education with increased usage of learning management systems as a key source of integration on distance learning. The objective of this study is to understand how university teachers are influenced to use and adopt web-based learning management systems. Blackboard, as one of the systems used internationally by various universities is applied as a case. Semi-structured interviews were made with professors and lecturers who are using Blackboard at Lappeenranta University of Technology. The data collected were categorized under constructs adapted from Unified Theory of Acceptance and Use of Technology (UTAUT) and interpretation and discussion were based on reviewed literature. The findings suggest that adoption of learning management systems by LUT teachers is highly influenced by perceived usefulness, facilitating conditions and gained experience. The findings also suggest that easiness of using the system and social influence appear as medium influence of adoption for teachers at LUT.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Integrating Student Learning into the Repository Ecosystem

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Employees’ continuous learning and job satisfaction - effects on productivity

In today’s knowledge intense economy the human capital is a source for competitive advantage for organizations. Continuous learning and sharing the knowledge within the organization are important to enhance and utilize this human capital in order to maximize the productivity. The new generation with different views and expectations of work is coming to work life giving its own characteristics on learning and sharing. Work should offer satisfaction so that the new generation employees would commit to organizations. At the same time organizations have to be able to focus on productivity to survive in the competitive market. The objective of this thesis is to construct a theory based framework of productivity, continuous learning and job satisfaction and further examine this framework and its applications in a global organization operating in process industry. Suggestions for future actions are presented for this case organization. The research is a qualitative case study and the empiric material was gathered by personal interviews concluding 15 employee and one supervisor interview. Results showed that more face to face interaction is needed between employees for learning because much of the knowledge of the process is tacit and so difficult to share in other ways. Offering these sharing possibilities can also impact positively to job satisfaction because they will increase the sense of community among employees which was found to be lacking. New employees demand more feedback to improve their learning and confidence. According to the literature continuous learning and job satisfaction have a relative strong relationship on productivity. The employee’s job description in the case organization has moved towards knowledge work due to continuous automation and expansion of the production process. This emphasizes the importance of continuous learning and means that productivity can be seen also from quality perspective. The normal productivity output in the case organization is stable and by focusing on the quality of work by improving continuous learning and job satisfaction the upsets in production can be handled and prevented more effectively. Continuous learning increases also the free human capital input and utilization of it and this can breed output increasing innovations that can increase productivity in long term. Also job satisfaction can increase productivity output in the end because employees will work more efficiently, not doing only the minimum tasks required. Satisfied employees are also found participating more in learning activities.

Informal learning and competence development during a management trainee program

The purpose of this research was to study how management trainee program participants experienced the program with respect to their learning and competence development. Additionally, the purpose was also to examine what the trainees learned and how the learning occurred. Furthermore, factors affecting learning in the workplace were examined. The theoretical framework of this research was formed utilizing individual competence and informal learning frameworks. Research was conducted as a single case study and data was gathered by thematic interviews. The results of this research indicate that the trainees experienced the program as a good method for learning the overall picture of the organization and its business. Regarding competence development, especially knowledge- and cognitive competence categories were developed during the program. The best learning outcomes were achieved through learning by doing, in co-operation with others, and learning from others. The results indicate that the planning of the program and its structure have a significant effect on learning. Furthermore, a sufficient level of challenge was experienced as being important for the quality of the learning as well.

Socially shared metacognitive regulation during collaborative learning processes in student dyads and small groups

Traditionally metacognition has been theorised, methodologically studied and empirically tested from the standpoint mainly of individuals and their learning contexts. In this dissertation the emergence of metacognition is analysed more broadly. The aim of the dissertation was to explore socially shared metacognitive regulation (SSMR) as part of collaborative learning processes taking place in student dyads and small learning groups. The specific aims were to extend the concept of individual metacognition to SSMR, to develop methods to capture and analyse SSMR and to validate the usefulness of the concept of SSMR in two different learning contexts; in face-to-face student dyads solving mathematical word problems and also in small groups taking part in inquiry-based science learning in an asynchronous computer-supported collaborative learning (CSCL) environment. This dissertation is comprised of four studies. In Study I, the main aim was to explore if and how metacognition emerges during problem solving in student dyads and then to develop a method for analysing the social level of awareness, monitoring, and regulatory processes emerging during the problem solving. Two dyads comprised of 10-year-old students who were high-achieving especially in mathematical word problem solving and reading comprehension were involved in the study. An in-depth case analysis was conducted. Data consisted of over 16 (30–45 minutes) videotaped and transcribed face-to-face sessions. The dyads solved altogether 151 mathematical word problems of different difficulty levels in a game-format learning environment. The interaction flowchart was used in the analysis to uncover socially shared metacognition. Interviews (also stimulated recall interviews) were conducted in order to obtain further information about socially shared metacognition. The findings showed the emergence of metacognition in a collaborative learning context in a way that cannot solely be explained by individual conception. The concept of socially-shared metacognition (SSMR) was proposed. The results highlighted the emergence of socially shared metacognition specifically in problems where dyads encountered challenges. Small verbal and nonverbal signals between students also triggered the emergence of socially shared metacognition. Additionally, one dyad implemented a system whereby they shared metacognitive regulation based on their strengths in learning. Overall, the findings suggested that in order to discover patterns of socially shared metacognition, it is important to investigate metacognition over time. However, it was concluded that more research on socially shared metacognition, from larger data sets, is needed. These findings formed the basis of the second study. In Study II, the specific aim was to investigate whether socially shared metacognition can be reliably identified from a large dataset of collaborative face-to-face mathematical word problem solving sessions by student dyads. We specifically examined different difficulty levels of tasks as well as the function and focus of socially shared metacognition. Furthermore, the presence of observable metacognitive experiences at the beginning of socially shared metacognition was explored. Four dyads participated in the study. Each dyad was comprised of high-achieving 10-year-old students, ranked in the top 11% of their fourth grade peers (n=393). Dyads were from the same data set as in Study I. The dyads worked face-to-face in a computer-supported, game-format learning environment. Problem-solving processes for 251 tasks at three difficulty levels taking place during 56 (30–45 minutes) lessons were video-taped and analysed. Baseline data for this study were 14 675 turns of transcribed verbal and nonverbal behaviours observed in four study dyads. The micro-level analysis illustrated how participants moved between different channels of communication (individual and interpersonal). The unit of analysis was a set of turns, referred to as an ‘episode’. The results indicated that socially shared metacognition and its function and focus, as well as the appearance of metacognitive experiences can be defined in a reliable way from a larger data set by independent coders. A comparison of the different difficulty levels of the problems suggested that in order to trigger socially shared metacognition in small groups, the problems should be more difficult, as opposed to moderately difficult or easy. Although socially shared metacognition was found in collaborative face-to-face problem solving among high-achieving student dyads, more research is needed in different contexts. This consideration created the basis of the research on socially shared metacognition in Studies III and IV. In Study III, the aim was to expand the research on SSMR from face-to-face mathematical problem solving in student dyads to inquiry-based science learning among small groups in an asynchronous computer-supported collaborative learning (CSCL) environment. The specific aims were to investigate SSMR’s evolvement and functions in a CSCL environment and to explore how SSMR emerges at different phases of the inquiry process. Finally, individual student participation in SSMR during the process was studied. An in-depth explanatory case study of one small group of four girls aged 12 years was carried out. The girls attended a class that has an entrance examination and conducts a language-enriched curriculum. The small group solved complex science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry during 22 lessons (á 45–minute). Students’ network discussion were recorded in written notes (N=640) which were used as study data. A set of notes, referred to here as a ‘thread’, was used as the unit of analysis. The inter-coder agreement was regarded as substantial. The results indicated that SSMR emerges in a small group’s asynchronous CSCL inquiry process in the science domain. Hence, the results of Study III were in line with the previous Study I and Study II and revealed that metacognition cannot be reduced to the individual level alone. The findings also confirm that SSMR should be examined as a process, since SSMR can evolve during different phases and that different SSMR threads overlapped and intertwined. Although the classification of SSMR’s functions was applicable in the context of CSCL in a small group, the dominant function was different in the asynchronous CSCL inquiry in the small group in a science activity than in mathematical word problem solving among student dyads (Study II). Further, the use of different analytical methods provided complementary findings about students’ participation in SSMR. The findings suggest that it is not enough to code just a single written note or simply to examine who has the largest number of notes in the SSMR thread but also to examine the connections between the notes. As the findings of the present study are based on an in-depth analysis of a single small group, further cases were examined in Study IV, as well as looking at the SSMR’s focus, which was also studied in a face-to-face context. In Study IV, the general aim was to investigate the emergence of SSMR with a larger data set from an asynchronous CSCL inquiry process in small student groups carrying out science activities. The specific aims were to study the emergence of SSMR in the different phases of the process, students’ participation in SSMR, and the relation of SSMR’s focus to the quality of outcomes, which was not explored in previous studies. The participants were 12-year-old students from the same class as in Study III. Five small groups consisting of four students and one of five students (N=25) were involved in the study. The small groups solved ill-defined science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry over a total period of 22 hours. Written notes (N=4088) detailed the network discussions of the small groups and these constituted the study data. With these notes, SSMR threads were explored. As in Study III, the thread was used as the unit of analysis. In total, 332 notes were classified as forming 41 SSMR threads. Inter-coder agreement was assessed by three coders in the different phases of the analysis and found to be reliable. Multiple methods of analysis were used. Results showed that SSMR emerged in all the asynchronous CSCL inquiry processes in the small groups. However, the findings did not reveal any significantly changing trend in the emergence of SSMR during the process. As a main trend, the number of notes included in SSMR threads differed significantly in different phases of the process and small groups differed from each other. Although student participation was seen as highly dispersed between the students, there were differences between students and small groups. Furthermore, the findings indicated that the amount of SSMR during the process or participation structure did not explain the differences in the quality of outcomes for the groups. Rather, when SSMRs were focused on understanding and procedural matters, it was associated with achieving high quality learning outcomes. In turn, when SSMRs were focused on incidental and procedural matters, it was associated with low level learning outcomes. Hence, the findings imply that the focus of any emerging SSMR is crucial to the quality of the learning outcomes. Moreover, the findings encourage the use of multiple research methods for studying SSMR. In total, the four studies convincingly indicate that a phenomenon of socially shared metacognitive regulation also exists. This means that it was possible to define the concept of SSMR theoretically, to investigate it methodologically and to validate it empirically in two different learning contexts across dyads and small groups. In-depth micro-level case analysis in Studies I and III showed the possibility to capture and analyse in detail SSMR during the collaborative process, while in Studies II and IV, the analysis validated the emergence of SSMR in larger data sets. Hence, validation was tested both between two environments and within the same environments with further cases. As a part of this dissertation, SSMR’s detailed functions and foci were revealed. Moreover, the findings showed the important role of observable metacognitive experiences as the starting point of SSMRs. It was apparent that problems dealt with by the groups should be rather difficult if SSMR is to be made clearly visible. Further, individual students’ participation was found to differ between students and groups. The multiple research methods employed revealed supplementary findings regarding SSMR. Finally, when SSMR was focused on understanding and procedural matters, this was seen to lead to higher quality learning outcomes. Socially shared metacognition regulation should therefore be taken into consideration in students’ collaborative learning at school similarly to how an individual’s metacognition is taken into account in individual learning.