How Individual self-regulation affects group regulation and performance: A shared regulation intervention

This study explored the relationship between individual self-regulated learning (SRL), socially shared regulation of learning (SSRL), and group performance plus the effect of an intervention promoting SSRL. We hypothesized that SRL would influence SSRL and group performance as groups with high SRL students will be better regulated and that the intervention would promote SSRL over time. The results revealed a significant relationship between SRL and SSRL, but no significant effects of the intervention on group performance. The limitations of the intervention are discussed and form the basis for future design of environments to promote SSRL. The main conclusion is that SRL is an important predictor of SSRL and should be considered when designing small group activities and their environments

Efforts in Europe for data-driven improvement of education : A review of learning analytics research in seven countries

Information and communication technologies are increasingly mediating learning and teaching practices as well as how educational institutions are handling their administrative work. As such, students and teachers are leaving large amounts of digital footprints and traces in various educational apps and learning management platforms, and educational administrators register various processes and outcomes in digital administrative systems. It is against such a background we in recent years have seen the emergence of the fast-growing and multi-disciplinary field of learning analytics. In this paper, we examine the research efforts that have been conducted in the field of learning analytics in Austria, Denmark, Finland, Norway, Germany, Spain, and Sweden. More specifically, we report on developed national policies, infrastructures and competence centers, as well as major research projects and developed research strands within the selected countries. The main conclusions of this paper are that the work of researchers around Europe has not led to national adoption or European level strategies for learning analytics. Furthermore, most countries have not established national policies for learners’ data or guidelines that govern the ethical usage of data in research or education. We also conclude, that learning analytics research on pre-university level to high extent have been overlooked. In the same vein, learning analytics has not received enough focus form national and European national bodies. Such funding is necessary for taking steps towards data-driven development of education

Tracing the process of self-regulated learning – students’ strategic activity in g/nStudy learning environment

Abstract This study focuses on the process of self-regulated learning by investigating in detail how learners engage in self-regulated and strategic learning when studying in g/nStudy learning environments. The study uses trace methods to enable recognition of temporal patterns in learners’ activity that can signal strategic and self-regulated learning. The study comprises three data sets. In each data set, g/nStudy technology was used to support and trace self-regulated learning. In the analysis, micro-analytical protocols along with qualitative approach were favoured to better understand the process of self-regulated and strategic learning in authentic classroom settings. The results suggested that the specific technological tools used to support strategic and self-regulated learning can also be used methodologically to investigate patterns emerging from students’ cognitive regulation activity. The advantage of designing specific tools to trace and support self-regulated learning also helps to interpret the way in which the learning patterns actually inform SRL theoretically and empirically. Depending on how the tools are used, they can signal the typical patterns existing in the learning processes of students or student groups. The learning patterns found in the students’ cognitive regulation activity varied in terms of how often the patterns emerged in their learning, how the patterns were composed and when the patterns were used. Moreover, there were intra-individual differences – firstly, in how students with different learning outcomes allocated their study tactic use, and secondly, how self-regulated learning was used in challenging learning situations perceived by students. These findings indicate log file traces can reveal differences in self-regulated learning between individuals and between groups of learners with similar characteristics based on the learning patterns they used. However, learning patterns obtained from log file traces can sometimes be complex rather than simple. Therefore, log file traces need to be combined with other situation-specific measurements to better understand how they might elucidate self-regulated learning in the learning context.Tiivistelmä Tässä väitöskirjassa tutkitaan oppilaiden itsesäätöisen ja strategisen oppimisen ilmenemistä oppimisprosessin aikana. Tutkimuksessa hyödynnetään g/nStudy- oppimisympäristöä, jonka avulla on mahdollista tukea ja jäljittää oppimisen strategista toimintaa. g/nStudy-oppimisympäristö tallentaa lokidataa, joka on tarkkaa ajallista informaatiota siitä toiminnasta, jota oppilas tekee työskentelynsä aikana. Toisin sanoen, lokidatasta on mahdollista jäljittää ne tiedot, jotka reflektoivat strategista – ja itsesäätöistä oppimista. Erityisenä mielenkiinnon kohteena oli selvittää miten lokidatasta voi löytää strategisia oppimisen toimintamalleja, ja miten nämä strategiset oppimisen toimintamallit vaihtelevat oppilaiden, oppilasryhmien ja erilaisten oppimisen tilanteiden aikana. Väitöstutkimus muodostuu kolmesta erillisestä tutkimusaineistosta. Jokaisessa kolmessa aineistossa on hyödynnetty g/nStudy-teknologian mahdollisuuksia tukea ja jäljittää itsesäätöistä oppimista. Tutkimusaineiston analyysissä hyödynnetään mikroanalyyttista lähestymistapaa sekä laadullista tutkimusotetta. Tutkimuksen analyyttinen lähestymistapa antaa mahdollisuuden ymmärtää itsesäätöisen- ja strategisen oppimisen ilmenemistä aidossa oppimistilanteessa. Tutkimustulokset osoittavat, että oppimisympäristöön sisällytettyjä teknologisia työkaluja voidaan käyttää tukemaan itsesäätöistä ja strategista toimintaa. Sen lisäksi samoja työkaluja voidaan käyttää myös menetelmällisenä välineenä tutkittaessa itsesäätöistä – ja strategista toimintaa erilaisissa oppimistilanteissa. Tutkimus -tulokset osoittavat, että oppimisen strategiset toimintamallit vaihtelivat oppilaiden – ja oppimistilanteiden välillä. Oppimisen strategisissa toimintamalleissa oli myös laadullisia eroja sen suhteen, miten usein ne ilmenivät oppimisprosessin aikana ja mistä strategisista toiminnoista ne koostuivat. Johtopäätöksenä voi todeta, että lokidatan käyttäminen tutkimusmenetelmänä edesauttaa paljastamaan opiskelun strategisia toimintamalleja oppilaiden – ja oppilasryhmien välillä. Tutkimuksen perusteella voidaan todeta, että strategiset toimintamallit voivat olla hyvinkin monimuotoisia. On tärkeää tunnistaa, missä tilanteissa ja milloin näitä toimintamalleja käytetään ja erityisesti mikä on niiden vaikutus oppimisen laatuun

Investigating the relation of higher education students’ situational self-efficacy beliefs to participation in group level regulation of learning during a collaborative task

AbstractUnderstanding the role individual beliefs play when the group faces challenge is key in understanding the shared regulation processes and participation that lead to collaborative learning success. As of now, there is not much research focusing on how self-efficacy plays a role in regulation taking place in collaborative group settings. Therefore, the aim of this study is to explore how situational self-efficacy beliefs relate to students’ participation in group level regulation during a collaborative task. The study involved 18 university students working in groups on a computer-based collaborative task. Repeated self-reports measuring group members’ self-efficacy were related to performance feedback from the task as well as participation in group level regulation identified from videotaped collaborative working. The results showed that self-efficacy varied depending on the nature of performance feedback. In addition, the way students participated in regulation was connected with their level of self-efficacy: low self-efficacy was associated with taking a passive role in regulation whereas high self-efficacy was associated with taking an active role. The study suggests that situational self-efficacy beliefs are associated with the participation roles during group level regulation, thus being of practical concern for educators seeking to support learners’ self-efficacy and active participation in collaborative learning

Elementary school students’ strategic learning: does task-type matter?

This study investigated what types of learning patterns and strategies elementary school students use to carry out ill- and- well-structured tasks. Specifically, it was investigated which and when learning patterns actually emerge with respect to students’ task solutions. The present study uses computer log file traces to investigate how conditions of task types that might affect strategic learning. Elementary school students (N = 12) participated in two science study lessons. During these lessons the students were asked to solve well- and ill-structured tasks. For both of these tasks, the students used the gStudy learning environment designed to support strategic learning. In addition, gStudy records traces of each student’s strategic actions as they proceed with tasks. First, the students’ task solutions was rated according to three categories, namely “on track”, “off track” and “partial solution”. Second, learning patterns in terms of learning strategies that emerged throughout these tasks were investigated. Third, detailed cross case analysis was used to explore in depth howandwhen these learning patterns were usedwith respect to the students’ task solutions. The results show that young students’ can provide in-depth task solutions, but also adapt to the task complexity. However, despite the task types being different, the students had same types of learning patterns. The detailed cross-case comparison of the students’ task solutions with respect to learning patterns indicates that there are intra individual differences concerning how students allocate their learning strategy use. Especially if the task is ill-structured, it can also mislead the students to focus on irrelevant aspects and hinder strategic learning

Exploring adaptation in socially-shared regulation of learning using video and heart rate data

Abstract In socially shared regulation of learning, adaptation is a key process for overcoming collaborative learning challenges. Monitoring the learning process allows learners to recognize the situations that require a need to change, revise, or optimize the current learning process. This can be done through adapting their strategies, task perception, goals, or standards for monitoring their progress. This process is called small-scale adaptation. It is not yet clear how shared monitoring in groups activates small-scale adaptation “on the fly” or how this phenomenon can be detected using multimodal data. The aim of this study is to explore how small-scale adaptation emerges during collaboration. Video and heart rate data were collected from four groups of three high-school students (age 16–17) who worked together during six 75-min advanced physics lessons. The results show small-scale adaptation occurs most often when groups switch from enacting tasks to defining them. Physiological synchrony occurred throughout the collaboration and was not significantly more prevalent before or after adaptation occurred. The opportunities and challenges of combining video observation to identify monitoring and adaptation events, and physiological synchrony as a possible indicator of “sharedness,” are discussed, contributing to the literature about using multimodal data to study learning processes

Examining the interplay of knowledge construction and group-level regulation in a computer-supported collaborative learning physics task

Abstract Group-level regulation (co-regulation (CoRL) and socially shared regulation (SSRL)) supports knowledge construction (KC), but it is not yet clear how the two phenomena intertwine in computer-supported collaborative learning (CSCL). This study aims to explore how phases of CoRL and SSRL (planning, task understanding, strategy use, evaluation) occur in relation to KC in collaborative interactions. Secondary school students (N = 34) were videotaped while collaborating in groups of three to four. Their task was to create a poster about “center of gravity” using an interactive tabletop. In the analysis, video data were coded for KC phases and phases of CoRL and SSRL. Process mining was applied to visualize sequential associations between KC and regulation as process maps. This was complemented by qualitative examples illustrating these associations in interactions. Results revealed that KC and regulation manifested either simultaneously via the same talk or interaction or prompted or followed one another. Group-level regulation guided, and supported KC. Current study contributes to CSCL research by demonstrating reciprocal relationships between KC and group-level regulation and the importance of regulation for collaboration. It also provides implications for designing pedagogical tools to support regulation and KC and advancing analytical methods that enable tracking CSCL processes for its better understanding

Capturing the dynamic and cyclical nature of regulation:methodological progress in understanding socially shared regulation in learning

Abstract Self-regulation is critical for successful learning, and socially shared regulation contributes to productive collaborative learning. The problem is that the psychological processes at the foundation of regulation are invisible and, thus, very challenging to understand, support, and influence. The aim of this paper is to review the progress in socially shared regulation research data collection methods for trying to understand the complex process of regulation in the social learning context, for example, collaborative learning and computer-supported collaborative learning. We highlight the importance of tracing the sequential and temporal characteristics of regulation in learning by focusing on data for individual- and group-level shared regulatory activities that use technological research tools and by gathering in-situ data about students’ challenges that provoke regulation of learning. We explain how we understand regulation in a social context, argue why methodological progress is needed, and review the progress made in researching regulation of learning