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

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

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

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

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

Abstract Understanding 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

Monitoring in collaborative learning:co-occurrence of observed behavior and physiological synchrony explored

Abstract Although research on collaborative learning suggests that monitoring plays an important role in successful regulation of the collaborative learning process, little is known about how students attend to it together. This study explores monitoring in collaborative learning. Specifically, it studies how students in a group monitor their cognitive, affective and behavioral processes during their collaboration, as well as how observed monitoring co-occurs with their physiological synchrony during the collaborative learning session. Data was collected from 48 Finnish highschool students who were learning about nutrition in groups of three. The session was videotaped and coded in terms of monitoring of cognition, behavior and affect. Students’ arousal was measured as electrodermal activity with wearable sensors and used to calculate physiological synchrony between the students. Three case groups, with priority on the quality of the data, were chosen for detailed analysis. The results indicate that the main targets of monitoring for these case groups were cognition and behavior, while monitoring of affect occurred the least. Most of the student pairs inside the groups showed significant amounts of physiological synchrony. High values of physiological synchrony occurred when monitoring was frequent. Time series analysis showed a weak positive connection between monitoring and physiological synchrony for two groups out of three. These results indicate that physiological synchrony could potentially shine a light on the joint regulation processes of collaborative learning groups

What makes an online problem-based group successful?:a learning analytics study using social network analysis

Abstract Background: Although there is a wealth of research focusing on PBL, most studies employ self-reports, surveys, and interviews as data collection methods and have an exclusive focus on students. There is little research that has studied interactivity in online PBL settings through the lens of Social Network Analysis (SNA) to explore both student and teacher factors that could help monitor and possibly proactively support PBL groups. This study adopts SNA to investigate how groups, tutors and individual student’s interactivity variables correlate with group performance and whether the interactivity variables could be used to predict group performance. Methods: We do so by analyzing 60 groups’ work in 12 courses in dental education (598 students). The interaction data were extracted from a Moodle-based online learning platform to construct the aggregate networks of each group. SNA variables were calculated at the group level, students’ level and tutor’s level. We then performed correlation tests and multiple regression analysis using SNA measures and performance data. Results: The findings demonstrate that certain interaction variables are indicative of a well-performing group; particularly the quantity of interactions, active and reciprocal interactions among students, and group cohesion measures (transitivity and reciprocity). A more dominating role for teachers may be a negative sign of group performance. Finally, a stepwise multiple regression test demonstrated that SNA centrality measures could be used to predict group performance. A significant equation was found, F (4, 55) = 49.1, p < 0.01, with an R2 of 0.76. Tutor Eigen centrality, user count, and centralization outdegree were all statistically significant and negative. However, reciprocity in the group was a positive predictor of group improvement. Conclusions: The findings of this study emphasized the importance of interactions, equal participation and inclusion of all group members, and reciprocity and group cohesion as predictors of a functioning group. Furthermore, SNA could be used to monitor online PBL groups, identify important quantitative data that helps predict and potentially support groups to function and co-regulate, which would improve the outcome of interacting groups in PBL. The information offered by SNA requires relatively little effort to analyze and could help educators get valuable insights about their groups and individual collaborators

How the monitoring events of individual students are associated with phases of regulation:a network analysis approach

Abstract The current study uses a within-person temporal and sequential analysis to understand individual learning processes as part of collaborative learning. Contemporary perspectives of self-regulated learning acknowledge monitoring as a crucial mechanism for each phase of the regulated learning cycle, but little is known about the function of the monitoring of these phases by individual students in groups and the role of motivation in this process. This study addresses this gap by investigating how monitoring coexists temporally and progresses sequentially during collaborative learning. Twelve high school students participated in an advanced physics course and collaborated in groups of three for twenty 90-minute learning sessions. Each student’s monitoring events were first identified from the videotaped sessions and then associated with the regulation phase. In addition, the ways in which students acknowledged each monitoring event were coded. The results showed that cyclical phases of regulation do not coexist. However, when we examinedtemporal and sequential aspects of monitoring, the results showed that the monitoring of motivation predicts the monitoring of task definition, leading to task enactment. The results suggest that motivation is embedded inregulation phases. The current study sheds light on idiographic methods that have implications for individual learning analytics