Sanna Järvelä

University of Oulu · Department of Educational Sciences and Teacher Education

This study maps the evolution and state of Computer-Supported Collaborative Learning (CSCL) research, analyzing 6,388 documents published between 1990 and 2022. The findings highlight the sustained engagement of a core group of scholars and the field’s geographic concentration in Western countries, particularly the USA and Europe. While the field r...

This study investigates secondary education students' self-regulated learning (SRL) processes with digital trace data, particularly whether SRL processes found in secondary education are comparable to those observed in higher education. We therefore adapted a digital learning environment and rule-based AI algorithm originally designed to measure SR...

Learning regulation research has advanced via the conceptualisation of socially shared regulation (SSRL) in collaborative learning. However, empirical evidencing of SSRL has still faced several challenges due to the unobservability of the cognitive and emotional processes at the core of regulation. Fortunately, with the aid of learning analytics an...

Computational methods for analysing learner data and theories that describe human learning are core aspects of learning analytics. However, the relationships between these methods and theories are often ignored. The four authors met online to discuss these relationships, drawing on experiences from our own work, as well as broader trends that we ha...

Socially shared regulation in learning (SSRL) contributes to successful collaborative learning (CL). Empirical research into SSRL has received considerable attention recently, with increasingly available multimodal data, advanced learning analytics (LA), and artificial intelligence (AI) providing promising research avenues. Yet, integrating these w...

Currently, artificial intelligence (AI) emerges simultaneously as an opportunity and a challenge to established traditions of learning and instruction. Although AI has existed since the 1950s, technological development remains intense. New sophisticated applications, which recently existed only in laboratories, are now on the market and have been q...

Studying in digital learning environments highlights the skills needed to regulate one’s own learning. In youth, students are acquiring and developing these skills, but for many students, effectively self-regulating their learning is challenging. To design support in this regard, an in depth understanding of how and why their self-regulated learnin...

Generative artificial intelligence (GenAI) tools, such as large language models (LLMs), generate natural language and other types of content to perform a wide range of tasks. This represents a significant technological advancement that poses opportunities and challenges to educational research and practice. This commentary brings together contribut...

This paper introduces a theory-driven trigger regulation framework for advancing multimodal analytical approaches to research about self-regulated learning. Events and/or situations that may inhibit learning processes and, thus, require regulatory responses are defined as trigger events. Empirically identifying trigger signals in multimodal data as...

The theory of socially shared regulation of learning (SSRL) suggests that successful collaborative groups can identify and respond to trigger events stemming from cognitive or emotional obstacles in learning. Thus, to develop real-time support for SSRL, novel metrics are needed to identify different types of trigger events that invite SSRL. Our aim...

The arrival of generative Artificial Intelligence (AI) in educational settings offers a unique opportunity to explore the intersection of human cognitive processes and AI, especially in complex tasks like writing. This study adopts a process-oriented approach to investigate the self-regulated learning (SRL) strategies employed by 21 doctoral and ma...

The arrival of generative Artificial Intelligence (AI) in educational settings offers a unique opportunity to explore the intersection of human cognitive processes and AI, especially in complex tasks like writing. This study adopts a process-oriented approach to investigate the self-regulated learning (SRL) strategies employed by 21 doctoral and ma...

This paper introduces Generative Multimodal Analysis (GMA), a novel method designed for utilizing Artificial Intelligence (GenAI) in the analysis of multimodal data derived from learning processes. The method is encapsulated in a systematic framework that integrates and optimizes GenAI technology with multimodal large language models (MLLMs) for ap...

Background The necessity of supporting socially shared regulation of learning (SSRL) for learners is widely acknowledged as a crucial element for successful collaborative learning (CL). However, inherent challenges are presented for the observation, study, and support of SSRL. Non‐verbal behaviours have been identified as a potentially rich resourc...

Healthcare education needs to be reformed to sustain quality, faster response to crises and ensure a rapid and efficient graduation path for future healthcare professionals. In this study, our multidisciplinary team has developed and tested a Human-centred extended reality (XR) to solve challenges in healthcare by connecting humans to technology in...

This poster introduces the Generative Multimodal Analysis (GMA) methodology, an approach that enhances the study of learning contexts through the utilization of generative artificial intelligence to analyze multimodal data. GMA enables the large-scale interpretation of complex non-verbal behaviors, such as posture, gesture, and gaze, within collabo...

Socially Shared Regulation (SSRL) contributes to collaborative learning success. Recent advancements in Artificial Intelligence (AI) and Learning Analytics (LA) have enabled examination of this phenom-enon's temporal and cyclical complexities. However, most of these studies focus on students' verbalised interactions, not accounting for the intertwi...

Purpose Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed...

This study examines the embodied ways in which learners monitor their cognition while learning about exponential functions in an immersive virtual reality (VR) based game, Pandemic by Prisms of Reality. Traditionally, metacognitive monitoring has been assessed through behavioural traces and verbalised instances. When learning in VR, learners are fu...

Metacognitive awareness is knowing about learners’ own thinking and learning, facilitated by introspection and self-evaluation. Although metacognitive functions are personal, they cannot be explained simply by individual conceptions, especially in a collaborative group learning context. This study considers metacognitive awareness on multiple level...

Background Health sciences education prepares students for social‐ and healthcare by developing evidence‐based nursing, leadership and working life skills, including collaboration. Due to the changes caused by the global pandemic, health sciences education has shifted more to online and hybrid contexts, which can challenge students' competence deve...

Self-regulated learning (SRL) is a critical skill for lifelong learning. However, many learners struggle with SRL and need support. With the recent advancement, learning analytics (LA) has offered capabilities for supporting learning, particularly, for SRL in lifelong learning. In designing SRL analytics, recent calls asked to consider the learners...

Recent advances in Learning Analytics (LA) and Artificial Intelligence (AI) have enabled us to gain a better understanding of socially shared regulation (SSRL), which is in collaborative learning. Although recent progress in studying SSRL with LA and AI has provided holistic insights into the temporal and cyclical processes of SSRL, few studies hav...

This study utilized multimodal learning analytics and AI-based methods to examine the patterns of the socially shared regulation of collaborative learning (CL). The study involved multimodal data involving video and electrodermal activities (EDA) data collected from ninety-four secondary school students (N = 94) during five science lessons to revea...

Despite the importance of socially shared regulation of learning (SSRL) in collaborative learning success, there remains a paucity of evidence on how it could be detected and supported effectively. In this paper, we present an experimental study with a systematic analysis approach that utilizes facial expression recognition technology to examine em...

Socially shared regulation (SSRL) has been recognized as a contributing factor to successful collaborative learning. In this paper, we adopted a process-oriented approach to examine how students deliberate for SSRL through different regulatory triggers in a collaborative learning context. More specifically, this study examines the relationship betw...

In this chapter, we outline how modes of interaction, such as cognitive and socio-emotional, and the regulation of learning provide support for collaborative engagement and examine how it changes over time. We start by framing how regulated learning is embedded in the cognitive and socio-emotional interaction between the group members from both a t...

Smart Learning environments (SLEs) are defined [1] as learning ecologies where students engage in learning activities, or where teachers facilitate such activities with the support of tools and technology. SLEs can encompass physical or virtual spaces in which a system senses the learning context and process by collecting data, analyzes the data, a...

Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research,...

We have been working to understand when, how, and what makes regulation in collaborative learning functional aiming to understand the process of collaboration so that we could better inform learners and teachers in practice. Multimodal learning data collection and learning process analytics have guided our work. Seamless and accurate integration of...

This study explored sequential patterns in social interaction states for group-level regulation of learning during collaborative learning. The participants were secondary school students (N = 92) performing collaborative physics tasks. The videotaped sessions were analyzed regarding participation, social interaction, and group-level regulation type...

Research on computer supported collaborative learning (CSCL) covers how to design the technological context for productive collaboration and how people learn in the context of collaborative activity. This is to optimize high level collaborative activities for sharing and building knowledge. CSCL can be designed to facilitate high-level cognitive ac...

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The...

This chapter builds on the notion that multimodal learning analytics (MMLA) has potential to offer new innovations for the field of education and can be gradually implemented to provide constitutive explanations to improve individual student regulation. We will ground the chapter on the theoretical framework of self-regulated learning (SRL), addres...

In this chapter we discuss how groups, and individuals within groups, can be supported to engage in, sustain, and productively regulate collaborative processes for better learning in digital environments. Regulation in collaborative learning is a complex phenomenon for researchers to understand and for learners to be aware. Working with advanced le...

s 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, g...

Background: Group affective states for learning are constantly formed through socio-emotional interactions. However, it remains unclear how the affective states vary during collaboration and how they occur with regulation of learning. Appropriate methods are needed to track both group affective states and these interaction processes. Aims: The p...

Self-regulated learning theory acknowledges the importance of an individual's metacognitive monitoring and group-level regulation for learning achievement in collaborative learning. However, very few studies have empirically investigated the interplay of these factors. This study aimed to investigate how groups' metacognitive interactions, group-le...

The Moving Maths study was conducted to increase third grade pupils’ physical activity and support their learning in Finland. Altogether 397 children (mean age 9.3 years) and 22 teachers took part in an intervention with two types of physically active maths lessons for five months. One group carried out lessons in which PA was integrated into maths...

Being aware of the progress towards one’s goals is considered one of the main characteristics of the self-regulation process. This is also the case for collaborative problem solving, which invites group members to metacognitively monitor the progress with their goals and externalize it in social interactions while solving a problem. Monitoring chal...

This study provides an overview of research on teachers' use of artificial intelligence (AI) applications and machine learning methods to analyze teachers' data. Our analysis showed that AI offers teachers several opportunities for improved planning (e.g., by defining students' needs and familiarizing teachers with such needs), implementation (e.g....

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 monito...

Collaborative problem solving (CPS) is a cyclical process in which team members go back and forth between various cognitive and affective phases as they interact with the problem state and each other. An increasing amount of research has been conducted to explore how sequential relationships between the CPS phases affect team performance outcomes....

Socially shared regulation of learning (SSRL) is essential for the success of collaborative learning, yet learners often neglect needed regulation while facing challenges. In order to provide targeted support when needed, it is critical to identify the precise events that trigger regulation. Multimodal collaborative learning data may offer opportun...

Collaborative problem solving (CPS) is widely recognized as a prominent 21st-century skill to be mastered. Until recently, research on CPS has often focused on problem solution by the individual; the interest in investigating how the theorized problem-solving constructs function as broader social units, such as pairs or small groups, is relatively...

Research has shown that metacognition plays a role in collaborative learning. We view metacognition as a central process supporting all modes of regulation (i.e., self-regulation, shared regulation, and co-regulation), as it enables learners to control and adapt their cognition, motivation, emotion, and behavior at both the individual and group lev...

The aim of this study is to explore how students experience and describe socio-cognitive and socio-emotional challenges in collaborative learning. The participants (N = 20) were teacher education students whose collaborative learning was supported with a designed regulation macro script during a six-week mathematics course. The purpose of the scrip...

This paper reports a design science research methodology (DSRM) study that develops, demonstrates, and evaluates a deep learning model utilizing multimodal data to automatically detect types of interactions for regulation in collaborative learning (RegCL) by using features extracted from electrodermal activity (EDA), video, and audio data involving...

Effective collaborative learning (CL) requires group-level regulation (Kwon et al., 2014) in terms of co-regulated learning (CoRL) and socially shared regulation of learning (SSRL), which invites learners to plan, monitor, and evaluate their learning beyond individual level (Järvelä et al., 2018). Group-level regulation actualizes in social interac...

This study explores how different types of regulated learning (CoRL and SSRL) interplay with phases of knowledge construction in a CSCL task. Secondary school pupils (N=34) were videotaped while working in groups on a poster about the topic “Center of gravity”. Based on micro-level interaction video analysis (Gunawardena et al.'s (1997) Interacti...

Technologies for computer-supported collaborative learning (CSCL) are playing an increasingly prominent role in educational contexts, especially as teachers and students strive to deal with pandemic-related constraints. However, the technologies being used for collaboration on a daily basis are not sufficiently equipped to promote collaborative lea...

This study explores pre-service and in-service teachers’ experiences in working as a learning community. Pre-service teachers (N = 60) and teacher educators (N = 9) from a Finnish university and in-service teachers (N = 27) from four local comprehensive schools worked together over six months. The teachers-as-learners continuous learning model was...

Using hidden Markov models (HMM), the current study looked at how learners' metacognitive monitoring is related to their physiological reactivity in the context of collaborative learning. The participants (N = 12, age 16–17 years, three females and nine males) in the study were high school students enrolled in an advanced physics course. The result...

This study summarises existing instruments for measuring and supporting self-regulated learning (SRL) in schools using articles from the SCOPUS and Web of Science databases. We analyse how the instruments address cognition, motivation or emotions as a target for regulation and whether they acknowledge the phase of SRL (forethought, performance or r...

This study investigated pupil experiences and perceptions of two kinds of classroom-based physically active math lessons during the Moving Maths programme in a Finnish primary school context. Qualitative data were gathered in focus group interviews with 16 nine-year-old pupils. Pupils in the group with physically active breaks during math lessons e...

Research-based discussions about 21st-century skills are currently needed; 21st-century skills refer to skills that today's students are expected to possess for successful future careers. The ways students perceive these skills or what kind of dispositions they have in this regard are significant. This paper provides an overview of the development...

Metacognition is a core process in human learning. The problem is that learners are not often aware of their metacognition. The purpose of this study is to examine learners’ metacognitive awareness in terms of their situation-specific perceptions in collaborative learning tasks. Upper elementary school students (N = 92) filled in a Metacognitive Aw...

This systematic review on data modalities synthesises the research findings in terms of how to optimally use and combine such modalities when investigating cognitive, motivational, and emotional learning processes. ERIC, WoS, and ScienceDirect databases were searched with specific keywords and inclusion criteria for research on data modalities, res...

In collaborative learning situations, monitoring is needed to maintain common progress toward shared goals. The present study aimed to analyze group-level monitoring events, as well as groups’ reactions to these events, to identify instances of adaptive regulation and maladaptive behavior. Three dimensions of monitoring events were qualitatively co...

The aim of this paper is to introduce our current research design to study socially shared regulation processes in a science classroom where a collaborative learning design is implemented. The design is based on a self-regulated learning framework that provides opportunities and support for self-initiated regulation among individual learners and co...

There is a growing body of research on physiological synchrony (PS) in Collaborative Problem Solving (CPS). However, the current literature presents inconclusive findings about the way in which PS is reflected in cognitive and affective group processes and performance. In light of this, this study investigates the relationship between PS and metaco...

Metacognitive experiences are the feelings and judgments that emerge in relation to an ongoing learning task. Much of the work on metacognitive experiences has studied these constructs piecemeal and mostly in individual learning settings. Thus, little is known about how metacognitive experiences co-occur in social learning settings. In light of thi...

This study investigated the interplay of temporal changes in self-regulated learning processes (i.e., behavioral, cognitive, motivational and emotional) and their relationship with academic achievement in computer-supported collaborative learning. The study employed electrodermal activity and self-report data to capture the dynamicity of self-regul...

While prominent empirical research exploring the possibilities to utilize different data channels in the research of regulation in collaborative learning is emerging, we are still in the process of discovering the relevant combinations of different data sources and proper ways to combine data from different channels. This is the case particularly w...

Collaborative learning (CL) can be a powerful method for sharing understanding between learners. To this end, strategic regulation of processes, such as cognition and affect (including metacognition, emotion and motivation) is key. Decades of research on self‐regulated learning has advanced our understanding about the need for and complexity of tho...

To support the development of pupils’ 21st-century skills, teachers themselves must also be competent in these skills and learn them during pre-service teacher education. The aim of this study is to investigate what kind of profiles emerge among Finnish first-year pre-service teachers’ (N = 872) in terms of perceptions of their strategic learning s...

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 soci...