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# Investigating Self-Regulated Learning Measurement Based on Trace Data: A Systematic Literature Review

Amine Boulahmel, Fahima Djelil, Grégory Smits

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Amine Boulahmel, Fahima Djelil, Grégory Smits. Investigating Self-Regulated Learning Measurement Based on Trace Data: A Systematic Literature Review. Technology, Knowledge and Learning, 2025, 10.1007/s10758-025-09816-y. http://hal-04907756

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Investigating Self-Regulated Learning
Measurement Based on Trace Data: A
Systematic Literature Review

Amine Boulahmel†, Fahima Djelil† and Gregory Smits†

IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest, F-29238,
France.

*Corresponding author(s). E-mail(s):

amine.boulahmel@imt-atlantique.fr;

Contributing authors: fahima.djelil@imt-atlantique.fr;

gregory.smits@imt-atlantique.fr;

†These authors contributed equally to this work.

Abstract

Self-Regulated Learning (SRL) theory comprises cognitive, metacogni-
tive, and affective aspects that enable learners to autonomously manage
their learning processes. This article presents a systematic literature
review on the measurement of SRL in digital platforms, that compiles
the 53 most relevant empirical studies published between 2015 and 2023.
The goal of this review is to exhibit current research orientations, char-
acteristics of their contexts, and the SRL theories they rely on. A part of
this study is then dedicated to a categorization of the trace data and indi-
cators used to capture SRL behaviors as well as automatic methods to
leverage them. Finally, we describe how learning scaffolds are provided to
support SRL. In this respect, current research has brought methodolog-
ical and theoretical insights to the study of SRL, particularly through
the analysis of student's behaviors and profiles, and their relationship to
learning performance. Researchers guide their work by using recognized
theoretical models of SRL to map trace data into SRL processes and
dimensions. Future research is motivated by the development of learn-
ing platforms that go beyond data collection to incorporate learning
analytics tools that provide personalized SRL support to students.

**Keywords:** Self-Regulated Learning, Trace Data, Measurement, Scaffolds, Learning Analytics, Educational Data Mining
# 1 Introduction

Although self-regulation has a long existence in the education literature (Zimmerman, 1986), it is a key challenge in modern Online Learning Environments (OLE)(Djelil et al, 2021). Self-regulated learning (SRL) is known to have a good potential for developing autonomy and maintaining motivation for learners (Schunk and Zimmerman, 2012). SRL encompasses an individual's ability to understand and control their learning environment (Zimmerman, 1986). As defined by Kuhl and Goshke (1994), self-regulation involves a set of volitional strategies aimed at maintaining learners' focus on a task while minimizing distractions to achieve their own goals. This implies that learners who have acquired SRL skills are autonomous or have agency over their own learning. Previous empirical studies (Azevedo and Cromley, 2004; Sadati and Simin, 2016) and reviews (Dignath and Büttner, 2008; Sitzmann and Ely, 2011; Richardson et al, 2012; Xu et al, 2023), have also provided evidence of the positive impact of SRL on academic achievement.

Current research has examined SRL primarily through the lens of learning analytics and educational data mining (Saint et al, 2022; Viberg et al, 2020). Specifically, ongoing reviews have examined the analytical techniques used to measure SRL, the trace data used to capture SRL dimensions and the theoretical underpinnings of SRL models. Additionally, they have examined the intervention methods employed to foster SRL within digital environments (Heikkinen et al, 2023). However, there is still a lack of studies that provide a comprehensive overview of SRL research in digital environments, including the range of objectives addressed and the measures and indicators used to study SRL.

In this systematic review, we aim to provide a current state-of-the-art of data-trace studies investigating the measurement of SRL. In particular, we aim to provide a more comprehensive overview of the different dimensions involved in the study and support of SRL in digital learning environments. First, this systematic review helps researchers identify the focus of current studies in terms of SRL research objectives. Examining multiple objectives (e.g. investigating SRL behaviors, student profiles, or the effect of SRL on learning performance) provides an understanding of current research trends and highlights gaps in the field. Second, it investigates the theoretical frameworks used in SRL research, including the SRL models, applied to specific objectives, allowing for the identification of the most widely used models and their applications. Third, as the review focuses on studies utilizing trace data, it also examines the types of trace data collected and their relationship to SRL processes. In addition, it reviews the measures and indicators used to assess SRL behaviors, providing insights into the types of behaviors measured and how they relate to SRL processes. The review also examines the most common analytical methods used to interpret trace data, measures, and indicators in relation to SRL research objectives. Finally, it identifies the scaffolds designed to support SRL, and how they are delivered. This helps to identify
the extent to which current research focuses on supporting SRL in digital envi-
ronments. In summary, this review aims to provide comprehensive coverage
and a complete perspective of all relevant areas of SRL research (see Table 2),
and to highlight trends and gaps in current research directions. We address
the following research questions:

1. RQ1. What are the **research trends** in measuring SRL?

2. RQ2. What **trace data and indicators** are used to measure SRL?

3. RQ3. What **methods** are used to analyze SRL?

4. RQ4. What are the **learning scaffolds** used to support SRL?

This review is aimed at practitioners who are either new or familiar with the study of SRL in digital learning platforms. It aims to provide practitioners with the latest advances in the literature on trace data collection, analysis methods, indicators, and the scaffolding used to support SRL.

The article is structured as follows. Section 2 introduces the background to the theory of SRL and its measurement in digital learning environments, as well as the related literature reviews. Then, in section 3, we describe the methodology used to conduct this systematic review: the search strategy and selection criteria, as well as the information extraction and classification processes. In section 4, we detail our findings in relation to the research questions RQ1 to RQ4. A discussion is finally provided in section 5, and conclusions and implications are drawn in section 6.

## 2 Background

To position our review in relation to the existing literature, we provide an
overview of related work defining SRL in the educational literature, and what
is the direction of existing research aimed at measuring SRL. In addition, we
summarize existing literature reviews conducted through the lens of learning
analytics for measuring SRL.

## 2.1 Self-Regulated Learning Theory

Since Zimmerman (1986)'s pioneer work in cognitive research, which explored
how individual learners become experts at different tasks, SRL has emerged
as a major area of education research. Self-regulation is a well-established
concept that encompasses the cognitive, metacognitive, behavioral, motiva-
tional, emotional, and affective aspects of learning (Azevedo, 2015; Panadero,
2017). Another facet of self-regulation is to protect the intention to learn by
regulating motivation, emotion, and attention (Cosnefroy, 2010). Metacogni-
tion includes its function in directing and controlling concentration during
learning (Corno, 1986). Affective processes relate to how appraisals influence
learning intentions (Boekaerts, 1992), and motivational processes remain the
driving force by which learners willingly perform their task (Schunk and Zim-
merman, 2012). A typology of fourteen strategies of SRL, mainly concerned
with information processing, has been developed using a collection of qualitative data (Zimmerman, 1986). These include cognitive strategies (reviewing courses, organizing information, memorizing), metacognitive strategies (self-assessment, planning), and strategies for seeking additional information (using documentary resources, asking others for help).

SRL theory has been established in various models proposed in the literature. These models provide a comprehensive framework describing the processes and sub-processes used by learners. While some models offer a broad perspective on SRL (Zimmerman, 1989; Zimmerman and Moylan, 2009), others focus on specific dimensions, such as metacognition (Winne and Hadwin, 1998; Efklides, 2011) or emotion and affect (Boekaerts, 1991). These models have been refined through a series of empirical tests (Panadero, 2017).

Some models have evolved over time, for example, Zimmerman and Moylan (2009) provided a cyclical approach that describes how learners actively manage their learning by setting goals, implementing strategies, monitoring their progress, and reflecting on their performance. This model recognizes motivation and metacognition as core elements within SRL. The Boekaerts' model (Boekaerts, 2011) provides a structured way of understanding how learners manage their learning process, either by focusing on task mastery and growth, characterized by a focus on tasks, goals, and the learners' needs, or by prioritizing the maintenance of their well-being through emotions and personal motivational goals. It recognizes the importance of both the cognitive and emotional dimensions of SRL. Winne and Hadwin (1998) developed their model by emphasizing the efficient use of cognitive strategies to optimize the learning process. This model emphasizes how students use criteria and standards to set goals, monitor progress, and evaluate their work. Although it doesn't explicitly address emotions, it recognizes the importance of motivation and is consistent with research on self-assessment. Pintrich (2000)'s model focuses on the relationship between SRL and motivation and is notable for incorporating elements including empirical evidence from the Motivated Strategies for Learning Questionnaire (MSLQ) (Panadero, 2017). This questionnaire includes items that assess learners' behaviors and experiences related to the four phases outlined in the model (i.e., forethought phase, monitoring phase, control phase, and reaction and reflection phase). The aforementioned characteristics such as cognition, motivation, affect (emotions), behavior, and context principles are integrated. Bannert et al (2014) proposed a framework that distinguishes between the main categories of metacognition, cognition, motivation, and others.

These models have some similarities in that they share a basic cyclic framework (Puustinen and Pulkkinen, 2001; Panadero, 2017): 1) A preparatory phase in which learners prepare for effective learning by understanding the task, setting goals, and developing a strategic plan to approach the learning process with purpose and motivation; 2) a performance phase in which one strives to achieve goals, process information, perform tasks, and continuously monitor and control actions to achieve optimal learning outcomes; and
<table><caption>**Table 1** Description of the macro and micro-level processes composing the Siadaty's SRL model (Siadaty et al, 2016c).</caption><thead><tr><th>Macro-level process</th><th>SRL process</th><th>SRL</th><th>Description</th></tr></thead><tbody><tr><td rowspan="3">Planning</td><td>Task Analysis</td><td></td><td>To get familiar with the learning context and the definition and requirements of a (learning) task at hand</td></tr><tr><td>Goal Setting</td><td></td><td>To explicitly set, define, or update learning goals</td></tr><tr><td>Making Personal Plans</td><td></td><td>To create plans and select strategies for achieving a set of learning goals</td></tr><tr><td rowspan="2">Engagement</td><td>Working on the Task</td><td></td><td>To consistently engage with a learning task, using tactics and strategies</td></tr><tr><td>Applying Strategy Changes</td><td></td><td>To revise learning strategies, or apply a change in tactics</td></tr><tr><td rowspan="2">Evaluation &amp; Reflection</td><td>Evaluation</td><td></td><td>Evaluating one's learning process and comparing one's work with the goal</td></tr><tr><td>Applying Strategy Changes</td><td></td><td>Reflecting on individual learning and sharing learning experiences</td></tr></tbody></table>

3) finally, an evaluation phase in which learners use feedback to regulate and adjust their strategies, engage in reflection, and make metacognitive adjustments to improve their learning process and outcomes. These three phases are outlined in the model proposed by Siadaty et al (2016c): 1) planning, 2) engagement, and 3) evaluation and reflection, which manifests the three common phases across the principal SRL models discussed in this section. Within these third phases, this model defines micro-level SRL processes, with a set of specific self-regulatory activities identified as shown in Table 1. This model serves as a framework for trace data analysis. It guides the tracing of learners' actions in a learning environment and translates these data into indicators of SRL processes. It is for this reason that we have chosen to use this model, particularly to describe the trace data and indicators used to measure SRL in the studies reviewed. In fact, the Siadaty et al (2016c)'s model offers a methodological approach to investigate SRL and ways to examine factors that affect learners' use of self-regulatory processes in technology-enhanced learning environments. One may also remark that this model is the most widely used to guide learning analytics investigation regarding SRL measurement.

## 2.2 Measurement of Self-Regulated Learning

The measurement of SRL has undergone three waves during its development (Panadero et al, 2016). The first wave of SRL measurement relied on the use
of traditional static instruments that examine SRL from a trait-based per-
spective, relying mainly on learners’ perceptions and beliefs. Such instruments
include the use of self-reports, questionnaires, surveys, and interviews to assess
SRL (Roth et al, 2016). Questionnaires are the most widely accepted instru-
ments, but limitations to their use have been identified (Winne and Perry,
2000; Veenman, 2005; Veenman et al, 2006; Rovers et al, 2019). They don’t
capture the dynamic changes in how learners adapt and change their learning
tactics and strategies throughout the learning process (El-Sayed et al, 2019).
Limitations also include the lack of quality and accuracy of the data reported
by learners on the use of strategies. However, they are still considered valid
if they are adequately adapted to the particular context in which the study
takes place (Panadero et al, 2016).

There has been a shift towards exploring more tailored approaches to
assessing SRL, with a second wave of SRL measurement, offering a process-
based perspective (Panadero et al, 2016). In this respect, SRL is established
as a dynamic and recursive process that unfolds in several steps over time
(Winne and Buttler, 1995). Event-based measures have been introduced to
capture the learners’ activity to understand their current SRL state. Examples
of measures include thinking-aloud protocols and traces (Panadero et al, 2016).
While think-aloud protocols are better predictors of learning outcomes, thus
improving the validity of SRL measurement (Schunk and Greene, 2018), other
concerns have been raised regarding their intrusive nature during learning and
the labor-intensive transcription and coding of the collected data (Greene and
Azevedo, 2009; van der Graaf et al, 2021). However, think-aloud protocols
provide insights into the explicit cognitive processes of learners, which leads
to an improved SRL measurement and, thereby allows a better understanding
of how learners self-regulate when properly used (Ericsson and Simon, 1980;
Greene and Azevedo, 2009; Greene et al, 2018; Lim et al, 2022).

Learning analytics offer many techniques for analyzing student log data (Roll and Winne, 2015; van der Graaf et al, 2021), which provide accurate observations of learners’ interactions with the digital platforms (Perry and Winne, 2006). Learning Analytics is defined as the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purpose of understanding and optimizing learning and the environments in which it occurs (Siemens et al, 2011). In practice, the main difficulty in using log data lies in identifying log events that represent the enactment of particular SRL processes (van der Graaf et al, 2021). This can be leveraged by incorporating tools into the digital platforms to reveal these processes (Winne, 2018) and capture valuable data about learners and their behaviors during learning, in order to provide, for example, actionable intervention support for the learner (Winne, 2017).

The third wave of SRL measurement is characterized by the incorpora-
tion of different methods that not only assess learners’ SRL, but also actively
improve it through interventions (Panadero et al, 2016). Different tools have
been proposed to incorporate SRL measurement and interventions (Azevedo
et al, 2022; Winne et al, 2019; Pérez-Alvarez et al, 2018; Siadaty et al, 2012). These digital environments guide learners by incorporating built-in tools that monitor students' activities during the learning process, such as note-taking, essay writing, information retrieval, and time management. They use learning analytics techniques to analyze these activities and offer guidance to learners based on the patterns identified in their actions. Moreover, combining SRL measurement and interventions can influence the measurement outcomes, as interventions often modify learners' cognitive and behavioral strategies during the learning process (Heikkinen et al, 2023; Shao et al, 2023).

In summary, many studies have investigated the measurement of SRL using multiple instruments, including trace data (Srivasta et al, 2022; Quick et al, 2020). The scope of this review includes empirical studies that use trace data to measure SRL, possibly supplemented by other instruments such as questionnaires. Studies using questionnaires only are not included in this review.

## 2.3 Related reviews of literature

Current research on SRL, conducted through the lens of learning analytics and educational data mining, involves the use of novel technologies to measure and support SRL and its related dimensions. Learning analytics and educational data mining provide methods and techniques for exploring and analyzing educational data to gain a deeper understanding of students learning (Romero and Ventura, 2020), and improve the learning process (Siemens, 2013). Previous findings have shown that research has focused on a deeper understanding of students' learning experiences and SRL behaviors (Viberg et al, 2018). This is an area of growing research interest, as shown by a number of systematic reviews.

El-Sayed et al (2019) provided a categorization of the most commonly used analysis techniques in SRL measurement, the types of data used to assess SRL, and the widely measured SRL behaviors, characteristics, and components. The review reported hierarchical agglomeration clustering and regression analysis as the most frequently used technique for SRL assessment. This popularity is explained by the ability of clustering methods to provide more adaptive scaffolding through agent-based Intelligent Tutoring Systems or Virtual classrooms (Bouchet et al, 2013). The study also reported log data as being the type of data mainly used across studies, which emerges from learner's interaction in learning environments and tools such as LMS and Intelligent Tutoring Systems. In terms of the most measured SRL behaviors, goal-setting and monitoring were the most measured processes identified across studies.

Viberg et al (2020) reviewed studies that measure and support SRL in online learning environments. They examined the phases of SRL, according to the Zimmerman (2002)'s model. They provided categories of learning analytics methods used to measure SRL, and categories of SRL support (e.g. visualizations, feedback, recommendations). They highlighted that most of the studies
were carried out in higher education, which can be seen as a limitation, as stu-
dents' SRL should be supported earlier. They pointed out that researchers did
not approach the studies in a systematic ethical way, and that little research
has been done on analyzing the reflection and the monitoring phases of SRL.

Araka et al (2020) outlined the advances and emerging trends in SRL across different empirical studies in the field. They categorized studies based on the methods used to measure SRL, the learning environment, the type of data collected, the data collection instruments, and the analysis methods. In addition, they provided a classification of SRL measurement and intervention tools. Finally, they summarized the challenges associated with measuring SRL in e-learning platforms.

Saint et al (2022) focused on temporal and sequential analyses of SRL and its related dimensions. They explored SRL as a cyclical process that unfolds over time rather than a traditional quantitative research paradigm, thus focusing on its temporal aspect. The main goal of their review is to emphasize the methodological importance of temporality and sequence, as opposed to conventional statistical analysis that does not consider the dynamic aspect of SRL, as there has been no previous systematic treatment of SRL in the context of its conceptualization as a phenomenon that unfolds sequentially over time.

They examined the types of data sources used in the temporal analysis of SRL, the underlying theoretical models of SRL used to interpret the temporal analysis results, the types of temporal analysis methods used, and the types of phenomena or processes modeled.

Heikkinen et al (2023) examined studies that used learning analytics interventions to explore their effectiveness and impact on SRL and learning outcomes. They categorized the methods used to identify the need for SRL support, the channels used in learning analytics interventions, the phases of the SRL cycle targeted by the interventions, and the effectiveness and impact of these interventions. The review also concluded that more comparative research on LA interventions is needed to identify the most effective interventions and to provide further evidence on the effectiveness of interventions to support SRL.

Damayanti et al (2023) conducted a systematic review to gain deeper insights into the study of SRL processes using trace data from online learning systems. Their review included the identification and categorization of different learning modalities used in online learning platforms (e.g. blended learning or flipped classrooms, MOOC, etc.), a categorization of the learning features incorporated in online learning systems, an analysis of the research objectives outlined in the literature, and finally a categorization of the methods used by researchers to achieve these objectives.

Similarly, Du et al (2023b) provided a comprehensive review for under-
standing the SRL process through online trace data. They reviewed studies
that used online trace data to measure SRL in different learning platforms.
Their review included the mapping of the various online trace data to the
relevant SRL process. They also identified commonly used methods for interpreting online trace data, highlighted challenges associated with using trace data to measure SRL, and concluded by proposing a flowchart to guide future research in the application of online trace data within SRL studies.

These literature reviews have mainly considered the commonly used methods of data analysis in SRL measurement, as well as the type of data sources and instruments used prior to analysis (see Table 2). SRL theory is also in the scope of several reviews. Some reviews also consider the types of SRL support. Although, these reviews provide a clear understanding of the state of research in SRL measurement, a more holistic view of the different dimensions involved in the study of SRL may facilitate the reading of the latest state of research. In fact, from one review to another, the findings and results presented cover different aspects of SRL measurement, and to our knowledge, there is no review that provides a complete analysis of all the necessary dimensions to facilitate the understanding of SRL measurement in modern digital learning environments. This motivates our research to broaden the scope of this review by including different dimensions such as study objectives, SRL theoretical models, trace data and measures, LA methods, and SRL scaffolds. We believe that this requires a systematic analysis of all necessary analysis dimensions of SRL measurement to contribute to established and future research in the field.

<table><caption>**Table 2** Comparison of the scope of existing literature reviews investigating SRL measurement.</caption><thead><tr><th>Literature review</th><th>Research trends</th><th>SRL theory</th><th>Data sources</th><th>Indicators</th><th>Analytic methods</th><th>SRL support</th></tr></thead><tbody><tr><td>El-Sayed et al (2019)</td><td></td><td></td><td>×</td><td></td><td>×</td><td></td></tr><tr><td>Viberg et al (2020)</td><td></td><td>×</td><td></td><td></td><td>×</td><td>×</td></tr><tr><td>Araka et al (2020)</td><td>×</td><td></td><td>×</td><td></td><td>×</td><td>×</td></tr><tr><td>Saint et al (2022)</td><td>×</td><td>×</td><td>×</td><td></td><td>×</td><td></td></tr><tr><td>Heikkinen et al (2023)</td><td></td><td>×</td><td></td><td></td><td>×</td><td>×</td></tr><tr><td>Damayanti et al (2023)</td><td>×</td><td></td><td></td><td></td><td>×</td><td></td></tr><tr><td>Du et al (2023b)</td><td></td><td>×</td><td>×</td><td>×</td><td></td><td></td></tr><tr><td>Our proposal</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td></tr></tbody></table>

## 3 Method

We now describe our strategy and criteria for searching the papers in the selected databases, and our procedure for categorizing the information extracted from the reviewed papers.
## 3.1 Search strategy and criteria

The structure of this review (Fig. 1) follows the guiding principles of the PRISMA framework (Moher et al, 2009). To gather the appropriate literature for this study, we focused mainly on two primary source databases, ScienceDirect (computer science field) and Scopus, and the Google Scholar search engine, using its advanced search function to select papers from Learning Analytics and Knowledge (LAK), Educational Data Mining (EDM) conference proceedings and journals, and Metacognition and Learning Journal. As we intend to search for papers related to the measurement and the scaffolding of SRL, we developed the following query: *(self-regulated learning OR SRL) AND (measurement OR measure OR indicator) AND (trace data) AND (learning analytics OR educational data mining)*. We limited the search period to papers published between 2015 and 2023 (8 years).

We performed three searches during the year using the same query, in February, March, and May 2023, resulting in a total of 1005 papers. Following the search, we applied exclusion criteria to the resulting papers to remove papers that were not related to education and studies that did not focus on SRL (N = 125). The remaining papers were reduced to a final sample (N = 53) by removing papers that did not describe empirical studies, studies that did not use trace data in their analysis methods, and duplicate papers.

<figure><img src="image_4.png" /><figcaption>Fig. 1 Search strategy and criteria for the systematic literature review.</figcaption></figure>
## 3.2 Information extraction procedure

To answer the research questions RQ1 to RQ4, we used a concept matrix method (Webster and Watson, 2002) to structure the content of the information extracted from the papers. RQ1 looks at research trends in SRL measurement, RQ2 refers to the trace data and indicators used to capture SRL, RQ3 focuses on the data analysis methods used to study SRL, and RQ4 examines the types of scaffolds used to support SRL.

To answer RQ1, we break down the question into the following: 1) the participant setting and system used in the study, 2) the research objective in measuring SRL, and 3) the SRL model used as the basis for the study. This led us to define the concept matrix to be fed in relation to RQ1. After rereading each article, we extracted the information related to each concept to classify the participants' settings and systems, research objectives, and SRL models.

To answer RQ2, we extracted the information related to the trace data used in each study by considering the collected event logs, we then applied the model of Siadaty (Siadaty et al., 2016c) to describe the trace data with respect to the SRL processes. The Siadaty model was employed for two reasons: 1) it is one of the most widely used frameworks for self-regulated learning (SRL), and 2) it offers a categorization of SRL processes through event-based learner interactions with a digital platform. In addition, we extracted the information related to the indicators used to capture the SRL dimensions, distinguishing between behavioral metrics and performance metrics.

To answer RQ3, we extracted information on the analytical method used in each study, distinguishing between unsupervised methods, which use unlabelled data, and supervised methods, which use labeled data.

Finally, to answer RQ4, we extracted information on the scaffolding nature of SRL when considered in the study.

# 4 Results

We present the results of the review according to the research questions. Fig. 2 shows the increasing trend in the number of published studies in this area, especially in the period 2020-2023.

For each research question, outcomes are described using meaningful categories and by indicating their frequency and rate of occurrence in the studies reviewed. In addition, for each outcome we have provided a sample of the studies reviewed from our set of papers.

## 4.1 RQ1. Research trends in measuring SRL and its related dimensions

In order to examine the research trends in measuring SRL and its related dimensions, it is worth considering first the educational settings in which learning occurs, including participant profiles and system typologies. In addition
<figure><img src="image_5.png" /><figcaption>Fig. 2 Papers counted by year of publication.</figcaption></figure>

to research trends, we also examine the theoretical foundations of the studies by looking at the SRL models used.

### 4.1.1 Participant settings and systems

In the reviewed studies (see Table 3), the most common educational setting was higher education (HE), particularly at the undergraduate level. Several studies mixed participants from different levels (undergraduate/graduate/-postgraduate). Primary and secondary education settings are less represented. Two studies were conducted in professional settings (Siadaty et al, 2016a,b) and were included as they contribute to the scope of this review. Similarly to (Saint et al, 2022), we distinguish between authentic and experimental settings. The majority of studies took place in authentic settings (e.g., classroom or online). In the case of experimental settings, studies were conducted in a laboratory or classroom-like laboratories. The sample size of the majority of studies is between 100 and 500 participants. Studies with a sample size of less than 100 participants form the second largest group. Few studies were conducted with more than 500 or 1000 participants.

Most studies use Learning Management Systems (LMS) as learning platforms and trace data collection systems. There is a relatively small number of studies that use other Online Learning Environments (OLE) and MOOCs. We identified three studies that used simulation-based environments and two studies that used intelligent tutors (ITS). Several other types of systems were used, such as a learning software (Siadaty et al, 2016a,b), an e-learning application (Goda et al, 2015) and a game (Ternblad et al, 2022).

### 4.1.2 Research objectives in the measurement of SRL

We have identified six categories of research objectives in the set of papers reviewed (see Fig. 3). There is a strong interest in studying SRL behaviors
<table><caption>**Table 3** Classification of the studies according to the participant settings and systems.</caption><thead><tr><th>Setting/system</th><th>Categories</th><th>F</th><th>RF</th><th>Sample studies</th></tr></thead><tbody><tr><td rowspan="6">Participants</td><td>Undergraduate</td><td>26</td><td>49%</td><td>Cicchinelli et al (2018); Rienties et al (2019)</td></tr><tr><td>Graduate</td><td>4</td><td>8%</td><td>Tabuenca et al (2015); Hatala et al (2023)</td></tr><tr><td>Postgraduate</td><td>1</td><td>2%</td><td>Suraworachet et al (2023)</td></tr><tr><td>Mixed</td><td>8</td><td>15%</td><td>Kizilcec et al (2017); Ye and Pensisi (2022)</td></tr><tr><td>Not specified in HE</td><td>6</td><td>11%</td><td>Dang and Koedinger (2020); Fan et al (2022a)</td></tr><tr><td>Primary, middle or high school</td><td>6</td><td>11%</td><td>Li et al (2020); Ternblad et al (2022)</td></tr><tr><td>Professional</td><td>2</td><td>4%</td><td>Siadaty et al (2016a,b)</td></tr><tr><td rowspan="2">Authenticity</td><td>Authentic</td><td>43</td><td>81%</td><td>You (2016); Chen and Li (2021)</td></tr><tr><td>Experimental</td><td>10</td><td>19%</td><td>Lim et al (2023); Sun et al (2023)</td></tr><tr><td rowspan="5">Sample Size</td><td>0-50</td><td>9</td><td>17%</td><td>Tabuenca et al (2015); Chen and Li (2021)</td></tr><tr><td>50-100</td><td>10</td><td>19%</td><td>Segedy et al (2015); Siadaty et al (2016b)</td></tr><tr><td>100-500</td><td>23</td><td>43%</td><td>Dang and Koedinger (2020); Quick et al (2023)</td></tr><tr><td>500-1000</td><td>6</td><td>11%</td><td>Fan et al (2021); Feldman-Maggor et al (2022)</td></tr><tr><td>&gt; 1000</td><td>5</td><td>9%</td><td>Li et al (2018); de Barba et al (2020)</td></tr><tr><td rowspan="6">System</td><td>LMS</td><td>28</td><td>53%</td><td>You (2016); Kim et al (2018)</td></tr><tr><td>OLE</td><td>7</td><td>13%</td><td>Sun et al (2023); Lim et al (2023)</td></tr><tr><td>MOOC</td><td>4</td><td>8%</td><td>Kizilcec et al (2017); Wong et al (2019)</td></tr><tr><td>Simulation</td><td>3</td><td>6%</td><td>Goda et al (2015); Zheng et al (2020)</td></tr><tr><td>ITS</td><td>2</td><td>4%</td><td>Dang and Koedinger (2020); Wang et al (2023)</td></tr><tr><td>Various</td><td>9</td><td>17%</td><td>Segedy et al (2015); Suraworachet et al (2023)</td></tr></tbody></table>

and student profiles, and then in studying different effects and the relationship between SRL and learning.

## SRL behaviors and student profiles

Most of the studies aim to examine SRL behaviors and processes or strategies (Fig. 3). Some of these studies focus on understanding the processes of self-regulation by exploring how students regulate their effort and learning (Molenaar et al, 2021; Zhang et al, 2021; Fan et al, 2021, 2022a; Saint et al, 2020b). Quick et al (2020) compared behavioral measurements to established measurements of components of learning as understood through self-regulated learning frameworks. Li et al (2018) identified significant behavioral indicators
for learning from SRL behaviors measurements. Indicators may represent SRL phases (Salehian Kia et al, 2021) and SRL strategies may be explored in terms of learning outcomes (Srivasta et al, 2022). Most of the studies seek to identify student profiles by identifying learners' patterns (Kim et al, 2018; Rodriguez et al, 2021; Bannert et al, 2014; Chen and Li, 2021; Cicchinelli et al, 2018). Temporality in students' SRL behaviors is often explored to understand the dynamics of SRL phases and transitions. Nazeri et al (2023) explored the temporal order of SRL phases and characterized different types of SRL processes based on phase transitions and the cyclic nature of SRL. Poquet et al (2023) examined students' profiles based on dynamic changes in their behavior. Dang and Koedinger (2020) analyzed temporal patterns of gaming behaviors for detecting trends in students' motivation. Li et al (2020) examined the change of students' SRL distinct profiles over time to understand SRL dynamics. SRL profiles can be analyzed in terms of SRL skills and based on learning pathways (Bourguet, 2022).

## Effect of SRL on learning performance

A second major part of the studies seeks to examine the effect of SRL on learning performance and to predict learning success. The aim is to understand which SRL strategies are most effective for learners and how these strategies manifest (Kizilcec et al, 2017). This implies identifying behavioral indicators reflecting SRL skills affecting students' final performance (Yildirim and Gülbahar, 2022; Rienties et al, 2019; Feldman-Maggor et al, 2022), engagement and grade (de Barba et al, 2020; Fan et al, 2022c), or predict course achievement (You, 2016). Pardo et al (2017) explored how to combine data about SRL skills with observable measures to increase predictive capabilities of student academic performance to inform teaching and task design.

## Effect of learning activities on SRL

Another category of studies seeks to understand the different effects of learning activities on SRL. Sun et al (2023) aimed to understand the effects of a personalized learning platform on students' SRL strategies. Li et al (2022) investigated the effectiveness of providing students with individualized analytic feedback in supporting SRL. Tabuenca et al (2015) explored the effects of tracking and monitoring time devoted to learning with a mobile tool on SRL. Wang et al (2023) examined how task complexity affects the temporal characteristics of SRL behaviors in clinical reasoning. Suraworachet et al (2023) explored the impact of personalized behavioral feedback interventions based on students' writing engagement with different SRL competencies.

## Combining self-reported and digital-trace measures of SRL

Other studies seek to combine students' self-reported SRL with the behaviors and strategies observed from digital trace data. This includes examining whether students' self-reported SRL aptitudes align with their observed strategies from trace data (Beheshitha et al, 2015; Ye and Pennisi, 2022; Han and
<figure><img src="image_6.png" /><figcaption>**Fig. 3** Classification of papers according to their research objectives in the measurement of SRL.</figcaption></figure>

Ellis, 2023), or the association between the perceived value of technological scaffolding interventions and micro-level processes of SRL (Siadaty et al, 2016a).

### Use of SRL scaffolds and effect on learning

Understanding the use of scaffolds and measuring their impact on learning is another objective of some studies (Hatala et al, 2023; Wong et al, 2019). Lim et al (2023) compared the effect of personalized scaffolds and generalized scaffolds on learning activities and performance. Schumacher and Ifenthaler (2021) investigated whether different SRL prompts affect declarative knowledge and transfer, perceptions, and online learning behavior. Du et al (2023a) evaluated the impact of two recommended approaches to SRL activities on SRL skills and learning performance and student engagement and motivation.

### Frameworks for measuring and interpreting SRL processes

Suggested frameworks are a separate category of studies. Fan et al (2022b) proposed a novel validation approach for trace-based measurement protocols of SRL processes, with the aim of increasing the validity of interpretations of SRL processes extracted from trace data. Segedy et al (2015) developed and evaluated a novel approach called "Coherence Analysis" for interpreting students' learning behaviors with a focus on SRL. Saint et al (2020b) proposed a framework composed of a sequence of techniques called the "Trace-SRL", which utilizes microlevel processing to transform raw trace data into SRL processes. It allows us to explore the use of micro-level process analysis techniques to identify optimal and suboptimal traits of SRL.

### 4.1.3 SRL models driving SRL measurement

In terms of models of SRL that serve as frameworks for the analysis, we bring some of the categories proposed by Saint et al (2022) to categorize our set of
studies (Fig. 4): 1) Recognized models, which are mainly Zimmerman (Zimmerman, 2002), Siadaty (Siadaty et al, 2016c), Winne and Hadwin (Winne and Hadwin, 1998) and Pintrich (Pintrich, 2000); 2) Combined models, referring to studies in which dimensions of several SRL models were combined in unified frameworks; and 3) Dimensions, referring to studies in which dimensions of SRL were examined without focusing on a single recognized or combined model.

While the models proposed by Zimmerman (Zimmerman, 2002), and Pintrich (Pintrich, 2000) are defined as conceptual frameworks that provide an understanding of the SRL theory, other models such as the ones proposed by Siadaty (Siadaty et al, 2016c) and Winne and Hadwin (Winne and Hadwin, 1998) are presented as frameworks used to distill the link between SRL processes and trace data. Although the Zimmerman (Zimmerman, 2002) and Pintrich (Pintrich, 2000) models are theoretical frameworks, they are still used for analyzing SRL processes (Fig. 4). The framework of Bannert et al (2014) is not presented as an SRL model, but rather as a means of demonstrating how process mining can be used to test theoretical models. It is used in one study (Fan et al, 2023) to guide the measurement of SRL processes. Combined models are also predominantly used in several studies, combining multiple model dimensions to drive their research (Schumacher and Ifenthaler, 2021; Li et al, 2020). In these studies, dimensions derived from multiple SRL models are distilled into a single customized framework (Suraworachet et al, 2023; Segedy et al, 2015; Zheng et al, 2020; Suraworachet et al, 2023). In addition, several studies examined dimensions of SRL without specifying any model or framework (Goda et al, 2015; Pardo et al, 2017; Li et al, 2018). Finally, some studies do not fit into the precedent categories and did not specify the theoretical basis of SRL (Dang and Koedinger, 2020; Quick et al, 2020).

Models of SRL can be used to map learning activities into SRL phases (Du et al, 2023a), but the models serve as coding schemes for analyzing learners' actions and fitting them into SRL processes (Srivasta et al, 2022; Sun et al, 2023; Wang et al, 2023). Based on SRL models, Saint et al (2020a); Fan et al (2021) examined learner actions, such as accessing schedules, to determine how learners set their goals. Ng et al (2023) employed SRL models to analyze log events recording learner's planning process. Similarly, Saint et al (2020b,a) applied Siadaty's model (Siadaty et al, 2016c) to discern when learners are creating plans and choosing strategies for reaching their learning objectives. Hatala et al (2023) and Nazeri et al (2023) used Winne and Hadwin's model (Winne and Hadwin, 1998) to identify SRL-associated activities, which highlighted how learners partition tasks into more manageable sub-tasks.

## 4.2 RQ2. Trace data and indicators for SRL measurement

Measuring SRL involves collecting trace data of learners' interactions with digital learning environments, aggregated to form indicators that provide insights
<figure><img src="image_7.png" /><figcaption>Fig. 4 Classification of the studies according to the underpinning model of SRL.</figcaption></figure>

into understanding SRL processes. We describe what trace data and indicators are used in our set of studies to measure SRL dimensions and processes.

## 4.2.1 Trace data capturing SRL processes

Trace data displayed in the studies are mainly event logs (mouse clicks, keystrokes) that provide useful information about the learner activities and interactions during SRL phases (Lim et al, 2023). These include navigation logs, which record the learner's navigation through the digital environment, moving on from one interface to another (Hatala et al, 2023; Poquet et al, 2023). SRL is also often instrumented in the digital learning environments, allowing for the collection of useful trace data, through interactive interfaces that enhance the learner's interactions and records (Sun et al, 2023; Srivasta et al, 2022).

We classify the trace data used to capture and measure SRL in our set of studies, according to the macro and micro levels of SRL as defined in Siadaty's model (Siadaty et al, 2016c): 1) planning through task analysis, goal setting, and making personal plans; 2) engagement through working on the task, and applying strategy changes; and 3) evaluation and reflection (Table 4). We describe what trace data are used to capture the SRL processes. Table 4.

### Trace data capturing planning

Several studies that have focused on capturing planning through task analysis have shown an approach that involves the elaboration on content-related comments during essay writing (Lim et al, 2023; Srivasta et al, 2022; Fan et al, 2023), using textual comments recorded on the essay writing panel. Other studies relied on recording the learners' choice of competencies related to their assignments from mouse-clicks (Lim et al, 2023; Srivasta et al, 2022; Fan et al, 2022a,b, 2023; Siadaty et al, 2016a). This allows to understand how students
<table><caption>**Table 4** Trace data categories according to the macro and micro level of SRL (Siadaty et al, 2016c).</caption><thead><tr><th>Macro-level process</th><th>Micro-level process</th><th>Trace Data (log event)</th><th>F</th><th>RF</th><th>Sample studies</th></tr></thead><tbody><tr><td rowspan="3">Planning</td><td>Task Analysis</td><td>Commenting content; Selecting competencies; Clicking on assignments, general instructions and guidelines or tutorials;</td><td>24</td><td>45%</td><td>Siadaty et al (2016a); Srivasta et al (2022); Lim et al (2023); Fan et al (2023)</td></tr><tr><td>Goal Setting</td><td>Clicking on schedules, learning objectives pages</td><td>15</td><td>28%</td><td>Sun et al (2023); Hatala et al (2023); Fan et al (2022c); Saint et al (2020b)</td></tr><tr><td>Making Personal Plans</td><td>Clicking on a planer or timer; Time logs; Navigation logs;</td><td>22</td><td>42%</td><td>Fan et al (2022a); Saint et al (2020b)</td></tr><tr><td rowspan="2">Engagement</td><td>Working on the Task</td><td>Keystrokes, search engine, bookmarks, Copy/Paste/highlight, clicks on learning materials, mouse movement</td><td>26</td><td>49%</td><td>Wong et al (2019); de Barba et al (2020); Chen and Li (2021); Siadaty et al (2016a); Fan et al (2022c); Siadaty et al (2016b); Fan et al (2021)</td></tr><tr><td>Applying Strategy Changes</td><td>Reviewing activities, undoing actions, adding activity to existing learning path</td><td>20</td><td>38%</td><td>Siadaty et al (2016b); Fan et al (2021); Chen and Li (2021)</td></tr><tr><td rowspan="2">Evaluation and Reflection</td><td>Evaluation</td><td>Navigation logs; Accessing to dashboards</td><td>10</td><td>19%</td><td>Salehian Kia et al (2021); Saint et al (2020b)</td></tr><tr><td>Reflection</td><td>Feedback, formative assessment, discussion forums</td><td>22</td><td>42%</td><td>Ng et al (2023); Wang et al (2023)</td></tr></tbody></table>

integrate and elaborate on information when planning their tasks. In addition, task analysis can be observed from navigation logs, comprising opening organizers, guidelines, rubrics and schedules (Hatala et al, 2023; Salehian Kia et al, 2021; Nazeri et al, 2023; Poquet et al, 2023).

Planning through goal setting has been examined in several studies, using instruments that facilitate its capture (Sun et al, 2023; Hatala et al, 2023; Kizilcec et al, 2017; Siadaty et al, 2016a,b; Zhang et al, 2021; Fan et al, 2021; Salehian Kia et al, 2021; Saint et al, 2020a; Ng et al, 2023; Jovanović et al, 2021). In fact, the use of learning environments that facilitate explicit goal setting and updating through user-friendly interfaces offers insights into how learners actively manage their learning objectives (Siadaty et al, 2016a,b). For
instance, Sun et al (2023) have instrumented goal-setting capture by providing panels that involve learners in setting and monitoring their goals, and logging learners' mouse clicks on preset goals. Additionally, logging task-specific sub-goals within instructional designs allows for the detection of goal-setting behaviors based on learners' interactions with these sub-goals (Hatala et al, 2023; Salehian Kia et al, 2021).

Planning is also displayed when learners make personal plans. This is enhanced by the use of interactive tools to facilitate trace data collection. For example, Sun et al (2023) used a timer tool from which they captured the mouse movements of learners who wanted to display their time countdown. Sri-vasta et al (2022) captured clickstream from a planner tool that helps learners manage their time across various tasks, where learners allocate their time to tasks. Similarly, Salehian Kia et al (2021) and Fan et al (2023) captured planning phases through navigation logs recorded from learners' interactions with a planner tool. Navigation logs provide useful information on planning from learners' actions such as arranging activities, selecting strategies, or moving on to new topics (Fan et al, 2022a,b).

## Trace data capturing engagement

SRL engagement is depicted through two micro-level processes, working on the task and applying strategy changes (Siadaty et al, 2016c). There are many examples of studies that collect trace data of students working on the task. In fact, they account for 49% of our set of studies. They include (Chen and Li, 2021), which tracks activities such as downloading materials for offline practice, and (de Barba et al, 2020), which monitors participation in video lectures in a MOOC context by capturing learners' clickstream data indicating viewing of lecture videos. In (Lim et al, 2023), learner interaction is observed by logging the use of task-related tools, reading, and linking design and development in computer programming.

Navigation logs related to accessing worked examples and reviewing activities are records that show how learners apply strategy changes (Siadaty et al, 2016c; Lim et al, 2023). These include logs and clickstream data on revisiting modules after assessment (Ye and Pennisi, 2022; Kizilcec et al, 2017) or in preparation for exams (Zhang et al, 2021), editing content (Beheshitha et al, 2015), highlighting notes and tagging information or using bookmarks (Fan et al, 2022a,b, 2023), completing previously unfinished tasks, and revisiting and revising past content (Du et al, 2023b). In (Siadaty et al, 2016a), event logs of learner actions such as dragging and dropping or adding new activities into existing learning paths are used to observe strategy changes.

## Trace data capturing evaluation and reflection

Capturing evaluation is essential for understanding how learners monitor their learning in relation to their performance and set standards (Siadaty et al, 2016a,b). In this line, some studies capture navigation logs of learners monitoring reading and writing activities in relation to instructions or plans (Lim
et al, 2023; Siadaty et al, 2016a,b) or navigation logs of learners accessing assessment criteria pages and guidelines (Hatala et al, 2023; Salehian Kia et al, 2021). Other studies (Lim et al, 2023; Srivasta et al, 2022; Fan et al, 2022a,b, 2023) analyze how learners evaluate their work by verifying the accuracy of the content. Evaluation is also captured through dashboard access, facilitating peer comparison and self-assessment (Saint et al, 2020b,a; Han and Ellis, 2023; Jovanović et al, 2017; Ng et al, 2023).

Capturing reflection allows us to examine how learners assess their ability for self-reflection to adapt their learning strategies accordingly. Various studies have relied on capturing navigation logs to observe learners' reflective activities. For example, Siadaty et al (2016c) observed learners attempting formative assessments as a prelude to accessing content, suggesting a reflective assessment of understanding. In some studies, reflection is captured from learners submitting personal summaries (Ng et al, 2023) and comparing their approaches with those of experts, which denotes a higher level of self-evaluation (Wang et al, 2023). Reflection is also captured by logging learners sharing their learning experiences and providing comments on their competencies or learning paths (Siadaty et al, 2016a,b), and completing post-class discussion forums (Du et al, 2023a).

### 4.2.2 Indicators of SRL processes

Indicators of SRL are presented as metrics that reveal more generalizable relationships between SRL strategies, student behaviors and learning outcomes (Quick et al, 2020). Behavioral measures can provide good indicators of specific strategies of learners within a particular context. We have identified behavioral metrics that are mainly related to the planning and engagement phases, and more specifically to time management and working on the task (Quick et al, 2020; Feldman-Maggor et al, 2022; Suraworachet et al, 2023) (Table 5). Metrics that are representative of evaluation and reflection seem to be less explored (only 2 studies, Han and Ellis (2023); Quick et al (2020)). In addition, we have identified performance indicators that are used to examine the relationship between behavioral activities and learning outcomes (You, 2016).

#### *Behavioural metrics*

Behavioral metrics related to time management are usually used, including several temporal measures, such as the time spent on a platform, a task, or on assignments (Quick et al, 2020; Suraworachet et al, 2023). These measures are usually examined in relation to learning performance.

You (2016) showed that total viewing time did not significantly predict course performance, casting doubt on the direct predictive power of this indicator of academic success. Conversely, Cicchinelli et al (2018) demonstrated a direct effect of total time spent on the platform on self-regulation. Similarly, de Barba et al (2020) and Cerezo et al (2016) examined the effects between time invested and improved SRL skills and performance, suggesting that students who spend more time tend to have stronger SRL skills and achieve
<table><caption>**Table 5** Suggested behavioural metrics representative of SRL dimensions.</caption><thead><tr><th>SRL dimension</th><th>Metric</th><th>F</th><th>RF</th><th>Sample studies</th></tr></thead><tbody><tr><td>Time management</td><td>Session duration, total active days or weeks, largest period of inactivity, time spent on a platform or a task, total access time, time between first access and submission, time between first access and deadline, submission time from deadline, early or late submission</td><td>13</td><td>24.52%</td><td>Quick et al (2020); Suraworachet et al (2023); Kizilcec et al (2017); Ye and Pennisi (2022); Li et al (2022); You (2016); Yildirim and Gülbahar (2022); Goda et al (2015); Cerezo et al (2016)</td></tr><tr><td>Working on the task</td><td>Number of views of resources, proportion of content visits, number of students completing a course</td><td>9</td><td>16.98%</td><td>Quick et al (2020); Feldman-Maggor et al (2022); Schumacher and Ifenthaler (2021)</td></tr><tr><td>Evaluation</td><td>Frequency of Students' Interactions with a Dashboard</td><td>1</td><td>1.92%</td><td>Han and Ellis (2023)</td></tr><tr><td>Reflection</td><td>Number of views of assignment instructions after the deadline</td><td>1</td><td>1.92%</td><td>Quick et al (2020)</td></tr></tbody></table>

better results. Yildirim and Gülbahar (2022) found a significant positive relationship between engagement and time spent, highlighting the importance of time spent as a measure of engagement.

Behavioral metrics also include measures that indicate how learners engage in their tasks and are more specifically related to access to content (e.g. hand-outs, learning materials, videos, etc.) (Schumacher and Ifenthaler, 2021; Han and Ellis, 2023). The number of resource views is usually used (Quick et al, 2020; Feldman-Maggor et al, 2022; de Barba et al, 2020). It is, for example, used to assess a randomized control trial, to determine the effectiveness of administering prompts to support SRL (Schumacher and Ifenthaler, 2021). Content access indicators can also help to describe learner profiles (Rienties et al, 2019). Rodriguez et al (2021) used the proportion of video visits and the proportion of late content visits to profile learners, and characterized four types of behaviors.

Although evaluation and reflection are considered to be central dimensions in many SRL frameworks (Siadaty et al, 2016c; Zimmerman, 2002; Winne and Hadwin, 1998), there is a lack of studies investigating indicators in relation to these dimensions. In our set of studies, only one study considered an evaluation metric as the frequency of dashboard views to explore how learners evaluate their learning compared to peers (Han and Ellis, 2023). Another study suggested the number of views of assignments after the deadline as an indicator of self-reflection (Quick et al, 2020).
<table><caption>**Table 6** Performance metrics used throughout the literature.</caption><thead><tr><th>Performance metrics</th><th>Sample metrics</th><th>F</th><th>RF</th><th>Sample studies</th></tr></thead><tbody><tr><td>Scoring metrics</td><td>Overall performance scores, final/intermediate overall performance scores, teaching unit scores, participant scores, individual exam marks, other score aggregate</td><td>20</td><td>37.73%</td><td>You (2016); Quick et al (2020); Cicchinelli et al (2018); Molenaar et al (2021); Fan et al (2022c); Chen and Li (2021); Yildirim and Gülbahar (2022); Segedy et al (2015); Pardo et al (2017)</td></tr><tr><td>Completion metrics</td><td>Late submission score, number pre-deadline accesses, total assignment sessions, Overall number of submissions, number of attempts (per exercise), Completion rate</td><td>4</td><td>7.54%</td><td>You (2016); Cicchinelli et al (2018); Quick et al (2020); Goda et al (2015)</td></tr></tbody></table>

## Performance metrics

Performance metrics indicate how learners are performing on their assignments, including scores and grades, and completion rates (Fan et al, 2022c; Quick et al, 2020; Li et al, 2018; Han and Ellis, 2023). This led us to classify performance metrics into two main sets, scoring metrics and completion metrics (Table 6).

Scoring metrics are the most commonly used performance measures to assess learning performance (37.73%) and include quantitative measures indicative of learning success. These metrics collectively represent a learner's ability to achieve and maintain high-performance levels, reflected by both formative and summative assessments. These scores highlight a broader understanding of academic performance on different levels of granularity (e.g. on a course level, skill level, and exercise level), depending on the goal of the assessment being undertaken.

Completion metrics, on the other hand, focus on learner behavior in relation to the completion of tasks. These metrics emphasize process-oriented aspects of learning, such as learner effort (total number of attempts) (Molenaar et al, 2021), and learning pace (i.e. completion rate) (Goda et al, 2015). These metrics are typically used not only to assess learner's performance, but also to measure their engagement patterns and predict potential outcomes based on their diligence and interaction levels (You, 2016; Cicchinelli et al, 2018; Quick et al, 2020; Goda et al, 2015). Some of these metrics incorporate temporality to provide insight into learners' work habits and adherence to deadlines (Quick et al, 2020). The use of completion metrics is significantly lower than scoring metrics, accounting for 7.54% of studies.
Performance metrics are often interpreted to classify learners with different profiles based on their performance (e.g., competent students, cognitively oriented, behaviorally oriented, minimally self-regulated (Zheng et al, 2020)) by examining the relationship between learner behavior and learning outcomes (Rodriguez et al, 2021; Cerezo et al, 2016; Han and Ellis, 2023; Rienties et al, 2019). In addition, learner performance can be predicted based on behavioral and performance metrics (Li et al, 2018; Cerezo et al, 2016; Schumacher and Ifenthaler, 2021). It also appears meaningful to perform cross-analysis of these two types of metrics to understand the effort made by learners to achieve the observed level of performance.

## 4.3 RQ3. Data analysis methods

Methods used to conduct analyses of trace data related to SRL measurement are now described. It is suggested hereafter to distinguish between unsupervised methods aiming at discovering patterns in unlabelled data, and supervised methods leveraging labeled data to predict links between the observed features and a target variable representing a learning outcome (Alloghani et al, 2020). Unsupervised methods aim at identifying patterns (data partitions, correlations, causal relations, etc.) from raw data. When a target label is attached to each data point, then supervised methods may be applied to infer a prediction function that links point descriptions to target labels that may correspond to performance scores or classes assignments.

### 4.3.1 Unsupervised methods

Interactions between learners and a digital platform are stored as trace data that may be of a tabular or a sequential form, the latter being predominant to capture the dynamicity of SRL behavior (see Section 4.2). From such raw data, many methods are used to capture statistical or structural patterns. Statistical patterns are identified by analyzing datasets and constructing models from them, while structural patterns result from the structure of objects and their relationships.

Clustering methods aim at reconstructing the inner structure of an unsupervised data set into compact and separable groups. For an SRL behavior analysis task, each cluster may be interpreted as a typical SRL behavior. The recourse to clustering methods is predominant in the studied articles. More precisely, most of the studies used clustering methods to identify SRL behaviors and student profiles (Li et al, 2018, 2020; Bourguet, 2022; Nazeri et al, 2023). Because they are easy to interpret, Hierarchical Agglomerative Clustering (HAC) and k-means algorithms are the most commonly used for this purpose. Expectation-Maximization (EM) and decision trees algorithms are also used but they are less common (Table 7).

To identify interesting patterns in unsupervised (generally sequential) data, process and sequence mining methods are also commonly used (Saint et al, 2020b; Quick et al, 2023). In the reviewed studies, First-Order Markov Model
(FOMM) is one of the most commonly used algorithms for process mining (Table 7) to identify order relations between SRL actions. Examples of studies that have used process mining, included Beheshitha et al (2015) to explore complex sequences of coded events, and investigate the relationship between students’ self-reported aptitudes and SRL strategies. Fan et al (2021, 2022c) used the FOMM algorithm to detect learning tactics from temporally ordered sequences of learning actions in learning sessions, based on which a clustering is performed. Similarly, Wang et al (2023) used process mining to detect the temporal characteristics of students’ SRL behaviors, before performing clustering on the behaviors. Chen and Li (2021) and Srivasta et al (2022) used sequence mining to reveal learning sequences on trace coded behaviors and strategies, and Cicchinelli et al (2018) analyzed behavior patterns and how they affect the course performance.

Statistical analysis is another major family of methods used to turn raw data into valuable knowledge about SRL behaviors. Multivariate statistical analysis, especially analysis of variance (ANOVA) and regression analysis is also widely used to determine correlation between features. For example, Rien-ties et al (2019) and de Barba et al (2020) examined differences between student profiles after identifying different clusters of students using ANOVA. Zheng et al (2020) examined the differences between groups in terms of students’ learning gains and perceived engagement in metacognitive regulation. Pardo et al (2017) examined the differences in terms of motivational, self-regulatory, and academic performance using ANOVA and regression analysis. After identifying patterns of learning, Li et al (2018) used regression analysis to examine significant behavioral indicators associated with course achievement. Statistical methods (t-test, Mann-Whitney U test, chi-square test, regression analysis) are at the heart of many studies to exhibit major patterns in raw data (Quick et al, 2020; Fan et al, 2022b; Suraworachet et al, 2023; Du et al, 2023a; Siadaty et al, 2016b). As an example, Tabuenca et al (2015) extract statistically significant patterns in time logs to understand how students learn along periods and to explore the impact on SRL, whereas Ternblad et al (2022) investigated the difference between self-reported SRL feedback and the recorded feedback using Mann-Whitney U test. Causal relations may also be exhibited from raw data as, in Goda et al (2015) where categories of learning types are put into perspective with learning outcomes.

Exploratory data analysis and statistical measures are often used to describe patterns and trends lying within the data. For example, Siadaty et al (2016a) investigated the associations between learners’ use of the proposed scaffolding interventions and their engagement in SRL processes. Similarly, Salehian Kia et al (2021) investigated the extent to which observed indicators of SRL and students’ self-reported behaviors converged.
<table><caption>**Table 7** Most frequent analytic methods and algorithms.</caption><thead><tr><th>Analytic method</th><th>F</th><th>RF</th><th>Sample studies</th></tr></thead><tbody><tr><td>Exploratory data Analysis</td><td>9</td><td>17%</td><td>Siadaty et al (2016a); Salehian Kia et al (2021)</td></tr><tr><td>Multivariate Statistical Analysis</td><td>30</td><td>57%</td><td>Kizilcec et al (2017); Hatala et al (2023)</td></tr><tr><td>Process mining</td><td>11</td><td>21%</td><td>Saint et al (2020a); Fan et al (2023)</td></tr><tr><td>Sequence mining</td><td>6</td><td>11%</td><td>Cicchinelli et al (2018); Wong et al (2019)</td></tr><tr><td>Hierarchical Agglomerative Clustering (HAC)</td><td>11</td><td>21%</td><td>Li et al (2020); Cicchinelli et al (2018)</td></tr><tr><td>K-means</td><td>8</td><td>15%</td><td>Zheng et al (2020); Bourguet (2022)</td></tr><tr><td>First-Order Markov Model (FOMM)</td><td>8</td><td>15%</td><td>Saint et al (2020b); Fan et al (2021)</td></tr><tr><td>Expectation-Maximization (EM)</td><td>3</td><td>6%</td><td>Cerezo et al (2016); Srivasta et al (2022)</td></tr><tr><td>Decision trees</td><td>2</td><td>4%</td><td>Feldman-Maggor et al (2022); Yildirim and Gülbahar (2022)</td></tr></tbody></table>

### 4.3.2 Supervised methods

Data analysis methods leveraging supervised data capture a predictive model to link some observed feature values with a target feature that often materializes an indicator of performance.

Regression methods learn to predict a continuous value from a set of input observed values. They are predominant for extracting knowledge from supervised data, as the target feature is usually modeling a performance indicator rather than possible class assignments. Indeed, in You (2016), behavioral indicators of learning are used to predict online course performance. Dang and Koedinger (2020) attempted to capture temporal fluctuations in student motivation through self-regulated behaviors. Schumacher and Ifenthaler (2021) predicted students' learning performance on the basis of their online behavior and study-related characteristics. Kizilcec et al (2017) investigated self-regulatory strategies that predict the achievement of personal course goals.

Prediction can also rely on the result of clustering to predict the performance of groups of students based on their characteristics and behaviors. Regression analysis is used to predict students' final grades or learning measures based on their learning behavior data (Segedy et al, 2015; Rodriguez et al, 2021; Yildirim and Gülbahar, 2022; Ye and Pennisi, 2022). Multivariate statistical analysis is used to examine differences in goal achievement between groups and to test the predictive validity of clusters in predicting students' final grades Cerezo et al (2016).

Although they do not fall in the category of supervised data analysis methods, statistical techniques have been used, but in lower proportion (only 3 studies out of 53), to estimate the value of indicators expressing the effort made by learners to self-regulate in adaptive learning environment Molenaar et al (2021). In the same line, Zhang et al (2021) provided an estimate of the
effect of COVID-19-induced campus closure on students' SRL behavior in an online learning environment. Finally, Li et al (2022) estimated the effects of providing students with analytics of their effort, along with analytics of the effort and performance of relevant peers, on their outcomes.

## 4.4 RQ4. Learning scaffolds for self-regulation

SRL scaffolding interventions were the subject of 10 out of 53 studies. This revealed a lack of studies focusing on the investigation of SRL scaffolds and learner support. The results showed that the types of scaffolding can take two main forms: Scaffolding intervention as recommendation (6 studies) and scaffolding intervention as feedback (4 studies).

The recommendation type of scaffolding is usually provided in the form of a prompt (pop-up box or window) with messages and suggested learning activity options (Srivasta et al, 2022; Lim et al, 2023) or a landing page with general guidelines and detailed specifications (Hatala et al, 2023). Recommendation scaffolds are usually provided to support meta-cognitive activities. Siadaty et al (2016a,b) proposed a system that allowed workplace learners to define goals, receive recommendations on which competencies to study next, receive recommendations on how to study competencies by receiving recommendations on learning plans and resources, and share experiences with colleagues in the workplace and receive updates on their progress. Srivasta et al (2022) investigated scaffolds suggesting meta-cognitive activities to be followed during the task. The meta-cognitive prompts were designed to initiate and support orientation, reading, monitoring of reading, writing, and monitoring of writing actions during the multi-source writing task. Hatala et al (2023) proposed a step-by-step approach to recommending how students should approach an assignment. General guidelines provide the learning objectives of an assignment and tell students how to approach the problem by dividing it into sub-tasks, indicating what knowledge, techniques, and additional resources to use.

Feedback scaffolds are typically informative messages designed to support student reflection (Rienties et al, 2019). One study investigated formative and summative feedback provided to students manually by tutors and via email to support reflective writing engagement (Suraworachet et al, 2023). Sun et al (2023) presented an adaptive and personalized learning system that acts as an intelligent tutor, where students can monitor their performance through both AI-enabled radar charts and feedback, and decide to retake the test with questions based on their performance status. Rienties et al (2019) explored a system that provides scaffolds that allow students to break down the learning into subsequent steps, and they are continuously provided with interactive feedback, and opportunities to choose the types of learning resources that may best fit with their preferred learning strategy. Ternblad et al (2022) investigated a system that provides students with informative text-based feedback on selected errors and hints on how to correct them, after they have completed a task and had it automatically corrected. The feedback was designed to be
critical and constructive, providing scaffolding for students to do better when revising the task.

# 5 Discussion

We will now discuss our findings according to the research questions, highlighting the methodological and theoretical contributions of current research, as well as current limitations and remaining challenges to address.

## 5.1 On methodological foundations

It is clear that among current research on the measurement of SRL, understanding learners' behavior to build categories of learners, according to their behavior and performance, is currently a central focus. This result is supported by the findings of a recent systematic review (Damayanti et al, 2023), as well as foundational works on the importance of SRL profiling (Zimmerman, 2008; Pintrich, 2000; Perry et al, 2006; Boekaerts and Corno, 2005). This emphasis highlights the substantial impact that understanding student behaviors has on educational outcomes. Research highlights the necessity to understand the diverse learning strategies and behaviors students display in different educational contexts. By profiling student behaviors, educators can tailor interventions that promote SRL, foster a more supportive educational environment that adapts to individual needs, and thus improve learning outcomes through targeted feedback and support. There is a lack of studies that focus on investigating SRL scaffolds and learner support. However, the numerous studies on categorizing learners based on their SRL behavior is a first step towards enabling educators to tailor interventions and feedback to improve learning. From a methodological point of view, categorizing students enables the identification of groups of students who share similar behaviors, according to the examined available trace data and indicators.

There are different approaches to capturing trace data for SRL processes and support. Several studies demonstrated the need for digital learning platforms to integrate interactive tools that enhance learner interactions and recording (Sun et al, 2023; Srivasta et al, 2022). These platforms allow the instrumentation of SRL and the collection of a wide range of trace data, from clicks and navigation paths to time management and content interaction patterns, thus enabling the effective capture of SRL behaviors (Kizilcec et al, 2017; Sun et al, 2023; Hatala et al, 2023). However, instrumentation is typically embedded in online tools and features specifically designed to support the SRL process throughout domain-specific activities. Within these tools, instrumentation is embedded to track learner interactions, which requires student engagement with the tools from the outset. This suggests that SRL instrumentation should primarily focus on the practical value that the tool can offer learners in terms of supporting SRL and improving learning performance. A
legitimate question to ask is whether these tools really help learners to accom-
plish the tasks they are engaged in, without overloading them with additional
tools that are not necessarily relevant.

Investigating SRL behaviors from trace data requires the use of machine
learning methods and statistical analysis enabling student profiles interpre-
tation (Segedy et al, 2015; Quick et al, 2023). In addition, process mining
allows the identification of student behaviors by uncovering SRL processes
from trace data (Saint et al, 2020b; Fan et al, 2022c,a). The aim is often to
determine the relationship between observed variables (i.e. the behavior) and
their effect on pedagogical success for instance. These methods are promising
and lead to relevant results, demonstrating an advanced state of research in
SRL measurement and a significant methodological input for researchers and
practitioners (Srivasta et al, 2022; Cicchinelli et al, 2018). As methods such as
process mining, sequence mining, and machine learning methods demonstrates
their importance in uncovering interesting patterns of behaviors indicative
of learning success or failure, it remains as important as to pre-process the
data prior to its analysis. The extract, transform, and load pre-stage aims at
minimizing potential biases and noise that can stem from experimental fac-
tors that control the data collection. On this note, there is a lack of studies
that highlight pre-defined and reusable data pipelines that cleanse the data
from such potential noise. In addition to pre-treatment administered for data
cleaning purposes, additional pre-treatments are required to ensure the right
usage of analytic methods that rely on mathematical assumptions that need
to be fulfilled to ensure a reliable analysis of the data. For instance, statisti-
cal methods such as ANOVA and regression analysis have model assumptions
that must be met; violations of these assumptions can undermine result valid-
ity. Bias and representativeness in the data must be carefully monitored
to prevent skewed or non-generalizable findings. Techniques such as dimen-
sionality reduction may be necessary to manage high-dimensional data and
improve interpretability while reducing complexity. For clustering methods
like Hierarchical Agglomerative Clustering (HAC), ensuring that clusters are
interpretable and meaningful is essential. Overfitting should be avoided by
validating models with new data, and ethical considerations related to data
privacy and security must always be prioritized. Since these procedures are not
always reported in studies, it is crucial that they remain an integral part of the
research methodology to ensure an accurate and appropriate data analysis.

## 5.2 On theoretical foundations

Model-driven approaches for SRL measurement are prevalent in current
research, serving as robust theoretical frameworks that elucidate SRL pro-
cesses and dimensions. This approach provides clarity on how SRL phases,
strategies, and processes operate, offering pathways to align trace data collec-
tion with specific SRL processes. Such models aid in capturing and mapping
SRL processes effectively, reflecting the widespread usage and theoretical
significance of these models (Zimmerman, 2002; Pintrich, 2000; Winne and Hadwin, 1998; Siadaty et al, 2016c).

SRL theoretical models can help to define trace-based protocols enabling the transformation of event-based interactions exhibited by learners throughout their learning, into SRL processes. In addition, the models have the potential to define intervention frameworks that link SRL processes to different types of scaffolding interventions. They therefore facilitate not only the trace-data collection but also the design of interventions for each SRL phase. These models can be used for two main objectives: 1) diagnosis of SRL processes (e.g., for student profiling), and 2) design of tailored interventions at different levels of SRL processes (e.g. tailored interventions for the planning process, engagement process, evaluation, and reflection process). Therefore, practitioners can examine the SRL processes responsible for students’ poor performance, and narrow the focus on the micro-level SRL dimensions that have enacted such failure, which thereby provides more insights into what targeted intervention they should use to address these issues.

## 5.3 Limitations

This section identifies the main limitations of the studies mentioned in this article.

### 5.3.1 Predominance of higher education as a research context

The studies showed a predominance of undergraduate higher education as an authentic setting, as opposed to primary and secondary education or vocational settings. This is likely to be due to the complexity of learning tasks and the availability of autonomous learning environments in higher education. The higher education setting also provides a rich context for the use of digital learning environments such as LMSs, with a sufficient sample size of participants (up to 500 participants) to allow extensive data collection for empirical studies. This research context provides more appropriate conditions for defining experimental protocols, as opposed to the research context of young children, which requires appropriate methods that may further complicate ethical compliance (Parekh et al, 2021; Wadhwa, 2021). In this respect, some researchers emphasized the importance of supporting SRL at a young age (Saint et al, 2022; Braund and Timmons, 2021; Korucu et al, 2022; Cuar tas et al, 2022), suggesting the need to extend the research to include primary and secondary education, in order to contribute to the development of basic SRL skills, which are crucial for lifelong learning. Researchers can develop and test early interventions designed to foster SRL skills from a young age, such as classroom-based programs and gamified learning experiences. By demonstrat ing the impact of these interventions on long-term learning outcomes, they can build a strong case for broader adoption and investment. Collaboration with schools, parents, and policymakers can also amplify the reach and effective ness of such efforts. Practically and easily implementing ethical protocols to
protect younger participants can be achieved through several straightforward
steps.

### 5.3.2 Difficulty in replicating studies

The difficulty of study replication is due to the lack of documentation of the data collection process and the unavailability of reusable data (Winne and Perry, 2000). This can help to ensure the validity of the information captured from raw data events and provide a deeper understanding of the semantics underlying SRL processes. The lack of data benchmarks and datasets can be explained by the sensitive nature of the data (data privacy) (Siemens and Bajer, 2012).

As more tailored approaches are needed to open up research data to enable replication and comparative studies while respecting data protection and ethics (Fischer et al, 2020), the major barrier to sharing educational data for research is its sensitive nature, often containing personal and performance-related student information. Legal regulations like the General Data Protection Regulation (GDPR) for Europe, and the Family Educational Rights and Privacy Act (FERPA) for the United States, mandate strict data handling practices, including anonymization, which can reduce data utility and is difficult to achieve without risking re-identification. Ethical concerns and fear of data misuse further discourage and hinder comparability. To overcome these challenges, researchers must develop standardized, privacy-compliant data protocols, adopt advanced anonymization techniques, collaborate with legal experts, and establish secure data-sharing frameworks. Improved data governance, ethical practices, and cross-institution collaboration can foster data accessibility and enhance research applicability.

### 5.3.3 Lack of research on indicators for SRL support and scaffolding

The research argues for indicators that can capture real-time changes in learner behaviors and strategies, which are essential for providing timely and accurate feedback to learners (Azevedo et al, 2010). However, the indicators we identified are primarily used for behavioral analysis rather than providing actionable insights for learners. They are mainly behavioral measures that do not result from complex calculations (e.g. mean, frequency, total). They are mainly used for two purposes, hypothesis testing (Schumacher and Ifenthaler, 2021) and student profiling (Rodriguez et al, 2021; Li et al, 2018; Han and Ellis, 2023). They allow us to investigate the relationship between SRL processes and learning performance. Furthermore, there is a lack of studies that focus on investigating SRL scaffolds and learner support. The studies that have focused on scaffolding have examined the effect of interventions in the form of prompts and feedback to support meta-cognitive activities (Srivasta et al, 2022; Lim et al, 2023; Rienties et al, 2019). No relationship has been highlighted between the indicators we identified and these scaffolds. This can
be explained by the study's objectives. Given that most studies focus on lever-
aging SRL measures for analyzing SRL behaviors and student profiles, the
measures, indicators, and analytic methods are designed and used for analysis
only, not to provide support.

5.3.4 Validity of indicators without contextualization

One may observe that, based on the same indicator, orthogonal conclusions
may be drawn. It highlights potential questions about the reliability of the
instruments used to capture such data, as well as the nature of the data itself.
This variability poses significant challenges when attempting to draw objective
conclusions about the relationship between indicators and SRL processes. To
address these issues, a thorough examination of methodological constructs and
a more detailed description of the experimental context are necessary, includ-
ing how these indicators are computed and whether they accurately capture
the intended aspects of SRL behaviors. Ensuring robust and reliable instru-
mentation requires clear definitions, consistent measurement approaches, and
validation processes that confirm the alignment of indicators with the theo-
retical constructs of SRL. These efforts are crucial to improving the reliability
and interpretability of SRL-related data in digital learning environments.

5.4 Possible research axis to tackle limitations

Learning tools that support SRL by providing real-time feedback, should encourage students to plan and monitor their progress, reflect on feedback, and revise their work iteratively. Their features must be closely aligned with the needs of the learning tasks, helping students to understand their tasks, improve their work, and self-assess their progress. These tools should provide practical and domain-specific value so that students find them useful and engaging, and use them regularly to improve their performance, and therefore various forms of instrumentation can be incorporated. These include tracking progress toward learning objectives, detailed revision logs to record changes during the learning process, and reflection prompts to encourage metacognitive awareness through self-assessment. This statement prompts an interesting question: how to integrate tools to capture SRL data in a transparent way for the learners so as to avoid breaking their engagement in the education task?

Furthermore, SRL behaviors are dynamic and context-sensitive, meaning
analyses must account for these changes to avoid static interpretations. Inte-
grating multiple data sources can offer a more holistic perspective and enhance
the robustness of insights gained from data mining methods in SRL measure-
ment. This implies that future research in SRL measurement using data mining
and machine learning methods should focus on refining data quality, ensuring
representativeness, and maintaining ethical standards throughout the data col-
lection and analysis processes. Researchers should prioritize the development
of robust data preprocessing and validation protocols to enhance the accu-
racy and reliability of their findings. Future work should also explore dynamic
models that can capture the evolving nature of SRL behaviors over time and across different contexts. And so the question could be: how to leverage available cutting-edge data mining and machine learning methods to faithfully capture the dynamicity of SRL behaviors? When categorizing student behaviors based on trace data, it is essential to ensure that the data collected are both accurate, fresh and reflect real behavior, to avoid flawed profiling and ineffective solutions. The process must also be ethical, respecting student privacy and ensuring informed consent, while transparently communicating how the data will be used. Careful attention is needed to avoid biases that could skew data interpretation and to ensure that diverse student groups are fairly represented. Equally important is an understanding that learning behaviors are shaped by specific contexts, such as subject matter and cultural norms, meaning that interventions must be contextually relevant and sensitive. The granularity of the data used also plays a significant role, as too much detail can be challenging to analyze, while insufficient detail may obscure critical nuances. This raises a question that involves both practical and philosophical aspects on the compromise between the granularity of the analyzed data and the grain of the learners categorization. How to ensure that the analyses are performed in a sufficiently fine grain to make useful recommendations considering the diversity of learner profiles without creating prejudicial ethical biases?

To obtain parental consent for SRL research, raising awareness about its importance through targeted campaigns can be effective. Clear, simple consent forms explaining study details and potential risks can facilitate understanding, with digital or school-based distribution simplifying the process. Age-appropriate explanations via engaging videos or presentations make research relatable for children. Less intrusive data collection methods, such as passive classroom observation or teacher-reported data, reduce the burden on young participants while preserving natural learning environments. It is therefore particularly important to combine technical analysis of SRL measurements with initiatives to communicate and promote the benefits of SRL analysis and scaffolding to increase the engagement of both learners and their parents, especially in primary education. A possible research direction of future works is: how to engage all the stakeholders in both the SRL measurement and scaffolding phases?

To sum up our discussion, a way to improve the usefulness of SRL indicators is to develop adaptive and context-sensitive measures to capture real-time changes in learner behaviors and in various education contexts, like task complexity and goals. Linking these indicators to real-time feedback can provide personalized prompts and strategies for better self-regulation. Research should focus on integrating dynamic scaffolds based on indicator values, expanding beyond simple behavioral measures to multi-dimensional data that reflect cognitive and metacognitive activities. Designing SRL measures that support both analysis and learner support, and fostering collaboration with educators can translate these insights into effective, practical systems for learners.
# 6 Conclusions and implications

In this paper, we have systematically reviewed 53 articles from 2015 to 2023 to investigate the current state of SRL measurement research in digital learning environments. In particular, our aim in this review is to identify the current research trends in empirical studies, the context in which they take place, who is involved, and what the theoretical underpinnings of the studies are. In addition, we aim to provide a clear scheme of the trace data collected in relation to the SRL dimensions and the type of metrics used to measure SRL. We are also interested in a clear overview of the analytical methods used to analyze the trace data. Finally, we want to know if the studies aim to go beyond measurement to support SRL.

We have identified an increasing number of empirical studies, particularly in the post-COVID-19 pandemic period 2020-2023, seeking to leverage learning analytics to capture and examine SRL processes. This reflects the increasing interest in this research area and the continuing desire to explore analytical methods and statistical measures. In this respect, current research has brought methodological and theoretical insights to understand the processes of SRL, mainly by studying students' behaviors and profiles. While this seems to be the predominant direction of research, other research is developing and focusing on other objectives. These include studies of the effect of SRL on learning performance, the effect of specific learning activities on SRL, the effect of SRL scaffolds on learning, the alignment of students' self-reported SRL aptitudes with digital trace measures, and finally, proposed frameworks for trace-based analysis of SRL. Although current research is mostly conducted in higher education contexts at the undergraduate level, primary and secondary education as well as professional settings also provide experimental contexts for conducting empirical studies. Higher education remains the main area of study, as it offers researchers the opportunity to collect meaningful data from digital learning environments, using learning activities that may require SRL support. It also provides a sufficient number of participants for statistical analysis.

An important methodological outcome is the analytical methods used to achieve the research objectives. This systematic review revealed a broad landscape of unsupervised and supervised data mining methods for measuring SRL, advancing the state of research in this area. Because trace data doesn't have meaningful tags, forming unlabelled datasets, unsupervised methods are usually used, mainly to cluster datasets by discovering patterns. Clusters lead to typical SRL behaviors or students' profiles. As trace data is generally sequential, other advanced methods such as sequence and process mining are used to identify order relationships between SRL actions, from which clustering can be performed. In addition, statistical methods are used to provide useful interpretations and properties of the clusters, for example, to examine the effect of students' SRL behaviors on learning performance. Although labeled data is more difficult to obtain, as trace data requires an additional labeling process, some studies use supervised methods to make predictions.
These include predicting learning performance from behavioral indicators using regression methods.

Another important methodological research outcome is the integration of theoretically grounded SRL models to conduct the studies. Regardless of their nature, whether theoretical or data analysis oriented, SRL models provide a framework for mapping trace data to SRL processes and dimensions according to SRL theory. They are also used to map learning activities into SRL phases, thus facilitating SRL measurement. This predominant use of models leads us to qualify research on SRL measurement as guided by a model-driven approach.

## 6.1 Limitations of this review

The main limitation of this review is the literature search strategy. Although we did not specify online and blended learning settings in our search terms, our search procedure yielded empirical studies that examined trace data collected in digital learning environments. Other types of studies conducted in different settings may provide valuable insights with different types of data, but we decided not to include them.

The second limitation of this review arises from its findings. While the results primarily highlight the predominance of digital platforms in higher education, they provide limited insights into other educational contexts, such as primary education, which may restrict the generalizability of our conclusions.

## 6.2 Implications

This work has academic and practical implications. We provide a broad landscape of the context in which research is taking place, the trends that are emerging, and the analytical methods that are being used. However, we believe that many challenges and opportunities remain for future research. The use of learning analytics in SRL measurement demonstrates the potential to develop tools that can handle complex, multivariate data to uncover SRL behaviors and student profiles and make predictions about learning performance. This requires platforms to go beyond data collection and incorporate powerful learning analytics tools that can process the data to provide actionable insights and ensure alignment with educational goals. In addition, making data available and providing comprehensive documentation of data collection and analysis processes will enable comparability and replication of research. This is a real challenge for future research in terms of data protection.

# 7 Declarations

## *Competing Interests*

The authors declare that they have no competing interests.
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