Encyclopedia
Please note this is a comparison between Version 1 by Indria Handoko and Version 2 by Vivi Li.
The COVID-19 pandemic has caused educators around the world to access online-learning systems. Applying the online system involves challenges, such as the students’ need to cope with changes in their learning process, where they must develop capabilities to manage their learning more independently. Self-Regulated Learning (SRL) is an approach considered to help us understand students’ ability to manage their learning strategies and achieve improved performance.
- self-regulated learning
- clickstream data
- higher education
- learning management system
- Indonesia
1. Introduction
The COVID-19 pandemic triggered extreme changes in almost all human activities, including in higher education. Strict social distancing regulations exerted by governments around the world forced higher education courses to become fully remote, using online learning formats immediately. One of the changes, referred to as emerging distance education, has inevitably caused several issues involving digital infrastructure, e-course materials, e-evaluation methods, and a lack of students’ motivation [1][2][3][4][5]. Higher education institutions globally responded to the sudden changes by developing several approaches (both technical and non-technical) to facilitate e-learning processes. Indeed, the shocking COVID-19 crisis motivated educational institutions as well as governments to generate innovations in e-learning systems that were as effective as possible. In this regard, the government in many countries faced complex and urgent educational issues that needed quick solutions [4]. Amongst the urgent issues was the inequality of access to education that became more visible during the pandemic in developing countries [6], including Indonesia [7][8].
The COVID-19 pandemic triggered extreme changes in almost all human activities, including in higher education. Strict social distancing regulations exerted by governments around the world forced higher education courses to become fully remote, using online learning formats immediately. One of the changes, referred to as emerging distance education, has inevitably caused several issues involving digital infrastructure, e-course materials, e-evaluation methods, and a lack of students’ motivation [1,2,3,4,5]. Higher education institutions globally responded to the sudden changes by developing several approaches (both technical and non-technical) to facilitate e-learning processes. Indeed, the shocking COVID-19 crisis motivated educational institutions as well as governments to generate innovations in e-learning systems that were as effective as possible. In this regard, the government in many countries faced complex and urgent educational issues that needed quick solutions [4]. Amongst the urgent issues was the inequality of access to education that became more visible during the pandemic in developing countries [6], including Indonesia [7,8].
Indonesia, as the fourth most populous nation globally, still lacks adequate human capital qualities [9][10] and has inescapably faced the complex issue of unequal education access during the pandemic. As an archipelago country (with more than 17 thousand islands), Indonesia incorporates distributed location spread across the country. Within Indonesia, 122 areas are considered underdeveloped areas or have low economic growth, lack good public infrastructure (e.g., electricity and internet connections), and offer poor health services [11]. The Indonesian government named these areas as the Frontier, Outermost, and Disadvantaged, or 3T (which stands for ‘Terdepan, Terluar, Tertinggal’ in the Indonesian language). In this regard, 3T areas are also referred to as having territorial boundaries with other countries. In 3T areas, universities face various limitations regarding not only the quality of infrastructures but also the capacity and resources relating to qualified education and updated knowledge [10][11]. University students in 3T areas are thus considered the most vulnerable regarding equal access to education, particularly when the pandemic hit Indonesia [10][11][12]. The Indonesian government indeed has devoted efforts to equalize these areas, even before the pandemic began, by initiating several programs to reduce the gaps. For example, it installed physical infrastructure and internet connections in 3T areas [10]. However, while the program was still running, the pandemic broke out and forced the government to immediately create an integrated educational program across the nation. The Indonesian Directorate General of Higher Education, the Ministry of Education, Culture, Research, and Technology began the integrated program in March 2021, which involved both hardware (such as 4G internet connections and tablets for students) and software (systems). One of the programs is a learning-management system (LMS), namely ‘Sistem Pembelajaran Daring Pendidikan Tinggi’ (SPADA DIKTI (SPADA DIKTI is written as ‘SPADA’ throughout this entry)), an online learning system for higher education (see
Indonesia, as the fourth most populous nation globally, still lacks adequate human capital qualities [9,10] and has inescapably faced the complex issue of unequal education access during the pandemic. As an archipelago country (with more than 17 thousand islands), Indonesia incorporates distributed location spread across the country. Within Indonesia, 122 areas are considered underdeveloped areas or have low economic growth, lack good public infrastructure (e.g., electricity and internet connections), and offer poor health services [11]. The Indonesian government named these areas as the Frontier, Outermost, and Disadvantaged, or 3T (which stands for ‘Terdepan, Terluar, Tertinggal’ in the Indonesian language). In this regard, 3T areas are also referred to as having territorial boundaries with other countries. In 3T areas, universities face various limitations regarding not only the quality of infrastructures but also the capacity and resources relating to qualified education and updated knowledge [10,11]. University students in 3T areas are thus considered the most vulnerable regarding equal access to education, particularly when the pandemic hit Indonesia [10,11,12]. The Indonesian government indeed has devoted efforts to equalize these areas, even before the pandemic began, by initiating several programs to reduce the gaps. For example, it installed physical infrastructure and internet connections in 3T areas [10]. However, while the program was still running, the pandemic broke out and forced the government to immediately create an integrated educational program across the nation. The Indonesian Directorate General of Higher Education, the Ministry of Education, Culture, Research, and Technology began the integrated program in March 2021, which involved both hardware (such as 4G internet connections and tablets for students) and software (systems). One of the programs is a learning-management system (LMS), namely ‘Sistem Pembelajaran Daring Pendidikan Tinggi’ (SPADA DIKTI (SPADA DIKTI is written as ‘SPADA’ throughout this paper)), an online learning system for higher education (see
(accessed on 5 December 2021)). The courses in SPADA thus far had been developed not only by the Ministry of Education, but also by some universities that participated in the program. The main purpose of SPADA is to increase equality in qualified higher education in Indonesia (see
https://spada.kemdikbud.go.id/berita/apa-itu-spada-indonesia (accessed on 5 December 2021)) [10]. SPADA is expected to help optimize university students’ learning process and outcomes during the pandemic, thus reaching a high level of impact on students’ learning behaviors. Considering that an e-learning process entails specific challenges for students, more attention should be devoted to helping students be successful in their learning process [1][2][4][13].
Some recent scholarly works have attempted to address the issues of the sudden shift to learning online due to the COVID-19 pandemic. For instance, Ref. [5] investigated the effects of applying online learning during the pandemic on students’ academic performance. Their study findings in comparing academic performances between students in specific groups reveal that good performers are negatively impacted by the shift, while the lower performers are not clearly conclusive. However, although the study involved 500 students, it mainly examines one particular course. Similarly, Ref. [14] discussed how online learning was applied in a sport course during the pandemic, including its advantages and challenges. Despite their convincing and promising findings, the recent studies mostly discuss one particular course, and rarely have they explored the application of online learning approaches in more massive data involving broader areas and more varied courses. By investigating broader data and coverage, researchers can enhance their knowledge about how one online system utilized by many students in different conditions and areas at the same time can (or cannot) influence the students’ learning performance. The present research therefore attempts to address this limitation by analyzing the effectiveness of SPADA as a new online learning system that had been applied by students at the nationwide scale during the COVID-19 pandemic. Furthermore, by focusing on some underdeveloped areas, researchers explore to what extent the new system had facilitated the students’ learning processes. Regarding the issue of limited access to online methods in some underdeveloped areas, one study by [6] discusses how students in Sri Lanka with very limited access to online learning coped with their limitations during the pandemic. Unlike their study, which focused on engineering courses by applying a survey method, researchers' study adopts a different approach by using a more direct method (i.e., clickstream data) to examine students’ behavior when employing SPADA. In this way, researchers aim to complement the existing study by providing more accurate results on students’ behaviors.
One approach considered as the most essential to examine the effectiveness of online learning from the students’ side is Self-Regulated Learning (SRL) [15][16][17]. Adapting an SRL approach is arguably relevant to the context in which control of the learning process has been shifted from the educational institutions to students as individuals [4][17]. In the context of the COVID-19 pandemic, students not only face the multidimensional crisis of the pandemic (e.g., economic, social, health, and mental ramifications), but must also be responsible for their educational tasks or those that were previously carried out by the institutions (e.g., setting goals and managing the learning process). This extreme, consistently changing situation could be overwhelming to students [18][19]. Some students might be able to adapt and handle the situation well, while others might not [2][13][20][21]. The situation is becoming more challenging for students in underdeveloped areas, where access to internet infrastructure is a critical issue in digital learning. Therefore, understanding students’ SRL in online learning processes during the pandemic, especially in underdeveloped areas, is an important area of study [6][22]. This nascent phenomenon should be addressed in order to help students cope with the challenges they face and can thus help them improve their performance [17].
Previous scholarly works suggest that, by monitoring students’ SRL activities, educators or regulators can have a greater chance for success in implementing an online academic system [23][24][25]. Furthermore, as revealed by some scholars, the use of an SRL approach will be influenced by factors such as contextual and individual constraints, especially in terms of regulating self-motivation, cognition, and behavior [25][26]. This notion reveals the need to investigate the online system of SPADA regarding its role in promoting SRL, particularly in the context of underdeveloped or 3T areas. With more constraints (such as the lack of internet connection, access to the platform, and a supportive educational environment) than in other areas, 3T students might have less opportunity to visit the platform regularly, which potentially can inhibit them from actively managing their learning process [16]. Such circumstances can also prevent them from developing SRL strategies [7]. The novel study by [22], for instance, shows the drop of academic performance of students in Cambodia as they adopting online learning during the pandemic. This suggests an urgent call to evaluate the implementation of SPADA, especially in terms of how the platform facilitates the monitoring of SRL activities at the early stage of its implementation. Failure to identify any potential problems on the platform might harm the students’ SRL processes in the future and the system’s effectiveness in general [16].
2. Self-Regulated Learning (SRL)
The basic idea of self-regulated learning (SRL) is that effective learners can effectively self-regulate themselves to achieve personal learning goals [27]. In online learning methods, when direct support and guidance from teachers are absent, a self-regulated system for students is becoming more critical [16]. SRL is defined as the strategies of students to manage their learning process by regulating cognition as well as resource management to control their learning [28]. By possessing SRL, students can actively set goals and make plans for their learning, monitor their learning process, and adjust their study plans [16][28]. In this way, SRL allows students to transform their mental abilities into academic performance [17]. In addition to dealing with individually directed forms of learning (e.g., seeking information and discovery learning), the SRL approach also deals with social forms of learning, such as seeking assistance from teachers, friends, and parents [29]. This suggests that the SRL approach not only offers advantages to students but can also be beneficial to lecturers (in the ways they interact with students) and the school (in developing school management) [30]. Study regarding SRL offers a great opportunity for educators to measure students’ self-regulatory processes and how they construct their knowledge [30][31]. The proactive use of processes among students in the SRL concept has encouraged scholars to develop a number of instruments to assess SRL [16][32][33]. Regarded as ‘an overarching construct that captures how students direct and monitors their own learning processes and progress’ [32] (p. 3), SRL comprises three constructs: metacognitive, motivational, and behavioral [30]. The metacognitive construct involves goal setting and planning, organizing and transforming, information searching, rehearsing, and memorizing. The motivational construct includes self-evaluation and consequences. The behavioral construct includes environmental structuring; keeping records and monitoring; reviewing texts, notes, and tests; and seeking helps from teachers, parents, and friends [30] (p. 168). This approach suggests that the activities of SRL strategies comprise a complex interrelationship between cognitive, metacognitive, and motivational regulatory aspects [34]. In this sense, each student is responsible for constructing his or her own meanings, goals, and strategies based on information from either external or internal (one’s own mind) environments [28]. Furthermore, Pintrich [35] coined three types of SRL strategies: planning, monitoring, and regulating. Under the planning strategy, students prepare their cognitive strategy and organize and understand the materials by setting study goals, skimming a text, and addressing critical questions before reading through the text. Monitoring strategy refers to how students become aware of any distractions from their goals and then find ways to address them by using regulation strategies. Monitoring activities involve tracking of attention while reading the text or listening to lectures, self-testing on the material, and utilizing test-taking strategies during an examination period. Regulating strategy involves students rereading some parts of the material or asking themselves questions to check their own understanding of the material. Considered together, SRL activities include overviewing or orienting tasks and resources, making plans, evaluating the learning results, and monitoring or controlling all activities [27].3. Clickstream Data (CSD)
The use of technology in education, particularly online-learning systems, helps researchers understand students’ SRL behaviors [32]. The measurement of digital learning suggests invaluable data through which lecturers or administrators can observe students’ learning behavioral patterns in real time [36]. This approach, known as the learning analytics technique, involves the collection of data produced by students when they learn and improve the learning [16], called clickstream data (CSD). CSD refers to the detailed logs of time-stamped actions from individuals interacting with an LMS that typically consists of events that the user initiates, such as navigating between web pages, downloading and uploading files, or clicking play on a video [37]. The CSD method is regarded as more beneficial to understanding SRL than the traditional self-reported data, because many students often deal with bias and find it difficult to recall past experiences. CSD helps researchers to collect timely and objective information about how students interact with online education resources, thus promising more objective and richer insight into the learning experience than many other methods can offer [32]. One major line of research on using CSD addresses the measurement of students’ SRL behaviors to gain a better understanding of the students’ learning, through which researchers can support how to improve their SRL strategies [38]. In this regard, CSD comprises detailed logs of students’ time-stamped actions when using the LMS platform or capturing the mechanical aspects of student behaviors, such as the overall level and frequency of activity, the temporal patterns of students’ online activity (both individually and relative to other students), and choices of which online resources students access [15]. Furthermore, analyzing CSD involves two main strategies: (1) aggregate non-temporal representations, and (2) time-dependent or sequence-dependent representations [32]. While the first strategy collects information over time in aggregate, the second retains the information of students’ behavior in more detail by capturing sequential or temporal aspects. The first approach offers data that can be analyzed using a multitude of statistical methods (e.g., multivariate regression to predict outcomes), but it does not present the sequential aspects of students’ behaviors. The second approach provides more nuanced temporal patterns of students’ behavior, but it is more complicated than the first method [32]. The present entry attempts to capture SPADA clickstream data first by using both approaches and then examining how the data can be used to measure students’ SRL. The approach is expected to help enhance researchers' understanding of the massive implementation of LMS on students’ SRL, such as the nationwide use of the SPADA platform. (accessed on 5 December 2021)) [10]. SPADA is expected to help optimize university students’ learning process and outcomes during the pandemic, thus reaching a high level of impact on students’ learning behaviors. Considering that an e-learning process entails specific challenges for students, more attention should be devoted to helping students be successful in their learning process [1,2,4,13].
Some recent scholarly works have attempted to address the issues of the sudden shift to learning online due to the COVID-19 pandemic. For instance, Ref. [5] investigated the effects of applying online learning during the pandemic on students’ academic performance. Their study findings in comparing academic performances between students in specific groups reveal that good performers are negatively impacted by the shift, while the lower performers are not clearly conclusive. However, although the study involved 500 students, it mainly examines one particular course. Similarly, Ref. [14] discussed how online learning was applied in a sport course during the pandemic, including its advantages and challenges. Despite their convincing and promising findings, the recent studies mostly discuss one particular course, and rarely have they explored the application of online learning approaches in more massive data involving broader areas and more varied courses. By investigating broader data and coverage, we can enhance our knowledge about how one online system utilized by many students in different conditions and areas at the same time can (or cannot) influence the students’ learning performance. The present research therefore attempts to address this limitation by analyzing the effectiveness of SPADA as a new online learning system that had been applied by students at the nationwide scale during the COVID-19 pandemic. Furthermore, by focusing on some underdeveloped areas, we explore to what extent the new system had facilitated the students’ learning processes. Regarding the issue of limited access to online methods in some underdeveloped areas, one study by [6] discusses how students in Sri Lanka with very limited access to online learning coped with their limitations during the pandemic. Unlike their study, which focused on engineering courses by applying a survey method, our study adopts a different approach by using a more direct method (i.e., clickstream data) to examine students’ behavior when employing SPADA. In this way, we aim to complement the existing study by providing more accurate results on students’ behaviors.
One approach considered as the most essential to examine the effectiveness of online learning from the students’ side is Self-Regulated Learning (SRL) [15,16,17]. Adapting an SRL approach is arguably relevant to the context in which control of the learning process has been shifted from the educational institutions to students as individuals [4,17]. In the context of the COVID-19 pandemic, students not only face the multidimensional crisis of the pandemic (e.g., economic, social, health, and mental ramifications), but must also be responsible for their educational tasks or those that were previously carried out by the institutions (e.g., setting goals and managing the learning process). This extreme, consistently changing situation could be overwhelming to students [18,19]. Some students might be able to adapt and handle the situation well, while others might not [2,13,20,21]. The situation is becoming more challenging for students in underdeveloped areas, where access to internet infrastructure is a critical issue in digital learning. Therefore, understanding students’ SRL in online learning processes during the pandemic, especially in underdeveloped areas, is an important area of study [6,22]. This nascent phenomenon should be addressed in order to help students cope with the challenges they face and can thus help them improve their performance [17].
Previous scholarly works suggest that, by monitoring students’ SRL activities, educators or regulators can have a greater chance for success in implementing an online academic system [23,24,25]. Furthermore, as revealed by some scholars, the use of an SRL approach will be influenced by factors such as contextual and individual constraints, especially in terms of regulating self-motivation, cognition, and behavior [25,26]. This notion reveals the need to investigate the online system of SPADA regarding its role in promoting SRL, particularly in the context of underdeveloped or 3T areas. With more constraints (such as the lack of internet connection, access to the platform, and a supportive educational environment) than in other areas, 3T students might have less opportunity to visit the platform regularly, which potentially can inhibit them from actively managing their learning process [16]. Such circumstances can also prevent them from developing SRL strategies [7]. The novel study by [22], for instance, shows the drop of academic performance of students in Cambodia as they adopting online learning during the pandemic. This suggests an urgent call to evaluate the implementation of SPADA, especially in terms of how the platform facilitates the monitoring of SRL activities at the early stage of its implementation. Failure to identify any potential problems on the platform might harm the students’ SRL processes in the future and the system’s effectiveness in general [16].
2. Self-Regulated Learning (SRL)
The basic idea of self-regulated learning (SRL) is that effective learners can effectively self-regulate themselves to achieve personal learning goals [29]. In online learning methods, when direct support and guidance from teachers are absent, a self-regulated system for students is becoming more critical [16]. SRL is defined as the strategies of students to manage their learning process by regulating cognition as well as resource management to control their learning [30]. By possessing SRL, students can actively set goals and make plans for their learning, monitor their learning process, and adjust their study plans [16,30]. In this way, SRL allows students to transform their mental abilities into academic performance [17]. In addition to dealing with individually directed forms of learning (e.g., seeking information and discovery learning), the SRL approach also deals with social forms of learning, such as seeking assistance from teachers, friends, and parents [31]. This suggests that the SRL approach not only offers advantages to students but can also be beneficial to lecturers (in the ways they interact with students) and the school (in developing school management) [32]. Study regarding SRL offers a great opportunity for educators to measure students’ self-regulatory processes and how they construct their knowledge [32,33].
The proactive use of processes among students in the SRL concept has encouraged scholars to develop a number of instruments to assess SRL [16,28,34]. Regarded as ‘an overarching construct that captures how students direct and monitors their own learning processes and progress’ [28] (p. 3), SRL comprises three constructs: metacognitive, motivational, and behavioral [32]. The metacognitive construct involves goal setting and planning, organizing and transforming, information searching, rehearsing, and memorizing. The motivational construct includes self-evaluation and consequences. The behavioral construct includes environmental structuring; keeping records and monitoring; reviewing texts, notes, and tests; and seeking helps from teachers, parents, and friends [32] (p. 168). This approach suggests that the activities of SRL strategies comprise a complex interrelationship between cognitive, metacognitive, and motivational regulatory aspects [35]. In this sense, each student is responsible for constructing his or her own meanings, goals, and strategies based on information from either external or internal (one’s own mind) environments [30].
Furthermore, Pintrich [36] coined three types of SRL strategies: planning, monitoring, and regulating. Under the planning strategy, students prepare their cognitive strategy and organize and understand the materials by setting study goals, skimming a text, and addressing critical questions before reading through the text. Monitoring strategy refers to how students become aware of any distractions from their goals and then find ways to address them by using regulation strategies. Monitoring activities involve tracking of attention while reading the text or listening to lectures, self-testing on the material, and utilizing test-taking strategies during an examination period. Regulating strategy involves students rereading some parts of the material or asking themselves questions to check their own understanding of the material. Considered together, SRL activities include overviewing or orienting tasks and resources, making plans, evaluating the learning results, and monitoring or controlling all activities [29].
3. Clickstream Data (CSD)
The use of technology in education, particularly online-learning systems, helps us understand students’ SRL behaviors [28]. The measurement of digital learning suggests invaluable data through which lecturers or administrators can observe students’ learning behavioral patterns in real time [37]. This approach, known as the learning analytics technique, involves the collection of data produced by students when they learn and improve the learning [16], called clickstream data (CSD). CSD refers to the detailed logs of time-stamped actions from individuals interacting with an LMS that typically consists of events that the user initiates, such as navigating between web pages, downloading and uploading files, or clicking play on a video [27]. The CSD method is regarded as more beneficial to understanding SRL than the traditional self-reported data, because many students often deal with bias and find it difficult to recall past experiences. CSD helps us to collect timely and objective information about how students interact with online education resources, thus promising more objective and richer insight into the learning experience than many other methods can offer [28].
One major line of research on using CSD addresses the measurement of students’ SRL behaviors to gain a better understanding of the students’ learning, through which we can support how to improve their SRL strategies [38]. In this regard, CSD comprises detailed logs of students’ time-stamped actions when using the LMS platform or capturing the mechanical aspects of student behaviors, such as the overall level and frequency of activity, the temporal patterns of students’ online activity (both individually and relative to other students), and choices of which online resources students access [15]. Furthermore, analyzing CSD involves two main strategies: (1) aggregate non-temporal representations, and (2) time-dependent or sequence-dependent representations [28]. While the first strategy collects information over time in aggregate, the second retains the information of students’ behavior in more detail by capturing sequential or temporal aspects. The first approach offers data that can be analyzed using a multitude of statistical methods (e.g., multivariate regression to predict outcomes), but it does not present the sequential aspects of students’ behaviors. The second approach provides more nuanced temporal patterns of students’ behavior, but it is more complicated than the first method [28]. The present study attempts to capture SPADA clickstream data first by using both approaches and then examining how the data can be used to measure students’ SRL. The approach is expected to help enhance our understanding of the massive implementation of LMS on students’ SRL, such as the nationwide use of the SPADA platform.
