Educational environments fall along a continuum from physical classroom where face-to-face interactions are common to fully online learning environments where asynchronous interactions are the default. This continuum of educational environments has provided opportunities for blended and web facilitated courses where learning materials and student-instructor interactions are delivered online with little or no face-to-face meetings.
Presently, there is a recognized shift towards technology supported learning commonly known as e-learning with most of institutions of higher learning adopting e-learning for fully online courses or complementary to the face-to-face sessions in blended learning approach in order to curb the challenge of large backlog of students to be admitted. The term “online learning” or as commonly known as e-learning refers to the web-based systems such as massive open online courses (MOOCs) and learning management systems (LMS) that are used by instructors to deliver learning materials and allow students to access the content and interact and obtain support during a learning episode. MOOCs are defined as open education systems for open and distance learning where students register for courses with limited admission restrictions such as pre-requisite courses and selection criteria. Despite the benefits to online learning, existing literature indicates challenges that need to be addressed. First is offering adequate support and guidance to learners undertaking online learning. Offering individualized support and guidance may not easily be achieved because of large number of students enrolling on e-learning. The increased number of students taking online courses is likely to face the challenge of having enough human capacity to offer adequate support. To provide effective support and guidance to online students, we need to tap into the potential opportunities offered by educational data mining (EDM) and learner analytics (LA) tools. EDM is described as the approach of applying data mining algorithms on datasets generated from educational environments in order to understand learners and learning environments. The datasets which is in form of logs generated when learners engage to various online learning activities can be analyzed to produce inferences that can be used as indicators to provide interventions that reduce dropout rates and increase retention rates, profile learners, develop learner models, and recommender systems. LA on the other hand involves integration and analysis of data collected from educational environments for insights and patterns on how students engage on various learning activities during online learning. The main objective is to support students by providing interventions to improve on undesirable learning behaviors and reinforce positive learning (Lodge,panadero, Broadbent, & De Barba, 2019). Lodge and Corrin (2017) opine that LA provide opportunity for monitoring students’ learning in order to understand their behavioral patterns and provide real-time interventions especially in online learning envrionments. According to Naif, Ayman, & Saeed-ul (2019), the outcome from LA helps in understanding the behaviour of learners with a view of providing early intervention mechanisms that enhances learning engagement which has been found to be positively correlated to academic performcance. This is likely to lead to reduced student dropout and increase the retention rates especially in higher education. In comparison, while EDM is concerned about techniques that can be used to explore data from educational environments and using the techniques to understand learners and learning environments, focusing on automated discovery of information, LA is more about analyzing and reporting of insights hidden in the data about learners and learning environments, focusing on insights to “inform and empower instructors and students” (Siemens & Baker, 2012).
Compared to physical classroom teaching where learners are confined together at certain periods, online learners are not restricted in managing their own schedules and learning process—what time to study and how long to engage in learning. The success of e-learning depends on the learner’s ability to take control of their own learning process (Nikolaki, Koutsouba, Lykesas, Venetsanou, & Savidou, 2017). The theory through which learners take control of the learning process is referred to as “self-regu-lated learning (SRL)”. Self-regulated learners are those who have the ability to take charge in managing their own learning while assuming an active role in achieving their academic goals (Zimmerman, 1990).
SRL is grounded on different theoretical models that provide frameworks on which research studies on SRL are carried out. According to Carlos Núñez, Romera, Magno, and Panadero (2017), the popular and commonly referred models include Zimmerman’s,Boekaerts’, Winne and Hadwin’s, Pintrich’s, Efklides’, and Hadwin, Järvelä, and Miller’s models. Each of these models describes phases, processes, and components that can be summed up into SRL strategies that are measured in a learning process. The strategies include time management, metacognition, effort regulation, critical thinking, rehearsal, elaboration, organization, peer-to-peer learning, and help seeking. Leaners who employ some or all of the identified strategies perform better than those with low level SRL skills and hence the need for supporting SRL on e-learning environments especially LMS which are majorly used by higher institutions of learning (Broadbent & Poon, 2015; Kizilcec et al.,2017; Littlejohn, Hood, Milligan, & Mustain, 2016). These strategies can be measured before, during, or after a learning process using instruments and methods specially designed for each of the SRL model.
The purpose of the current study was to investigate the research trends in terms of SRL measurement and promotion for SRL online and establish research gaps between the period 2008 and 2018. From the review, it can be noted that measurement and promotion of SRL has greatly advanced from the use of traditional methods which relied on learner perceptions on their SRL skills and use of offline data to online measures such as the use of learner logs from e-learning environments. The findings from the review indicate that there is noticeable evidence of a recognizable shift from using tools that only measures SRL to tools that measure SRL while providing interventions that stimulates growth of SRL in learners during the learning process (Azevedo & Witherspoon, 2009; Barnard et al., 2009; Cho & Shen, 2013; Dawson et al., 2015; Delen et al.,2014; Onah & Sinclair, 2017; Siadaty, 2016; Winne & Hadwin, 2013).
The study provides an understanding on the research trends in terms of the tools and instruments used to evaluate and promote SRL online learning environments. In some studies, instructors were presented with processed information through data analytics dashboards. The instructors could utilize the analyzed information in offering support to students based of the processed information.
The visualized reports about learners’ behaviors are first delivered to instructors for their synthesis and interpretation so as to know how to provide individualized support to learners. While these tools support instructors to gain insights on how learning is taking place and allow them to customize SRL support to students, one issue need to be addressed: the large number of students enrolled on online courses and reliance on human judgments in the provision of interventions. This approach requires that instructors know how to interpret data and make human judgments before offering feed back to students. The increased number of students taking online courses could be an hindrance to human capacity of offer adequate support. Offering individualized feed-back to e-learning students may no longer be tenable as the number of learners is becoming large for tutors to guide them individually (Nussbaumer et al., 2015).
Researchers and educators there need to shift from instructor-centered support toa data-centered applications based data collected from various e-learning sources such as LMSs, MOOCs, PLEs, social education networks, and e-portfolios. From the review, the SRL measurement and promotion approaches can be categorized into two. First category is approaches that extend the decision making capability for teachers to be able to offer data-driven and personalized support to learners. These approaches take advantage of teachers’ knowledge and augment its information from the analyzed data. The second type of approaches are those that offer metacognitive feedback by making learners stop learning and reflect on the learning process and then proceed. Most of the existing studies focus on the first model. The increase in number of online learners however implies that it may not be easy for online instructors to interact with every student and provide individualized guidance and support. Researchers therefore argue that the effective way on how EDM can help promote SRL is the provision of individualized interventions through metacognitive feedback such as hints and prompts (Lodge et al., 2019). It can also be noted that there is developing potential of using EDM tools to provide measurement and interventions concurrently. Interventions, when implemented within the measurement tools, could play a significant role in stimulating the growth of SRL skills. Although this study identified an observational trend that the use of learning analytics and EDM in measuring and promoting SRL has started to emerge and now advancing, literature indicates that there is continued use of self-report tools that were originally created for the traditional face-to-face classroom set-up. According to Winne and Baker (2013), EDM can been used to identify, model, and predict learners’ behavior. It can also be noted that that the SRL measurement intervention tools have started to emerge.
So far, we have had separate tools for measuring and promoting SRL. Some of the studies that used LA as an alternative approach to measuring SRL also used the dashboard results from LA to promote SRL skills for learners on e-learning environments. This review indicates that EDM and LA are now being applied to establishing learner behaviors in online learning. Their guided implementation could lead to the development of various tools that are used specifically to mine education data generated from various learning environments including web-based systems such as LMS. The EDM tools accomplish various aspects of data preparation, modeling, processing, analysis, and visualizations. The tools ensure availability of visualized and interactive feedback on learner styles to both students and instructors. Real-time access toi visualized feedback will enhance continuous monitoring and support for the benefit of learners to self-regulate based on the personalized feedback received on LMS dashboard. External agents such as instructors will also be able to use the feedback to provide personalized support/scaffolding to learners. The impact will increase learner motivation, satisfaction, and better learning outcome. This will also enable researchers and instructors to detect, isolate, and engineer changes on e-learning environments that impact learners. Early interventions and support that can be offered to learners via EDM tools will lead to improved performance for learners and reduce drop-out rates and reduce the time learners take to graduate especially through online courses. The real-time visualized feedback from actual datasets can also be accessed by instructors to monitor how SRL skills for learners change over time.
Despite the challenges identified in this review, it has been established that self-report methods that were designed to measure SRL in face-to-face classroom setup are continuously being used to measure SRL in online learning environments. Although researchers argue that the popularity of self-report tools could be their reliability and validity that has been proven over the years (Roth et al., 2016), self-report tools are obtrusive in nature. When learners are prompted to provide their perceptions on their SRL skills, they may not only overestimate their responses but also fail to capture their actual study behaviors. Literature also indicates that self-report tools are usually modified or enhanced to fit the context of online learning environment while the items still remain same as those that were designed for face-to-face classroom settings. This denotes that continued use of self-report tools is likely to lead to situations where the measured SRL levels do not represent actual learner behaviors (Broadbent & Poon, 2015). According to Winne and Baker (2013), measuring metacognition and motivation faces the challenge of identifying the constructs that can be modeled especially when self-report tools are used. They argue that the instruments used to gather and process metacognition and motivation are unreliable and erroneous in noticing change of state of learners’ skills and that in most research studies, experimental data is usually manipulated in order to improve reliability of the instruments used.
It would also be important to observe that there is a lack of a model that can be used to implement the “third wave” of SRL measurement and promotion in higher education especially in online learning environments. The existing studies did not describe an EDM model for implementation except for one study that proposed a conceptual model (Araka et al., 2019). To address the challenges encountered when using self-report tools for measuring SRL in online learning environments, we propose the use of EDM techniques for measuring and promoting SRL as EDM relies on the use log or trace data collected from educational environments as indicators of SRL. Given the observed challenges, there is a need to develop a framework that helps to integrate the SRL measurement and promotion tools with EDM tools.
Conclusion
This study presents various tools and methods that have been used to measure and promote SRL for online learning environment for the last one decade. The potential of EDM in measuring and promoting SRL on e-learning environments has also been established. While there are challenges in measuring and promoting SRL strategies in online learning environments, some of them may be addressed through the implementation of EDM techniques. Effectively, the techniques need to be deployed on e-learning systems such as the popular learning management systems which are being used by most of institutions of higher learning to offer both blended and online course to students. Similarly, the EDM tools have the potential of capturing and analyzing real-time learner traces and present visualized feedback to learners and hence allowing continuous assessment of SRL. Learner scaffold can also be provided through learner dashboard. This implementation allows students to be supported and guided while studying online without the limitation of numbers of students enrolled (Araka et al., 2019). However, we found out that there is lack of a framework on how the EDM measurement and intervention models can be conceptualized and deployed on LMSs.
Additionally, this systematic review indicates SRL interventions have been important in improving or stimulating growth of SRL. Nonetheless, one critical issue that researchers need to address is the lack of a model that captures all the SRL strategies from the existing SRL conceptual models and maps them to the LMS data indicators from which SRL strategies for each learner can be inferred. In order to design a model that can be generalized, there is a need for further study on existing frameworks that are used to provide SRL interventions, nature of interventions, and indicators that were used in each study. This will then act as guide to understanding the nature and effectiveness of SRL interventions provided for each learner. According to Lodge et al. (2019), it is indispensable that e-learning environments provide tools to evaluate students, establish the levels of their individual engagements, and identify those who need interventions and reinforce each learner with the right kind of interventions.
Researchers in the field of SRL have started to embrace the use EDM and LA in measuring and providing SRL interventions. Future research should now focus on implementing EDM approaches that measure and provide SRL interventions in realtime. More interesting will be the integration of EDM tools into existing LMS for higher institutions of learning and MOOCs. This will enable collection, analysis, and provision of feedback to learners in real-time ensuring individualized support to learners. The educational data mining tools must provide solutions that should make use the rich and actual datasets from online learning environments to provide inferences on students’ levels of SRL skills while at the same time providing interventions for promoting SRL skills among learners.
Currently, most institutions of higher learning have adopted e-learning for online courses to curb the increased demand of higher education (Hadullo et al., 2018). How-ever, the numbers of instructors are not enough to provide the support in terms of self-regulated learning (Muuro, Wagacha, Oboko, & Kihoro, 2014). Consequently, there is a need to include other tools such as EDM which can be used to promote SRL with little human intervention. Even though such tools have been developed, from this review, there is a need to carry out more empirical evidence on the effectiveness of using EDM measurement and intervention models compared to human interventions in promoting learning in institutions of higher learning given the increased demand of higher education.
