Enhancing self-regulated learning in online environments: challenges and opportunities for recommender system algorithms

Self-regulated learning (SRL) can predict academic performance. Yet, it is difficult for learners. The ability to self-regulate learning becomes even more important in emerging online learning settings. To support learners in developing their SRL, learning analytics (LA), which can improve learning practice by transforming the ways we support learning, is critical. This scoping review is based on the analysis of 54 papers on LA empirical research for SRL in online learning contexts published between 2011 and 2019. The research question is: What is the current state of the applications of learning analytics to measure and support students' SRL in online learning environments? The focus is on SRL phases, methods, forms of SRL support, evidence for LA and types of online learning settings. Zimmerman's model (2002) was used to examine SRL phases. The evidence about LA was examined in relation to four propositions: whether LA i) improve learning outcomes, ii) improve learning support and teaching, iii) are deployed widely, and iv) used ethically. Results showed most studies focused on SRL parts from the forethought and performance phase but much less focus on reflection. We found little evidence for LA that showed i) improvements in learning outcomes (20%), ii) improvements in learning support and teaching (22%). LA was also found iii) not used widely and iv) few studies (15%) approached research ethically. Overall, the findings show LA research was conducted mainly to measure rather than to support SRL. Thus, there is a critical need to exploit the LA support mechanisms further in order to ultimately use them to foster student SRL in online learning environments.

The increasing development in smart and mobile technologies supports transformation of learning environment into a smart learning environment to meet diverse learners’ needs. Learning in an online environment requires motivation and skills to succeed. Self-regulated learning has been identified as one of the strategies for supporting students in an online learning environment. However, despite these innovations, there is a scarcity of well exploratory works to understand if students have skills, experiences, and technology resources for implementing a self-regulated smart learning environment. This study attempts to fill the gaps by investigating these experiences and technology resources. The study used mixed-method to investigate the readiness of 157 undergraduate students. The quantitative data used an online self-regulated learning questionnaire (OSLQ), and the qualitative data used focus group discussions. The quantitative data were analyzed using descriptive statistics and correlations, and the qualitative data used a thematic method to understand students’ experiences. The investigations show that students understood the self-regulated learning process and its’ role in their academic success. Moreover, they have access to mobile internet services and smart devices, mostly an android operating system. More so, goal setting, time management, and self-evaluation are among the top-level skills readiness, and there is a relationship between previous online learning experiences and these skills. Future research could investigate facilitators’ readiness to develop a self-regulated smart learning environment for engaging online learning experiences and skills development.

Numerous empirical studies, including meta-analyses, have confirmed the impact of self-regulated learning (SRL) on learners’ academic achievement in traditional or face-to-face learning environments. However, prior meta-analyses rarely examined the efficacy of SRL interventions on academic achievement in online or blended education across elementary education, secondary education, higher education, and adult education. Therefore, this meta-analysis addresses this research gap by focusing on the effect of SRL interventions on students’ academic test performance in online and blended learning environments in elementary, secondary, and higher education settings as well as informal settings. The present meta-analysis compares SRL phase, SRL scaffolds, and SRL strategies between treatment and control groups. We also investigated possible differential effectiveness due to substantive features of the included studies, such as different educational levels of learners (e.g. elementary, secondary, and higher education), academic subjects (STEM vs. non-STEM), and learning contexts (e.g. online learning, blended learning, web-based learning, mobile learning). Consistent with previously published meta-analyses, the present meta-analysis confirmed a positive and moderate effect of SRL intervention (ES = 0.69) on learners’ academic achievement in online and blended environments for learners in elementary, secondary, and higher education as well as informal adult education settings.

This study investigates the relationship between students' self-efficacy beliefs, goal-setting, and learning tactics in an online business course. Using sequence analysis and process mining techniques, we analyzed log data from 209 students to identify distinct learning tactics and their association with self-efficacy beliefs, inferred from initial goal-setting and final grades. Four learning tactics were identified, with significant differences observed between students with high and low self-efficacy beliefs. High self-efficacy students demonstrated more varied and adaptive tactics, including greater use of quizzes and project-focused activities. In contrast, low self-efficacy students and those who didn't set goals showed less engagement and fewer monitoring activities. The project-focused tactic had the strongest correlation with final grades and goal achievement. Goal-setting at the course's start was linked to more effective learning behaviors and better outcomes. The study reveals how students' online learning behavior changes based on initial goal-setting and subsequent reflection. These findings contribute to research on self-regulated learning in online environments and offer practical implications for designing online courses and learning analytics interventions. Understanding these differences enables the development of targeted interventions to improve learning tactics and self-efficacy beliefs among students, ultimately enhancing their ability to achieve learning goals in online educational settings.

The Framework for DIRECTED Online Learning Environments

Self-Regulated Learning (SRL) is a field in educational psychology that as attracted tremendous research studies from various communities including ICTs. Recent studies and meta-analysis have greatly contributed to the domain knowledge that the use SRL strategies boost academic performance for learners. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Additionally, most of these studies focus on SRL on Massive Open Online Course (MOOC) environments and only analyse SRL strategies with limited attention to the scaffolds or interventions employed to stimulate growth of SRL skills in learners. The modern Learning Management Systems (LMS) such as Moodle allow storage of datasets on student activities. Consequently, it is possible to use Educational Data Mining (EDM) to extract learner patterns which can be used to support SRL and explore how SRL strategies change over time and inform the instructor. In view of this, the research examines the current SRL measurement and promotion tools for e-learning environments and proposes a conceptual model grounded on EDM for implementation as a solution to measuring and promoting SRL strategies for learners within LMS.

Background: Self-regulated learning refers to the monitoring and controlling of one's own cognitive performance before, during, and after a learning episode. Previous literature suggested that self-regulated learning had a significant relationship with academic achievement, but not all self-regulated learning strategies exerted the same influences. Using an invalid strategy may waste the limited psychological resources, which will cause the ego depletion effect. The present meta-analysis study intended to search for the best self-regulated learning strategies and inefficient strategies for Chinese students in elementary and secondary school, and analyzed the critical phases of self-regulated learning according to Zimmerman's theory. The moderating effects of gender, grade, and publication year were also analyzed.Methods: Empirical studies which conducted in real teaching situations of elementary and secondary education were systematically searched using Chinese academic databases. Studies focused on undergraduate students, students of special education, or online learning environments were excluded. Fifty-five cross-sectional studies and four intervention studies (which generated 264 independent samples) were included with a total sample size of 23,497 participants. Random effects model was chosen in the current meta-analysis, and publication bias was also examined.Results: The results indicated that the overall effect size of self-regulated learning on academic achievement was small for primary and secondary school students in China. The effect sizes of self-efficacy, task strategies, and self-evaluation were relatively higher than other strategies. Self-regulated learning strategies have the largest effect size on science disciplines (including mathematics and physics). Performance phase and self-reflection phase are key phases of self-regulated learning. From 1998 to 2016, the effect size between self-regulated learning and academic achievement was gradually decreasing.Conclusions: The main findings of the current study showed that self-efficacy, task strategies, and self-evaluation were key self-regulated learning strategies for Chinese students. Performance phase and self-reflection phase played significant roles in the process of self-regulated learning. Future studies need to include more intervention studies with rigorous treatment fidelity control and provide more empirical evidence from online learning, so as to compare the different effects of self-regulated learning between traditional education and online education.

Many music educators are concerned about building healthy classroom communities as they prepare to transition to online or blended learning environments. Research involving distance education has indicated that several techniques in the design and delivery of online instruction can increase students’ sense of community, commitment to the course, and overall learning experience. The purpose of this research-to-resource article is to provide music educators with research-based strategies for establishing and connecting students to a sense of community in courses delivered partially or entirely online.

<p>Research on the effectiveness of self-regulated learning (SRL) strategies in mathematics is growing. SRL strategies are an effective way of learning mathematics in an online learning environment. Generally, many undergraduate students are not ready to study mathematics courses due to its complex nature. Lack of proper application of SRL when learning mathematics in an online learning environment can be a significant factor undermining student academic achievement and learning satisfaction. This research employed a true experimental design (Randomized control trial). The<span style="color: rgb(1, 0, 0);"> two </span>groups of students were randomly assigned to the experimental and control groups. The experimental group consists of fifty-seven students, while the control group consists of forty-four students. The result of the independent sample <em>t</em>-test analysis for the study revealed that the experimental group, which received the treatment of SRL strategy, achieved better outcomes and higher learning satisfaction during the learning period. The results of the present study have important implications for the application of SRL strategies in online learning environments to enhance students’ success in learning mathematics.</p>

Assessing participant learning in online environments provides benefits and challenges. Fortunately, the available technology tools allow for a wide range of assessment techniques.

Among the literature on self-regulated learning and social networking, the studies, which explore the impact of social networks on learning regarding connection sizes and relationship-establishing factors, are rarely seen in the context of social networking among strangers. This descriptive study addresses the gap by exploring data from 468 Chinese junior high school graduates in an online learning resource platform with an integrated social network. The data is digitally generated when the graduates engaged in online self-regulated learning activities for an average of 36 days without any facilitations. The data analysis explores the connection sizes and types of follow links, types of self-regulated learners, and their relationship with lesson completion. The study reveals that social networks trigger different levels of learning engagement. Specifically, the graduates with bidirectional follow links and the optimal connection size of five complete more lessons than other graduates. The study also finds that academic factors (similar learning goals and achievement gaps) are more important than social factors (common identity) in establishing social connections to support self-regulated learning activities. These findings have direct implications for the design of social networking that facilitates self-regulated learning, and enhances students’ self-regulated learning efficacy in online learning environments.

Technology makes possible abundant new opportunities to capture and display data in online learning environments. We describe here an example of using these opportunities to improve students' use of the rich supports available in online learning environments. We describe an example of a blended learning experience that uses an online inquiry‐based middle‐school science curriculum in which we provided sixth graders (n = 126) data aligned with the Universal Design for Learning instructional framework. Students were provided and asked to reflect on their own data not only about performance but also about use of optional embedded supports vis‐à‐vis their perceptions of difficulty of key concepts. We determined that students were generally able to understand and interpret these rich data and that providing these data influenced subsequent help seeking in the online environment. We discuss implications for supporting help seeking and designing assessment and feedback within online learning environments.

Collecting and analyzing data for continuous improvement of student learning has been a staple of the United States education system for the past two decades, but K-12 education is experiencing a paradigm shift to online learning. Teachers in face-to-face environments have experience monitoring student learning and collecting data through formative assessment. Assessment strategies implemented in face-to-face classrooms can be modified for an online learning environment. Additionally, higher education has utilized online platforms far longer than K-12 education and have extensive knowledge on data collection in an online learning environment. K-12 pre-service and in-service teachers can learn from the trials and tribulations of higher education in online learning. Lastly, teacher educators need to respond to this change in the instructional environment by altering their current curriculum to prepare preservice teachers to teach in an online environment. This chapter addresses these issues and provides practical examples and strategies for data collection in K-12 online learning environments.

Open, online learning environments, such as massive open online courses (MOOCs) and open learning communities have been promoted as a way to expand equitable access to quality education. Such learning experiences are potentially enriched via extensive networks of peer learners. Even though challenges exist to realize these aspirations, open, online learning environments can serve as a mechanism for how we provide transformative learning experiences. This workshop aims to bring researchers and practitioners from diverse disciplinary backgrounds to explore how the body of CSCW knowledge can better support the vision of sustaining peer-to-peer learning in online environments. Integrating contributions from designers, researchers, and practitioners at the intersection of CSCW & education, participants will co-create future visions and proposed implementations for open, online learning environments.

Low Higher Order Thinking Skills (HOTS) among students is a persistent and pervasive challenge in modern classroom. The problem remains for years despite the intensive manoeuvres to solve it. Yet, the need to enhance HOTS is imperative in ensuring quality education. The purpose of this research is to enhance HOTS among students in Mathematics using an online learning environment. The online learning environment was developed based on Polya’s problem solving model. However, the system is not discussed in this paper. There are three research objectives which are to investigate the current HOTS achievement among students, to study the effect of the developed online learning environment in nurturing HOTS among samples and finally to study how learning process taken place to achieve HOTS among sample while engaging the system. This research implemented experimental research design with a set of pre and posttest and the developed online learning environment as intervention. A total of 76 samples were sampled for the preliminary study and another 32 samples for the four weeks treatment using cluster random sampling technique. Samples are from three schools from a district in Malaysia. Finding shows that the current HOTS achievement among students are still low at mean = 32.05, σ = 15.87. During the intervention, samples show improvement in their HOTS from mean = 39.52, σ = 11.65 in the pretest to higher level in the posttest at mean = 77.84, σ = 12.84. The Paired-Samples T Test conducted to identify the differences between the pre and posttest shows a statistically significant difference at t (31) = -11.58, p < 0.05. Indicating that an online learning environment using Polya’s problem solving for four weeks enhance HOTS among samples. The process of how samples achieve HOTS is also reported. This research shows the importance of integrating technology and inquiry in modern classroom to increase students’ thinking skill ability. Finding suggests that HOTS can be nurtured via technology enhanced learning environment.