FLoRA Engine

Measuring self-regulated learning (SRL) skills of engineering college students while using enhanced guided notes (EGN) promotes a better understanding of how students deal with notetaking activities. This study focused on students’ task interpretation, cognitive strategies, and SRL processes including planning, monitoring, and regulating strategies while using EGN in an electric circuits course. The main objectives of this study were to (1) understand how students’ SRL skills changed after using EGN; and (2) evaluate how students’ conceptual understanding on electric circuits improved after using EGN. Ninety-seven students enrolled in the Fundamental Electronics for Engineers course at a land grant university participated in the study. A self-regulated learning survey instrument developed using Butler and Cartier’s SRL model was used to capture students’ task interpretation, cognitive strategies, and SRL processes. In addition, Holton’s DC/AC Circuits Concept Inventory was used to measure students’ conceptual understanding of DC/AC concepts. Participants were asked to complete both surveys twice online, at the beginning and end of the semester. Descriptive statistics, independent and paired t-tests, and a cluster analysis technique were used to analyze data. Data analysis of the SRL survey revealed that students had different SRL profiles. Students in the improved group reported a significant improvement in planning, monitoring, and regulating strategies. On the other hand, those in the declined group showed a lower awareness of all SRL skills at the end of semester. Furthermore, the findings revealed an improvement in students’ conceptual understanding. This article will also discuss the potential implications for electric circuit concepts instruction.

This study investigates secondary education students' self-regulated learning (SRL) processes with digital trace data, particularly whether SRL processes found in secondary education are comparable to those observed in higher education. We therefore adapted a digital learning environment and rule-based AI algorithm originally designed to measure SRL in higher education and collected multi-trace data from 13-year-old students ( N = 179) across three European countries during an essay-writing task. Hidden Markov modeling was employed to capture latent SRL processes. Four latent SRL processes emerged: orientation, first-reading, writing, and re-reading combined with monitoring. By clustering sequences of these latent SRL processes, we identified four sequential patterns of SRL processes at the task level: writing with metacognitive monitoring, writing intensively, reading first, writing next, and reading and writing simultaneously. Our findings highlight how AI and multi-trace data can be used to measure SRL during learning, providing a basis for enhancing personalized support. Self-regulated learning (SRL) is vital in the digital world. In this study, we investigated secondary education students' SRL processes with digital trace data. We also demonstrated that the instrumentation of a digital learning environment and rule-based AI algorithm originally designed to measure SRL processes in higher education can be leveraged to measure SRL processes in secondary education students. The real-time measurement of covert SRL processes is important as this information can 1) raise students' awareness on their learning, 2) help teachers to support students' learning, and 3) form the basis for providing personalized support for these processes with the help of AI-enhanced learning technologies. • Collected multi-trace data from 13-year-old students during an essay-writing task. • Found four SRL processes: orientation, reading, writing, re-reading with monitoring. • Identified four sequential patterns of SRL processes at the task level. • Students' task knowledge was associated with SRL at the task level.

Researchers, especially in the field of educational psychology, have argued that self-efficacy plays an important role in self-regulated learning. As such, teaching of self-regulated learning often focuses on enhancing self-efficacy. However, few studies have examined how the process of self-regulated learning might lead to the enhancement of self-efficacy. Therefore, the aim of this study was to investigate the effects of the self-regulated vocabulary learning process on self-efficacy. A total of 303 learners of English as a Foreign Language from two universities participated in the study. They completed a questionnaire measuring the process of self-regulated learning (i.e. forethought, performance or volitional control, and self-reflection). They also completed a vocabulary test as a measure of learning outcome. Correlation analysis was employed to evaluate the relationships between various variables investigated in the study. The results showed that self-regulated learning process can boost self-efficacy and increase vocabulary knowledge. The pedagogical implications of the current study are discussed mainly in terms of incorporating instructions aimed at enhancing self-efficacy and self-regulation in vocabulary teaching.

Massive Open Online Courses (MOOCs) have been integrated into face-to-face language modules to enhance the educational experience of students. MOOCs appear to offer language learning opportunities as well as challenging the self-regulated learning (SRL) behaviour of students. This paper presents a work in progress with regards to the literature review of my PhD, which investigates SRL in face-to-face language modules using MOOCs. It discusses SRL as a requirement for engaging with these courses and analyses the way studies have integrated MOOCs into instructional language modules. This paper concludes by highlighting the need for understanding students’ SRL behaviour when engaging with MOOCs as part of their language modules.

Self-regulated learning (SRL) process is temporally bounded and unfolds over time. Achieving insights into temporal dynamics in SRL processing, however, requires not only fine-grained data collection and methods but also modeling approaches that accurately account for the sequential structure of the events during the SRL process. Addressing this, we investigate an understudied aspect of SRL theory: how temporal SRL processing, as captured by multimodal traces, differs between mastery and progressing groups in the context of technology-enhanced biology learning in a lab setting. We examine count-based, duration-based, and sequence-based models of SRL processing to unveil diverse dimensions of the process across different achievement levels. Specifically, for the sequence-based model, we employed a multi-method approach, incorporating epistemic network analysis (ENA) to analyze the temporal and sequential dynamics of multimodal events during SRL processing and then qualitative analysis to delve deeper into those dynamics. Our study offered valuable methodological contributions by empirically comparing different methods for modeling temporality and by utilizing a novel analytic approach (i.e., ENA) to model the temporal dynamics of SRL processes. Additionally, it makes theoretical contributions to advancing the understanding of the dynamic interplay between SRL processing and academic performance, thereby informing targeted interventions to support students’ learning processes, ultimately enhancing their learning outcomes in technology-enhanced STEM education. Future researchers can leverage these contributions to advance models of the dynamic and temporal nature of SRL.

In this study the researcher investigates  Thai secondary students’ experiences with a personalised blended learning (PBL) platform using Microsoft Teams (MS Teams) to enhance their English communication skills and self-regulated learning (SRL) skills. Employing an intrinsic case study method, I collect data from 19 students with A1 level Common European Framework of Reference for Languages  enrolled in the remedial English course through classroom observation field notes, learners’ reflective journals, semi-structured interviews, and MS Teams assignments integration. Through thematic analysis, the findings reveal that MS Teams features, such as Reading Progress, Immersive Reader, and interactive task-based assignments, support listening, speaking, reading, and writing skills, allowing students to practise asynchronously and receive timely, personalised feedback. Regarding SRL, MS Teams enables students to set goals, track progress, and reflect on their performance while offering flexibility through adjustable deadlines and revisitable materials. However, I note challenges such as technical limitations, variations in digital literacy, and inconsistent engagement levels. Via thematic analysis I identify recurring patterns in qualitative data, while recorded data from MS Teams Insights highlight student performance and engagement trends. In conclusion, MS Teams, when integrated into PBL, significantly enhances English communication skills and SRL behaviours, providing valuable insights for improving technology-supported language instruction in secondary education contexts.

This study aimed to investigate the impact of self-regulated learning (SRL) skills on the academic success of non-commissioned officer (NCO) students within a Special Vocational College (SVC) belonging to the government context. As blended courses become increasingly prevalent, there is a growing interest in understanding the role of SRL, a critical determinant of academic performance. While SRL has garnered substantial attention in higher education, limited research has focused on its relevance within SVCs. Furthermore, little is known about the factors influencing students' SRL levels in online learning settings and their subsequent impact on course performance. Therefore, this study endeavors to identify the factors that influence SVC students' SRL and assess how it impacts success in blended courses. The study was conducted during the Fall semester of 2022-2023, encompassing five distinct compulsory blended courses and involving a total of 203 SVC students. We utilized a path model, expanding upon the framework proposed by Liaw and Huang in 2013, to assess the influence of SRL on academic achievement. The findings demonstrated that students' perceptions of the interactivity in the learning environment (ILE) and their perceived self-efficacy (PSE) had a positive and significant impact on their perceived satisfaction (PS). Additionally, ILE, PSE, and PS significantly influenced the perceived usefulness (PU) of the learning management system (LMS). Furthermore, ILE, PU, and PS emerged as significant predictors of students' SRL skills. However, the observed results did not entirely align with the expected impact on academic success. The study's findings shed light on the complex relationship between course achievement and SRL in blended learning settings, as well as the interrelated factors at play. The practical implications of these findings extend to how LMS administrators can support students in blended courses in becoming more self-regulated learners.

While virtual learning environments (VLEs) are widely used in K-12 education for classroom instruction and self-study, young students’ success in VLEs highly depends on their self-regulated learning (SRL) skills. Therefore, it is important to provide personalized support for SRL. One important precursor of designing personalized SRL support is to understand students’ SRL behavioral patterns. Extensive studies have clustered SRL behaviors and prescribed personalized support for each cluster. However, limited attention has been paid to the algorithm bias and fairness of clustering results. In this study, we “fairly” clustered the behavioral patterns of SRL using fair-capacitated clustering (FCC), an algorithm that incorporates constraints to ensure fairness in the assignment of data points. We used data from 14,251 secondary school learners in a virtual math learning environment. The results of FCC showed that it could capture six clusters of SRL behaviors in a fair way; three clusters belonging to high-performing (i.e., H-1. Help-provider, H-2) Active SRL learner, H-3) Active onlooker), and three clusters in low-performing groups (i.e., L-1) Quiz-taker, L-2) Dormant learner, and L-3) Inactive onlooker). The findings provide a better understanding of SRL patterns in online learning and can potentially guide the design of personalized support for SRL.

Over-reliance on generative artificial intelligence (AI) for writing tasks can have negative effects on engineering education. Despite growing concerns over generative AI’s impact on authentic writing skills, limited research has critically examined alternatives that integrate between self-regulated learning (SRL) theories with knowledge visualization tools to foster monitoring and evaluation processes in engineering education. This exploratory study addresses this gap by exploring how machine learning-based text analytics can scaffold SRL, extending prior frameworks on information processing. Thirty participants were recruited from two sections of an engineering technology course. As a course task, participants wrote essays using the knowledge visualization system over a semester. SRL skills were measured through a survey, and final course grades served as a measurement of learning performance (LP). First, there were no noticeable relationships between students’ SRL, LP, and writing performance (WP). Second, regression analysis showed that SRL and course grades do not significantly predict WP. Third, engineering students’ WP significantly increased over time. Lastly, there were no differences in WP changes over time between high and low SRL or LP groups. However, there is a main effect of LP on WP, and an interaction effect of LP and time was observed in the evaluating component.

First-year higher education (HE) students experience different challenges during their studies. These challenging learning situations can trigger self-regulated learning (SRL) skills, which students use to handle these situations. Thus, the aim of this study is to investigate (a) first-year HE students’ cognitive, motivational and emotional challenges experienced in both individual and collaborative learning situations and (b) the relationship between SRL skills and experienced learning challenges. Participants included 107 first-year pre-service teachers. Data consisted of students’ self-reports via (a) open-ended answers on a challenge questionnaire and (b) Likert-scale items from MSLQ and MRS questionnaires. Based on students’ SRL profiles, differential effects on the challenging experiences were investigated. The results show that different aspects related to students’ cognition, motivation, emotions and well-being were challenging for students. Also, connections were found between students’ SRL skills and the types of experienced learning challenges. Students with high SRL reported fewer learning challenges related to motivational aspects and more challenges related to cognitive aspects than students with low SRL.

Self‐regulated learning (SRL) is an active process involving multiple interacting components that evolve over time, exhibiting characteristics of complex systems such as non‐linearity, emergent behaviour, self‐organization, and hierarchy. These interactions unfold at different temporal levels, each warranting a dedicated lens to capture their distinct dynamics. In this study, we apply a complex dynamic systems lens to analyse the longitudinal dynamics of SRL. We map how different SRL processes interact with each other across time and scales: (1) the stable between‐person level, which represents the dominant approach to learning or roughly the trait of SRL, (2) the contemporaneous level, which maps how SRL processes influence each other within the same time and (3) the temporal level, which captures how processes predict or influence each other in the future. Data were collected through a weekly survey administered over 4 weeks in five courses at two institutions, complemented by LMS behavioural engagement data. A panel vector autoregression model was employed to examine the structure and dynamics of SRL and LMS behavioural engagement at the three levels. The findings suggest that central SRL processes, such as planning and adapting, take place in separate stages, in accordance with the classic SRL models, whereas other processes, like effort regulation, are more pervasive, co‐occurring with most other regulatory processes. At the aggregate level, adjusting was the most central process that drove students' SRL. As such, our results align with the main characteristics of complex systems, including non‐linearity and hierarchy. These findings have implications for the design of SRL interventions, where effort can benefit from real‐time prompts, whereas metacognitive processes might require long‐term scaffolding. Furthermore, the weak association between LMS engagement and SRL processes across all levels highlights the limitations of relying solely on behavioural trace data to infer regulation. Practitioner notes What is already known about this topic? Self‐regulated learning (SRL) is an important driver of academic success and can be influenced through targeted interventions. Most SRL research is based on group‐level data, often using static, cross‐sectional designs that overlook temporal dynamics. Recent work has highlighted that SRL can and should be modelled as a complex dynamic system. What this paper adds? There is evidence of complex systems characteristics in SRL such as hierarchy, non‐linearity and feedback loops. SRL processes follow distinct temporal phases, with some processes persisting throughout all phases. Metacognition is the most central process at the between‐person level, whereas effort is central at the within‐person level. LMS behavioural data is weakly linked to self‐reported SRL. Implications for practice and/or policy SRL interventions should consider how regulatory processes unfold over time, rather than treating SRL as a static trait. Interventions targeting effort regulation and metacognition have the potential to be the most consequential. Caution must be exerted when using average or between‐person data to inform individualized support. LMS metrics should be interpreted with care and ideally complemented by self‐report or observational data.

Despite the growing integration of generative artificial intelligence (GenAI) into academic writing, learners’ self-regulated learning (SRL) behavior patterns and their relationships with individual characteristics remain insufficiently understood. Drawing on 37 hours of screen-recorded GenAI-assisted writing data, comprising 3268 behavioral units from 30 graduate students, the present study examines the dynamic SRL behaviors of students across different learner characteristic clusters in GenAI-assisted academic writing. This study employs multiple learning analytics methods (clustering, descriptive statistics, non-parametric tests, and lag sequential analysis) and incorporates English proficiency and final writing performance as key learner characteristics. The results show that (a) three student clusters emerge based on English proficiency and final writing performance: the Higher-English-Proficiency–Lower-Writing-Performance group, the Higher-English-Proficiency–Higher-Writing-Performance group, and the Lower-English-Proficiency–Higher-Writing-Performance group; (b) higher-performing groups exhibit more cyclical and well-coordinated SRL behavioral patterns, whereas the lower-performing group demonstrates relatively less coordinated SRL behavioral patterns; and (c) among higher-performing students, those with lower English proficiency tend to engage more extensively in SRL behaviors and achieve comparable writing performance; however, this pattern also raises concerns about a possible overreliance on GenAI. Based on these findings, a series of theoretical and pedagogical implications are proposed to support the enhancement of students’ SRL in GenAI-assisted academic writing contexts.

Developing problem solving competence is one of the main goals of Mathematics Education. Self-regulated learning skills are important for success in problem solving. The aim of this paper is to present the results of a research about 10-11 years old pupils’ self-regulated learning and problem solving skills and the relation between these two skills. The results show that more than three quarters of 10-11 years old pupils like Mathematics, but only the half of them likes to solve non-routine problems. The positive attitude towards Mathematics is influenced by pupils’ positive experiences on solving problems and their beliefs in the utility of Mathematics in their future. Most of the pupils don’t give up if they can’t solve the problem, they go over the text again, they try to find similar worked examples, they ask for help. There is a mild correlation between pupils’ self-control and problem solving skills, a weak correlation between pupils’ attitude towards Mathematics and their problem solving skills.

In order to manage the demands of university studies, research suggests that university students should possess efficient self-regulated learning (SRL) skills. The use of mobile learning is believed to enhance SRL skills. Yet, despite the potential benefits of mobile learning, many first-year students encounter challenges in using mobile devices to foster self-regulated learning skills. It is against this backdrop that this study aimed to examine how first-year medical students utilisee mobile learning to foster self-regulated learning. This study employed a Explanatory Sequential Study Design , combining both quantitative and qualitative data. It used the interpretativist and pragmatic paradigms to understand how students perceive and use mobile devices for self-regulated learning. The interpretivist paradigm focused on understanding students’ perspectives, while the pragmatic paradigm aimed to identify effective ways of using mobile devices to promote self-regulated learning. Participants to this study were first-year medical students. The mixed-methods approach aimed to provide a comprehensive understanding of the research problem by collecting data through various methods. Questionnaires gathered quantitative data, while participatory observation, reflective workshops, and focus group discussions provided qualitative data. The theoretical frameworks that guide the study include the Unified Theory of Acceptance and Use of Technology model (UTAUT) (Venkatesh et al., 2003) and Vygotsky's (1978) socio-cultural theory (SCT). The study findings revealed that mobile devices were used by first year medical students to manage and control their learning, both inside and outside the classroom. Notably, significant improvements were observed in cognitive learning strategies, metacognitive self-regulation strategies and resource management learning strategies, including time management. Additionally, the study found that first-year medical students had a positive perception of mobile learning. Furthermore, mobile devices were generally perceived as enablers of self-regulated learning (SRL), providing flexibility, convenience, and access to diverse learning resources. However, distractions and interruptions posed challenges to focus and attention. To mitigate these challenges, strategies such as device management and setting boundaries were suggested. Moreover, first-year medical students expressed the need for support in developing SRL through mobile learning. Guidance in selecting effective mobile applications, opportunities for collaborative learning, and technological skill training were identified as essential forms of support. The study thus recommends integrating mobile learning into curricula to enhance self-regulated learning skills in higher education students, especially among first-year students. Higher education institutions should establish deliberate policies and initiatives for mobile learning programs, especially for first-year students. Educators should offer guidance and training on using mobile learning tools effectively, including information searching skill. However, students should be cautious about distractions from the use of mobile devices.

Assessment has several advantages like involving students in learning and making them cognizant of their strengths and weaknesses. The effects of peer assessment and self-assessment as two main kinds of assessment have not examined on EFL learners’ self-regulated learning, critical thinking, and problem-solving skills; therefore, the present research attempted to inspect the impacts of self and peer assessment activities on self-regulated learning, critical thinking, and problem-solving skills of Iranian EFL learners. To do so, 75 Iranian EFL learners were selected and divided into two experimental groups (peer assessment group (PAG) and self-assessment group (SAG)) and one control group (CG). Then, three questionnaires of self-regulated learning, critical thinking, and problem-solving skills were administered to the students as the pre-tests of the study. After that, one experimental group received the treatment via using self-assessment and the other experimental group received the instruction through peer assessment. The control group received a teacher-based assessment instruction. After teaching 13 English conversations to all groups, 3 post-tests were given to them to measure the impacts of the treatment on their self-regulated learning, critical thinking, and problem-solving skills. The results of one-way ANOVA showed that there was a substantial difference between the post-tests of the experimental groups and the control group. The results showed that both experimental groups outperformed the control group in the post-tests of self-regulated learning, critical thinking, and problem-solving skills. Furthermore, the outcomes revealed that there were no significant differences between the post-tests of the experimental groups. It can be concluded that both types of assessments play a vital role in English language teaching and learning. Based on the results of this study, language teachers, materials developers, and education authorities can concentrate on using self and peer assessments as one of the main strategies to develop the language learning ability of Iranian EFL learners.