Examining Prediction Observation Explanation Method based Interactive e-books in terms of High School Students' Misconceptions, Self-Regulated Learning, Cognitive Engagement

Digital technology has made smartphones an essential part of students’ lives. Although smartphones can aid learning, excessive use can distract students and decrease their engagement in learning. A quantitative method with a mediation model for a correlational design using multiple linear regression analysis was used in this study. The purpose of this study is to explain the role of self-regulated learning in mediating the relationship between smartphone usage intensity and academic motivation of high school students. A total of 395 high school students were randomly selected from the high school student population. The scales used for data collection included a smartphone usage intensity scale, a self-regulated learning scale, and an academic motivation scale. The research results show that 1) the intensity of smartphone use significantly affects self-regulated learning by 30.3%, 2) academic motivation significantly affects self-regulated learning by 20.8%, and 3) self-regulated learning significantly affects the intensity of smartphone use and academic motivation. Followed by an F regression value of 15.392. An Rsquared value of 0.073 indicates that self-regulated learning contributes 70.3% to the intensity of smartphone use and academic motivation. Smartphone use intensity and academic motivation are considered significant predictors of the variation in selfregulated learning among high school students. The pattern of smartphone use and the level of academic learning drive influence students’ ability to independently plan, monitor, and evaluate their learning process.

A Longitudinal Study of Learning Engagement of Math and Science Gifted Students in Junior and Senior High School

Self-regulated learning is one of the growing aspects in the field of educational psychology. It plays a very important role in the academic success of the students. In addition, the failure of self-regulation resulted in academic procrastination. Academic procrastination is recurring behavior of students. The objective of the present study is to find out the levels of self-regulated learning, levels of academic procrastination, and the influence of self-regulated learning on the academic procrastination of the students. The present study was a Descriptive survey and questionnaire-based. The study was performed in the Kamrup (M) colleges of Assam affiliated with Gauhati University. The sample of the present study was 142 college students both boys and girls. The data were collected both offline and online. To run the statistical test, SPSS was used. Z score, percentage, Chi-square, correlation, and regression analyses were performed to test the null hypotheses and find out the objectives. The result indicates that college students are self-regulated learners as all the students fall under the extremely high level of self-regulated learning to average level of self-regulated learning. Again they are also an academic procrastinator. It was also indicated that there is a significant negative correlation between self-regulated learning and academic procrastination. Also, regression analysis indicated that self-regulated learning is a strong predictor of academic procrastination of college students. The level of students' self-regulated learning and academic procrastination must be pointed out in order to ensure the student's academic success.

Based on Ellis’s three-dimensional learner engagement framework, this study examined the learner engagement in multiple feedback of nine second language learners with different language proficiency through writing texts, retrospective interviews, and semi-structured interviews. It was found that (1) learners generally have a positive attitude towards multiple feedback; they experience positive emotions while negative emotions such as disappointment, doubtful, angry, guilty are also generated in the process of revision; learners tend to absorb more teacher feedback and more meaning-level feedback and they are relatively poor at self-regulated learning. (2) Language proficiency has an effect on learner engagement, with a more significant effect for lowerlevel learners. Learners with lower language proficiency tend to be opposed to multiple feedback and show lower behavioral and cognitive engagement. (3) There is a correlation between the three dimensions of learner feedback engagement: Affective engagement influences behavioral and cognitive engagement, and the behavioral engagement is inspired by learners’ cognitive engagement. This study provides insights into the implementation of multiple feedback and second language writing, which is beneficial to the cultivation of second language talents in the new era.

This article focuses on teacher educators’ reflections on prospective teachers’ cognitive engagement in the teaching and learning of mathematics during the pandemic. Using three interacting aspects that can foster learners’ cognitive engagement as a lens, observations and reflections from two teacher educators and anonymous screenshots of students’ work were gathered and analysed by a mathematics teacher educator. The prospective teachers’ self-regulated learning, engagement in solving tasks, and participation in productive discourse were positively surprising, showing the cognitive presence of the learners during virtual teaching. The influence of digital platforms like Blackboard and digital tools like GeoGebra, Desmos, Padlet, Google Docs, Google Forms and Google Sheets in teaching helped us observe the learners’ cognitive engagement in real time.Contribution: The GeoGebra Class function and the Desmos Teacher feature helped us to observe the prospective teachers’ cognitive engagement during the online teaching. However, continuously and rapidly creating digital content on said digital platforms can be demanding for educators.

In this study, we systematically examined the effects of self-concept clarity on high school students’ learning engagement and the mediating role of sense of life meaning and future orientation between self-concept clarity and learning engagement in order to provide guidance to enhance students’ learning engagement. A total of 997 students from freshmen to seniors were selected for the study using a cluster random sampling method. The following tools were used: the Self-Concept Clarity Scale, the Learning Engagement Scale, the Sense of Life Meaning Scale, and the Future Orientation Questionnaire. The results indicated that the clarity of self-concept positively predicted the level of learning engagement of high school students. Sense of life meaning and future orientation partially mediated the effect between self-concept clarity and learning engagement, and sense of life meaning and future orientation had sequence-mediated effects between self-concept clarity and learning engagement among high school students. This study suggests that high levels of self-concept clarity can help high school students actively seek a sense of life meaning and make more optimistic future orientations, thereby increasing their level of learning engagement.

The role of mathematics is not only to emphasize mastery of formulas and calculations, but also to develop students’ mathematical reasoning skills, enabling them to think logically, systematically, and critically in problem-solving. This study aimed to analyze students’ mathematical reasoning skills in terms of self-regulated learning on the topic of algebraic expressions for class VII-B students at SMP Negeri 1 Suwawa. This research employed a descriptive method with a quantitative approach. The sample consisted of 27 students from class VII-B, selected using purposive sampling. The research instruments included a mathematical reasoning skills test and a self-regulated learning questionnaire, both of which had been tested for validity and reliability. Data were analyzed descriptively through the calculation of mean scores, percentages, and the categorization of students’ mathematical reasoning skills and levels of self-regulated learning. The results showed that: 1) students with high self-regulated learning had high mathematical reasoning skills, with an average score of 70.50; 2) students with moderate self-regulated learning had moderate mathematical reasoning skills, with an average score of 43.67; and 3) students with low self-regulated learning had low mathematical reasoning skills, with an average score of 22.50. Thus, it can be concluded that the higher the students’ level of self-regulated learning, the better their mathematical reasoning skills.

The research objective is to determined the relationship between academic procrastination with self-regulated learning and learning motivation in high school students. The research was carried out through a quantitative approach with a correlation design. The populations of this study were students of class XI at Senior High School Bojonegoro. The research sample used was class XI students at Senior High School 1 Kedungadem, Senior High School 1 Kepohbaru and Senior High School 1 Sugihwaras as many as 257 students taken through simple random sampling technique. Research data were collected through self-report techniques with academic procrastination scale, self-regulated learning scale and learning motivation scale. The three scales have been tested for validity and respectively have reliability coefficients of 0.917, 0.865, and 0.951. Data analysis was carried out through statistical methods using multiple correlation formulas. The results of analysis prove that there is a significant relationship between academic procrastination with self-regulated learning and learning motivation with a significance score. The results of this study can be used by guidance and counseling teachers as study material to deal with academic procrastination problems that occur among students.

This study aims to examine the effect of self-regulated learning, self-esteem, social support, and task commitment on academic flow among high-achieving students at State Senior High Schools in Sidoarjo, Indonesia. This study used a correlational quantitative design. Data were collected using the self-regulated learning scale, the self-esteem scale, the social support scale, the task commitment scale, and the academic flow scale. Participants of this study involved 80 students from a total population of 3,204 students who were selected using the quota sampling technique. The result of Partial Least Square Structural Equation Modeling (PLS-SEM) indicated SRMR = 0.073, and NFI = 0.81, R2=0.484. This result means that self-regulated learning, self-esteem, social support, and task commitment simultaneously explain 48.4% of the variance in academic flow. Further analysis showed that of the four variables, only self-regulated learning and task commitment have a significant effect, implying that higher self-regulated learning and more commitment to the tasks lead to more intense academic flow. This finding differs from some previous study and implied that the students need to be facilitated with self-regulated learning-oriented learning design and need for improvement in task commitment to intensify academic flow that eventually will maintain and exert their optimal performance.

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.

Online courses in higher education are growing in popularity (Putulowski & Crosby, 2019). The worldwide COVID pandemic of 2020 brought new attention to online courses and the percentage of higher education students enrolled in online courses skyrocketed from 29.7% in 2015 (Allen & Seaman, 2017) to 75% in 2020 (NCES, 2022). By 2022, an average of 54.2 % of higher education students enrolled in an online course (NCES, 2023). The student experience in online courses can be impacted by many different factors including work and family responsibilities, study commitments for the course, insufficient time, ill health, and approach to learning (Farrell & Brunton, 2020; Willging & Johnson, 2019). Students may also encounter different barriers such as institutional, technological, financial, and pedagogical barriers that may hinder their online student experience (Neben, 2014). Much of the research that focused on what students in higher education perceive as barriers to their online experience has been conducted prior to the COVID pandemic of 2020. This body of research points to a range of perceived barriers to the online student experience such as lack of social interaction, instructor issues, time and support for studies, learner motivation, and technology issues that can interfere with the online student experience (Muilenburg & Berge, 2005). This survey study was conducted to determine what barriers are perceived in online courses after the COVID pandemic of 2020. The survey consisted of two measures including the Barriers to Online Learning measure developed by Muilenburg and Berge (2005) and the Online Self-Regulated Learning Questionnaire (OSLQ) used to measure student self-regulation in online learning (Barnard et al., 2009). Composite means of barrier subscales were determined as well as the percentage of participants who rated barrier subscales as a moderate barrier or stronger. Mann-Whitney U tests were performed to determine if there were significant differences in perception of barriers among genders, self-regulated learning levels, graduate and undergraduate students, and the number of online courses taken. Motivational barriers were rated as a moderate barrier or higher by the highest percentage of participants. Social barriers had the second highest percentage of participants rating them as a moderate barrier or higher. Also, data suggest that perceptions of barriers are not significantly different among genders. Participants who reported lower levels of self-regulation in learning had significantly higher scores for the motivation barriers than participants who reported higher levels of self-regulation in learning, graduate students rated time barriers significantly higher

Contextual problems play an important role in learning in vocational high schools because they connect mathematical concepts with real-life situations relevant to students. However, studies that specifically examine vocational high school students’ inductive thinking skills in solving contextual mathematical problems, particularly in geometric sequences, viewed from the level of Self-Regulated Learning (SRL), are still limited. This study aims to analyze the inductive thinking abilities of vocational high school students in solving contextual geometric sequence problems based on their SRL levels. A qualitative approach with descriptive analysis was employed. The research subjects consisted of 33 eleventh-grade students from the Accounting and Institutional Finance class at SMK Negeri 1 Boyolali. The results showed that 3 students were categorized as having high SRL, 24 students had moderate SRL, and 6 students had low SRL. Analysis of students’ responses revealed that students with high SRL were able to meet all indicators of inductive reasoning, namely observing regularities, identifying patterns, and formulating generalizations. Students with moderate SRL were able to observe regularities and identify patterns but experienced difficulties in formulating generalizations. Meanwhile, students with low SRL were only able to observe regularities. These findings indicate that SRL levels influence students’ inductive thinking abilities. This research revealing students’ characteristics and difficulties at each inductive reasoning indicator based on SRL levels within the context of geometric sequences in vocational high schools, as well as providing practical implications for teachers in designing more effective contextual mathematics learning.

Developing Web-based assessment strategies for facilitating junior high school students to perform self-regulated learning in an e-Learning environment

This study aims to identify student learner profiles based on a combination of prior knowledge and Self-Regulated Learning (SRL) levels and to analyze the differential impact of an adaptive learning module on knowledge and SRL improvement in each profile. Using a mixed-methods explanatory sequential design, 92 undergraduate physics education students were selected through purposive sampling. K-Means cluster analysis was applied to form learner profiles, followed by a six-week pre–post intervention and qualitative interviews. The results identified three learner profiles (Proficient-Autonomous Learner, Resilient-Developing Learner, and Proficient-Fragile Achiever). The result showed that the adaptive module significantly improved Results showed significant knowledge gains across profiles, while SRL improvements differed significantly. The Proficient–Fragile Achiever group demonstrated the largest SRL gain (p < .001; large effect size, d > 0.80), associated with more frequent scaffolding support. In conclusion, the effectiveness of adaptive modules is highly dependent on learner profiles, with the most significant benefits in their ability to provide external support for building self-regulation skills. These findings imply that learning technology design should incorporate SRL as a key variable for personalization, and institutions can utilize these platforms as intervention tools for students with weak learning independence.

<sec> <title>BACKGROUND</title> Medical graduate education increasingly uses blended and online delivery, although students' academic self-regulation may be shaped by different motivational and cognitive processes across learning contexts, with emotional factors potentially playing a complementary role. Understanding how these mechanisms operate and whether their structural relationships differ between online/blended and face-to-face formats can inform targeted educational supports. </sec> <sec> <title>OBJECTIVE</title> The present investigation developed and tested a comparative causal model of academic self-regulation among medical graduate students in online/blended versus face-to-face programs. We examined how key motivational constructs (eg, academic self-efficacy, task value, future orientation, perfectionism, and academic help-seeking), positive achievement emotions, and cognitive factors (cognitive academic engagement and need for closure) relate to academic self-regulation, and whether these relationships differ by learning context. </sec> <sec> <title>METHODS</title> The design was cross-sectional, comparative causal modeling. Participants were master’s-level students at Shahid Beheshti University of Medical Sciences enrolled in either face-to-face (population n=1554; sample n=310) or blended/online (population n=449; sample n=205) programs selected using cluster sampling. Data were collected using validated instruments measuring academic self-regulation (Bouffard scale), academic self-efficacy (Midgley et al), academic engagement (Schaufeli &amp; Bakker), multidimensional perfectionism (Frost), academic help-seeking (Ryan &amp; Pintrich), task value (Pintrich), future orientation (Seginer), need for closure (DeBacker &amp; Crowson), and achievement emotions (AEQ; Pekrun et al). Data were analyzed using path analysis/structural equation modeling. Model fit was evaluated using χ²/df, CFI, GFI, AGFI, and RMSEA. Direct, indirect, and total effects were estimated for each group, and comparative interpretation focused on effect patterns and explained variance. </sec> <sec> <title>RESULTS</title> The hypothesized causal model reached an acceptable fit in both face-to-face and blended/online groups (χ²/df approximately &lt;3; CFI/GFI/AGFI in the acceptable range; RMSEA approximately 0.02–0.05). In both groups, most of the specified direct effects reached statistical significance, while the indirect effects of exogenous variables on academic self-regulation through intermediate constructs were supported overall. Cognitive academic engagement and academic self-efficacy were important proximal predictors of academic self-regulation. The need for closure had a negative direct effect with regard to academic self-regulation. However, a previously specified direct effect from need for closure to self-regulated learning strategies could not be retained in the final revised model. In both cohorts, the indirect pathway from positive achievement emotions to academic self-regulation via cognitive engagement was not supported, indicating that positive emotions alone were insufficient to increase self-regulation through cognitive engagement. The model explained a substantial proportion of variance in academic self-regulation in both groups—being approximately 0.44 in face-to-face and 0.46 in blended/online students—indicating comparable overall explanatory power across learning contexts. </sec> <sec> <title>CONCLUSIONS</title> A comparative causal model integrating motivational, emotional, and cognitive pathways provided an adequate explanation of academic self-regulation among medical graduate students in both face-to-face and blended/online formats. Findings highlight the central role of cognitive engagement and academic self-efficacy as proximal levers for supporting self-regulation across contexts. The lack of a supported indirect effect from positive emotions to self-regulation via cognitive engagement suggests that emotional experiences may not be enough unless they are accompanied by cognitively engaged learning behaviors. Considering motivational and cognitive mechanisms that together shape self-regulation within different delivery modes, educational interventions in medical graduate programs should focus on strengthening self-efficacy beliefs and cognitively engaged learning practices. </sec>