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The purpose of this study was to investigate the effect of self-regulated programming learning on undergraduate students’ academic performance and motivation compared to traditional methods.

This study was conducted with an explanatory sequential mixed method. Participants consist of 31 undergraduate students studying in the department of computer and instructional technologies education. The students were separated into two groups as experimental (n = 15) and control (n = 16) in the robotic programming course. Academic performance tests, programming motivation scale and interview form were used as data collection tools. After collecting quantitative data, interviews were conducted with the students regarding their academic performance and motivation.

The results indicated that the self-regulated programming learning process can contribute positively to students’ academic performance and motivation compared to traditional methods. Students stated that self-regulated learning strategies can positively affect their academic performance and motivation.

In this study, a self-regulated learning support system was designed to encourage students to use self-regulated learning strategies. This study has the potential to contribute to the gap in the literature, especially as a study of adapting the phased model of self-regulated learning to programming teaching. Instructors can use the self-regulating programming learning framework by adapting it to different disciplines.

This study aims to investigate which a priori factors documented in the literature and new factors that influence employees’ self-regulated microlearning behavior and the utilization of internal microlearning platforms in organizations.

The authors conducted a single-case study on a Swedish retail group that had developed an internally-built microlearning platform, collecting data through semistructured interviews with 13 informants.

The authors have identified eight factors that affect employees’ self-regulated microlearning processes. In addition to confirming the presence of five factors from previous research, the authors have discovered the influence of three new factors on self-regulated learning. These new factors are prioritization, other learning platforms and relevant content.

The study conducted a unique investigation into the factors influencing employees’ self-regulated learning strategies and their impact on the utilization of microlearning platforms. Previous research has given limited attention to this research topic and associated questions, making this study a valuable contribution.

This study explores a conceptual framework that addresses a school principal's self-regulated learning (SPSRL) as well as possible avenues for future conceptualization of, and research into this issue.

The conceptual framework of SPSRL is based on an extensive literature review of the research on student’s and teacher’s self-regulated learning models.

A novel conceptual and practical SPSRL framework for planning, performing, monitoring and self-reflection is elaborated.

This novel SPSRL conceptual framework provides school principals with a means to shape and develop processes, strategies and structures to monitor and evaluate their learning, enabling them to react effectively in uncertain and dynamic environments. This framework may open the way to future research into possible contributions of the SPSRL construct with other variables related to principal effectiveness. The suggested framework should be examined empirically in various sociocultural contexts, possibly substantiating its conceptual validity.

The SPSRL conceptual framework can improve school learning, which might connect the individual (the school principal) and organizational (teachers) learning levels.

The aims of this study were (1) to examine the relationships among achievement goals, self-concept, learning strategies and self-regulation for post-secondary Indigenous Australian and Native American students and (2) to investigate whether the relationships among these key variables were similar or different for the two groups.

Students from the two Indigenous groups answered questionnaires assessing the relevant variables. Structural equation modelling (SEM) was used to analyse the data. Structure-oriented analysis was used to compare the two groups in terms of the strengths of the pathways, while level-oriented analysis was used to compare mean level differences.

Self-concept was found to positively predict deep learning and self-regulated learning, and these effects were mediated by achievement goals. Students who pursued mastery and social goals had more positive educational outcomes. Both structure and level-oriented differences were found.

Drawing on two distinct research traditions – self-concept and achievement goals – this study explored the synergies between these two perspectives and showed how the key constructs drawn from each framework were associated with successful learning.

To improve learning outcomes, interventions may need to target students’ self-concept, mastery-oriented and socially oriented motivations.

Supporting Indigenous students in their post-secondary education is an imperative. Psychologists have important insights to offer that can help achieve this noble aim.

Research on Indigenous students has mostly adopted a deficiency model. In contrast, this study takes an explicitly positive perspective on Indigenous student success by focusing on the active psychological ingredients that facilitate successful learning.

As a response to the COVID-19 pandemic, both educators and students adapted to course delivery modes no longer centered on in-person interactions. Resiliency and self-regulation are key to success in online contexts, but the rapid transition to remote learning left many students without the necessary support to develop these skills. Much of the existing literature on self-regulation and resiliency focuses on cognitive processes and strategies such as goal orientation, time management, and mindset. However, the added stress and trauma of learning in the context of a global pandemic highlighted the many other factors relevant to students’ development of these skills. Drawing from the literature, the authors explore evidence-informed teaching practices to foster self-regulation and resiliency, highlight the power and privilege of being able to be resilient, advocate for the development of pedagogies of kindness, and emphasize the “how” of implementing techniques to best support students. The authors provide evidence-informed suggestions with the goal of assisting instructors and students during times of high stress, while acknowledging their limitations in addressing structural inequalities highlighted by the COVID-19 pandemic. Nonetheless, the authors argue that evidence-informed techniques and compassionate pedagogies adopted during a period of upheaval remain applicable to future in-person and online pedagogies.

This paper aims to understand what drives people – their motivations, autonomous learning attitudes and learning interests – to volunteer as mentors for a program that helps families to ideate technological solutions to community problems.

A three-phase method was used to build volunteer mentor profiles; elicit topics of interest and establish relationships between those. The mentor profiles were based on self-assessments of motivation, attitude toward lifelong learning and self-regulated learning strategies. The topics of interest were elicited through content analysis of answers to reflection questions. Statistical methods were applied to analyze the relationship between the interests and the mentor profiles.

Bottom-up clustering led to the identification of three mentor groups (G1 “low”; G2 “high” and G3 “medium”) based on pre-survey data. While content analysis led to identifying topics of interest: communication skills; learning AI; mentoring; prototype development; problem-solving skills; working with families. Analyzing relationships between mentor profile and the topics of interest, the group G3 “medium,” with strong intrinsic motivation, showed significantly more interest in working with families. The group with the overall highest scores (G2 “high”) evidenced also substantial interest in learning about AI, but with high variability between members of the group.

The study established different types of learning interests of volunteer mentors and related them to the mentor profiles based on motivation, self-regulated learning strategies and attitudes toward lifelong learning. Such knowledge can help organizations shape the volunteering experience to provide more value to volunteers. Furthermore, the reflection questions can be used by volunteers as an instrument for reflection and by organizations to elicit the learning interests of volunteers.

This study aims to present a scaffolding framework incorporating sophisticated technology that can inform instructional design to support student inquiry learning in the self-regulated online environment.

The classic predict, observe and explain (POE) framework has been extended through the incorporation of an additional evaluate (E) phase into the model to enhance the self-regulated online learning environment. The extended POEE scaffolding approach, in this study, has been conceptualized as an implicit guide to support the process of guided inquiry for learning particular science concepts. Digital tools were sourced and integrated into this design framework to substitute for the support typically offered by teachers and peers in classrooms.

The findings suggest that the POEE pedagogical design facilitated the inquiry process through promoting self-regulation and engaged exploration. It also promoted positive emotions in students towards the scaffolded learning modules.

Integrating technologies that benefit students differentially in educational settings remains a considerable challenge. More specifically, in science education, an appropriate inquiry learning context that allows access to well informed pedagogical design is imperative. The application of this inquiry-based scaffolding framework can inform educators in the process of creating their own instructional designs and contexts to provide more effective guided learning.

Self-regulation is the level of learning where the learner becomes an active agent in their learning process in terms of activity and aspects of motivation and metacognition. The current paper mostly deals with the metacognitive aspect. The purpose of this study is to gain insight into self-regulation of learning in the context of modern technology in higher education. This study also aims to highlight the direction, tendencies and trends toward which self-regulation of learning is moving in relation to modern technologies.

The review study was compiled via searches in three databases: Scopus, Web of Science and ERIC. A filter was used to search for empirical studies solely in English, published over the past decade on the topics of self-regulation of learning and technology in higher education.

The findings clearly show a correlation between self-regulation of learning and modern technology, especially after a significant event such as the Covid-19 pandemic. However, in the wake of this change, the field of education has seen the emergence of methods and new platforms that can provide support for the development of self-regulated learning strategies.

The originality of the study lies in the fact that it focuses on the link between self-regulation of learning and modern technologies in higher education, including some predictions of the future direction of self-regulation of learning in this context.