The Corelation Of Students’ Mathematics Learning Engagement On Their Academic Performance In Junior High School

In this study, 312 junior high school students are tested on their mathematics learning engagement and achievement. This research will also find out the connection between the students’ mathematics learning engagement with their mathematics academic performance. The results of this research show that junior high school students’ mathematics learning engagement is closely related to their mathematics academic performance in which cognitive engagement has the highest correlation and emotional engagement has the lowest correlation with students’ mathematics academic performance. The result also shows that when there is a higher mathematics academic achievement, the mathematics learning engagement will also be higher. This research also shows that gender factor doesn’t really affects the students’ mathematics learning engagement but their geographic factor does affect the learning engagement

Enhancing Jewish Learning & Engagement in Preschool Life: Documenting the JRS Model

The Jewish Resource Specialist (JRS) Initiative, designed in 2008 by the Early Childhood Education Initiative (ECEI) of the Jewish Community Federation of San Francisco, the Peninsula, Marin and Sonoma Counties (the Federation), in partnership with the Jim Joseph Foundation, positions the early childhood years as a gateway into Jewish life for children and their families. It is a response to several catalyzing factors. First, preschool is a critical time for young families. Children are eager to learn and are developing socially, emotionally, cognitively and spiritually. For parents, at no other moment will they be so involved in their children's schooling. They are also choosing how they spend their time and with whom they spend it. The JRS Initiative came about to leverage this unique time for families.Second, the JRS Initiative also addresses the dearth of leaders working to build the field of Jewish early childhood education (ECE). Those who want to focus on Jewish ECE and build communities of engaged Jewish families with preschool-aged children are challenged to find the support, mentors and professional development opportunities they need to craft a career path. The JRS Initiative seeks to meet these field-wide demands by developing the skills and Jewish knowledge of the JRS educators who then bring ideas and guidance to their schools

Enhancing Learning Engagement and Academic Performance: A Systematic Review among Private College and University Students in Xi'an, China

This systematic review aims to explore the factors influencing learning engagement and its impact on learning performance among private college and university students in Xi'an, China. Learning engagement is a crucial element in students' educational experiences, and understanding the factors that contribute to it can significantly enhance learning outcomes. By systematically reviewing and synthesizing existing literature, this study identifies the key factors influencing learning engagement and investigates the relationship between learning engagement and learning performance. The findings of this review can inform educational institutions and policymakers in designing effective strategies to enhance student engagement and improve academic performance

How Academic Self-Efficacy Influences Online Learning Engagement: The Mediating Role of Boredom

Academic self-efficacy and boredom were identified as key predictors of learning engagement in online learning. However, there has been little research designed to examine the mediating role of boredom in the relationship between academic self-efficacy and online learning engagement. To address this gap in knowledge, the present study utilizes social cognitive theory, control-value theory, and the self-system process model to examine the following: (1) the impact of academic self-efficacy on three sub-dimensions of learning engagement in online learning; and (2) whether four sub-dimensions of boredom mediate the relationships between academic self-efficacy and the three sub-dimensions of learning engagement in online learning. Data were collected from 528 university students (Mage = 19.77, SDage = 1.24) who voluntarily completed questionnaires assessing academic self-efficacy, boredom, and learning engagement. The results of the structural equation modeling indicated the following findings: (1) academic self-efficacy can predict online learning engagement; (2) affective boredom mediates the relationship between academic self-efficacy and behavioral and cognitive engagement; (3) cognitive boredom mediates the relationship between academic self-efficacy and cognitive engagement; (4) motivational boredom mediates the relationship between academic self-efficacy and behavioral and emotional engagement; and (5) physiological boredom mediates the relationship between academic self-efficacy and behavioral, emotional, and cognitive engagement. Finally, this study supports the notion that academic self-efficacy can influence learning engagement by addressing boredom in online learning. It also offers significant theoretical and practical implications for promoting students’ release from boredom and enhancing their engagement in online education

Factors that Influence Informal Learning in the Workplace

Purpose – The purpose of this study was to investigate factors that influence informal learning in the workplace and the types of informal learning activities people engage in at work. More specifically, the research examined (1) the relationship between informal learning engagement and the presence of learning organization characteristics, and (2) perceived factors that affect informal learning engagement. Methodology – Workplace learning and performance improvement professionals were invited to respond to an anonymous online survey, and 125 professionals volunteered to participate in the study. Findings – This study did not find a significant correlation between informal learning engagement and the presence of learning organization characteristics. While age and education level did not impact informal learning engagement, it was found that older workers tended to engage in more informal learning. There were also certain types of informal learning activities in which they were most likely to engage. The findings also include rank-ordered lists of personal and environmental factors that workers perceived to influence their engagement in informal learning. Practical implications – The rank-ordered lists of factors that influence informal learning engagement is likely to be useful to practitioners for prioritizing informal learning interventions. The results of this study suggest that the degree of engagement in informal learning alone would not be a sufficient construct for predicting the presence of learning organization characteristics. Originality/value of paper – Very little empirical research has attempted to connect individual learning to the learning organization concept. This research addresses that gap by examining the relationship between individual informal learning engagement and the presence of learning organization characteristics

Instructors’ emotional intelligence and learning engagement of online students

© 2023 Patel et al. published by Sciendo. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).This study explored how online students perceived their instructors’ emotional intelligence (EI) and its impact on their learning engagement. Using eight EI behaviours of online instructors and a learning engagement instrument, 100 online university students were surveyed regarding their observation of those EI behaviours and their learning engagement. Regression analysis indicated that 27.2% of the variance in learning engagement could be attributed to four dimensions of EI behaviours (R2 = 0.272, F (4, 95) = 8.873, p < 0.001). Instructors providing individual support, demonstrating concern for the students’ situations, adapting their resources to online delivery, and being honest about their situation and challenges were associated with higher student learning engagement.Peer reviewe

Big Data Techniques to Improve Learning Access and Citizen Engagement for Adults in Urban Environments

This presentation explores the emerging concept of ‘Big Data in Education’ and introduces novel technologies and approaches for addressing inequalities in access to participation and success in lifelong learning, to produce better life outcomes for urban citizens. It introduces the work of the new Urban Big Data Centre (UBDC) at the University of Glasgow, presenting a case study of its first data product – the integrated Multimedia City Data (iMCD) project. Educational engagement and predictive factors are presented for adult learners, and older adult learners, in a representative survey of 1500 households. This was followed up with mobility tracking data using GPS data and wearable camera images, as well as one year’s worth of contextual data from over one hundred web sources (social media, news, weather). The chapter introduces the complex dataset that can help stakeholders, academics, citizens and other external users examine active aging and citizen learning engagement in the modern urban city, and thus support the development of the learning city. It concludes with a call for a more three-dimensional view of citizen-learners’ daily activity and mobility, such as satellite, mobile phone and active travel application data, alongside administrative data linkage to further explore lifelong learning participation and success. Policy implications are provided for addressing inequalities, and interventions proposed for how cities might promote equal and inclusive adult learning engagement in the face of continued austerity cuts and falling adult learner numbers

Applying a social justice framework to ensure good practice in monitoring student learning engagement

A current Australian Learning and Teaching Council (ALTC) funded action research project aims to provide a set of practical resources founded on a social justice framework, to guide good practice for monitoring student learning engagement (MSLE) in higher education. The project involves ten Australasian institutions, eight of which are engaged in various MSLE type projects. A draft framework, consisting of six social justice principles which emerged from the literature has been examined with reference to the eight institutional approaches for MSLE in conjunction with the personnel working on these initiatives during the first action research cycle. The cycle will examine the strategic and operational implications of the framework in each of the participating institutions. Cycle 2 will also build capacity to embed the principles within the institutional MSLE program and will identify and collect examples and resources that exemplify the principles in practice. The final cycle will seek to pilot the framework to guide new MSLE initiatives. In its entirety, the project will deliver significant resources to the sector in the form of a social justice framework for MSLE, guidelines and sector exemplars for MSLE. As well as increasing the awareness amongst staff around the criticality of transition to university (thereby preventing attrition) and the significance of the learning and teaching agenda in enhancing student engagement, the project will build leadership capacity within the participating institutions and provide a knowledge base and institutional capacity for the Australasian HE sector to deploy the deliverables that will safeguard student learning engagement At this early stage of the project the workshop session provides an opportunity to discuss and examine the draft set of social justice principles and to discuss their potential value for the participants’ institutional contexts. Specifically, the workshop will explore critical questions associated with the principles