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The study examined the impact of feedback types through a learning management system (LMS) on employees’ training performance. The purpose of this study is to establish effective feedback on advanced technologies for promoting corporate training.

A total of 148 trainees were recruited from a multinational medical company. Employees were randomly assigned to receive feedback from shallow to more constructive details on their learning performance with LMS. Data sources included are employees’ goal setting (GS) performance evaluated by the experts and their posttest scores obtained from the LMS. A series of statistical analyses were performed to investigate the impact of feedback intervention on employees’ GS and their impacts on corporate training results.

GS has a significant impact on learning outcomes. Employees who set greater specific goals attained higher scores. Furthermore, feedback with more formative evaluation and constructive developmental advice resulted in the most significant positive influence on the relationship between participants’ GS and learning outcomes.

Organizations can benefit from delivering appropriate feedback using LMS to enhance employees’ GS and learning efficacy in corporate training.

This study is one of the first to examine the moderating effect of feedback provided by LMS on GS and online learning performance in corporate training. This study contributes to GS theory for practical application and proposes a viable method for remote learning. The current study’s findings can be used to provide educational psychological insights for training and learning in industrial contexts.

Employee training and learning have transformed over the years. The movement from classroom training to the blended format represents the magnitude of this evolution. This has placed much attention on self-regulated learning. This study aimed to understand the individual and organizational mechanisms that sustain the formal learning process in organizations. It explored the goals the organizations and employees strive to achieve by investing in learning. Through this, the authors investigated how technology assistance makes learning more goal-oriented, despite the possibility of different goals for different stakeholders. They also examined how person-job fit can be achieved in employee training.

The study adopted a grounded theory-based inductive approach using a qualitative inquiry that used in-depth interviews of employees working in the Indian IT/ITES sector. This sector is knowledge-intensive and engages in constant skill development. A content analysis of the interview transcripts unraveled the most relevant themes from the participants' discussion.

Individual learners use dimensions of self-regulated learning to set and achieve goals such as better performance and career development. On the other hand, organizations use learning support mechanisms such as better access and flexibility to direct employee learning behavior to achieve organizational goals. Focusing on goal congruence leads to better achievement of results. Goal congruence also implies good person-organization fit.

This research established how aligning individual and organizational mechanisms can help achieve training goals that ultimately contribute to organizational performance. The study differentiated itself by investigating training goal setting and goal achievement at two levels – organizational and individual – using a qualitative approach. It also showed how goal congruence is vital in improving organizational performance and how technology-enabled training practices rely on self-regulated learning and help achieve goal congruence.

This study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.

The authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.

Empirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).

Efficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

This study aims to construct a scale and identify the factors that might affect the implementation of gamification and Bloom's Digital Taxonomy-based assessment of students' learning, guided by the Goal-Setting Theory.

This study used a sequential exploratory mixed-methods design where data were collected qualitatively via interviews (n = 18) and field notes. Quantitative analysis was then applied via questionnaires (n = 96).

Four major themes emerged following thematic analysis of the observation notes and interview data: learning engagements; understandable learning goals; friendly competitions; and interactive learning feedback. A scale consisting of 32 items was developed based on the qualitative data. The quantitative data were then collected to evaluate the scale's factor structure using maximum likelihood exploratory factor analysis with Promax factor rotation. The final version of the scale had 29 items. The authors verified the alpha coefficients for each element and the entire hierarchy, ranging from 0.94 to 0.98 for the factors. The total scale was determined to be 0.96, and the Item Content Validity Index was considered valid.

The scale developed in this study is expected to inform future research and guide educators who wish to incorporate contemporary digital pedagogies in a different context. This study could also be beneficial to researchers who aspire to perform research on Goal-Setting Theory in the educational context.

This article details findings on how leaders of deeper learning schools establish, maintain, and propel unique teaching and learning environments. In this case study, the authors present findings from data collected through interviews with 30 leaders of self-proclaimed deeper learning initiatives and site visits to those elementary and secondary schools.

Using a case study approach, the authors collected data from interviews and observations of 30 school leaders.

The study's findings indicate how leaders of schools that engage in deeper learning tend to adhere to three core practices. First, the leaders of deeper learning schools in this study intently listened to the community to ascertain needs and desires; this drove the vision. Second, leaders of deeper learning schools created learning spaces that empowered students and gave them voice, agency, and choice. Third, leaders of deeper learning schools sought to humanize the schooling experience.

This study provides actionable examples of what leaders currently do to engage kids and teachers in deeper learning. These leaders offer insights into specific actions and practices that they espoused to make the schooling experience markedly different.

Previous studies focused on the deeper learning of schools and students. This is one of the first studies to focus on the inteplay between deeper learning and school leaders.

The paper discusses the use of co-design for staff professional learning within higher education. It suggests that three distinct approaches to professional learning can be characterised as help-yourself platforms/services, drive-by workshops and co-design workshops. It makes pragmatic suggestions for where co-design might be used and heuristics for its successful use, based upon the authors' collective experiences.

This practitioner paper presents a case-study of co-design in a university context. Staff from across disciplinary boundaries were brought together to co-design novel learning experiences for students for a non-traditional context.

Findings from a case study are used to highlight the strengths of a co-design approach, as understood through the lenses of networked learning and self-determination theory. It juxtaposes co-design for staff learning with other approaches and finds it to be valuable and underutilised.

The research discusses a single case study involving two workshops with a sample size of 112 participants. It is included as an example of co-design for professional learning in higher education.

Co-design for professional learning in higher education is poorly understood and presently underutilised. This paper addresses this gap by presenting an example of co-design for professional learning in higher education and theorising its significance.

This paper aims at identifying crucial factors that affect student satisfaction with synchronous e-learning. For this purpose, a research model comprised of perceived usefulness (PU), perceived ease of use (PEU), computer anxiety (CA), self-efficacy (SE), system accessibility (SA), perceived interaction (PI) and perceived flexibility (PF) was developed.

For the purpose of this research, an online questionnaire was used. All of the measures are adapted from previously validated instruments and adjusted to fit the research aim. Accordingly, the questionnaire resulted in 49 items, which yielded eight constructs. To test the hypothesized model, structural equation modeling was employed on a valid sample of 263 higher education students.

According to the results, PU, PI, CA and flexibility were all found to be significant. PU demonstrated the most contribution following by PF and PI.

Even though this study has included some critical factors theorized to influence e-learning settings, it does not incorporate all elements. In terms of sample size, a bigger sample would be more favorable. Lastly, the study took place during the COVID-19 pandemic, where free movement and social activities were restricted, which may have influenced students' perception of synchronous e-learning.

This research contributes to a greater understanding of the student experience with synchronous e-learning, and its findings can provide relevant stakeholders, particularly e-learning practitioners, with insights into effectively adopting and improving such settings.

This study uses an integrated model of several constructs to investigate student satisfaction in terms of just synchronous e-learning rather than e-learning in general. Several validated instruments were used and tested in a new context and sample. Additionally, the study provides evidence during COVID-19.

This paper aims to study the relationship between the ball grid array (BGA) flip-chip underfilling process parameter and its void formation region.

A set of top-down scanning acoustic microscope images of BGA underfill is collected and void labelled. The labelled images are trained with a convolutional neural network model, and the performance is evaluated. The model is tested with new images, and the void area with its region is analysed with its dispensing parameter.

All findings were well-validated with reference to the past experimental results regarding dispensing parameters and their quantitative regional formation. As the BGA is non-uniform, 85% of the test samples have void(s) formed in the emptier region. Furthermore, the highest rating factor, valve dispensing pressure with a Gini index of 0.219 and U-type dispensing pattern set of parameters generally form a lower void percentage within the underfilling, although its consistency is difficult to maintain.

This study enabled manufacturers to forecast the void regional formation from its filling parameters and array pattern. The filling pressure, dispensing pattern and BGA relations could provide qualitative insights to understand the void formation region in a flip-chip, enabling the prompt to formulate countermeasures to optimise voiding in a specific area in the underfill.

The void regional formation in a flip-chip underfilling process can be explained quantitatively with indicative parameters such as valve pressure, dispensing pattern and BGA arrangement.

This conceptual study investigates the adoption of education technology (EdTech) products among college students, focusing on identifying the key factors influencing the adoption process within educational institutions. Technology integration in education has rapidly gained prominence, with EdTech offering innovative solutions to enhance teaching and learning experiences. However, understanding the determinants that affect EdTech adoption remains critical for its successful implementation and impact. This paper aims (1) to identify the factors influencing the adoption of EdTech by college students (2) to create a conceptual model that shows the connections between the elements that lead to college students adopting EdTech.

The research employed a mixed-methods approach, combining qualitative data analysis and conceptual modeling to achieve the objectives. The underlying knowledge required to create a qualitative data gathering tool was obtained through a thorough literature analysis on innovation dissemination, educational psychology and technology adoption. College students, teachers and administrators participated in semi-structured interviews, focus groups and surveys to provide detailed perspectives on their attitudes about and experiences with EdTech. The Scopus and Web of Science databases are searched for relevant information in an organized manner in order to determine the factors influencing the adoption of EdTech. Second, an extended version of the technology adoption model is adopted to develop a qualitative data-based conceptual framework to analyze EdTech adoption in the Indian context.

Overall, by highlighting the critical components that emotionally influence college students' adoption of EdTech products in educational institutions, this course adds to the body of information already in existence. The conceptual framework model serves as a roadmap for educational stakeholders seeking to leverage EdTech effectively to enrich the learning environment and improve educational outcomes. By recognizing the significance of the identified factors, academic institutions can make informed decisions to foster a climate conducive to successful EdTech integration.

A comprehensive conceptual framework model was developed based on qualitative data analysis to illustrate the interrelationships between the identified factors influencing EdTech adoption. This model presents a valuable tool for educational institutions, policymakers and EdTech developers to comprehend the complex dynamics of implementing these technological solutions.

The findings of this study demonstrated a number of important variables that affect the uptake of EdTech products in educational settings. These factors encompassed technological infrastructure, ease of use, perceived usefulness, compatibility with existing academic practices, institutional support, financial constraints and individual attitudes towards technology. Additionally, the research explored the significance of institutional preparation for embracing technological advancements as well as the influence of socio-cultural elements.