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The purpose of this study is to propose and examine an integrated learning model for improving training effectiveness in workplace learning. Specifically, this study investigated the effect of achievement goal-setting intervention across three groups of new employees from a multinational medical company. During a three-day remote training program, the role of each achievement goal orientation (AGO) in goal setting intervention and their relations with trainees’ applied learning strategies were examined. This study proposed and validated an integrated training model for improving remote workplace learning effectiveness.

This study was based on two data sources, the pre- and posttests scores; time on task (deep learning: completing reflective practice) and time on content learning (surface learning: watching tutorials) retrieved from an adaptive learning platform. A total number of 133 participants were recruited in this study, and they were randomly assigned to three interventional groups. The intervention was grounded from the AGO theory and goal setting theory. A series of statistical analysis were conducted to examine the effect of each type of achievement goal setting as a prompt for new employees’ learning behavior and performance.

Results indicated that setting mastery goal at the beginning of the training program leads to productive learning outcomes. Compared with the groups being required to set performance goal (final rank) or not to set any goal for the training purpose, trainees’ who were assigned to set a mastery goal (final performance score) performed statistically significantly higher than the other groups. Additionally, learners who set mastery goal spent higher proportion of time on deep learning than learners from the other groups. The results proved mastery goal setting as an effective prompt for boosting workplace learning effectiveness.

Organizations and institutions can take setting mastery approach goals as a prompt at the beginning of the training to increase learning effectiveness. In this way, trainees are promoted to apply more deep learning strategies and achieve better learning outcomes while setting mastery goal for their training purpose.

To the best of the authors’ knowledge, this study was the first to combine the intervention of goal setting and types of AGOs into workplace learning. This study adds to previous research on goal setting theory and AGO theory for the practical application and proposes an effective model for learners’ adaptive remote learning. Findings of this study can be used to provide educational psychological insights for training and learning in both industrial and academic settings.

Drawing on self-determination theory (Deci and Ryan, 2012) and Hew’s (2016) five-factor model, our study aimed to investigate the impact of two online training design factors (instructor accessibility and active learning) on learner’s self-efficacy and learning outcome amid the COVID-19 pandemic.

A randomized pretest-posttest control group field experiment was designed to assess participants’ self-efficacy and learning outcome of an online training program - territory business management plan, under three conditions: instructor accessibility, active learning, and controlled. Participants (N=87) were medical sales representatives from a Fortune-Global 500 pharmaceutical company’s subsidiary office in China. Data was analyzed with 2 (time) x 3 (group) MANOVA with time (pretest and posttest) as a repeated measure to investigate differences in changes in self-efficacy and learning outcome between three groups.

Overall, participants’ self-efficacy and learning outcome were significantly improved via the online training program for all three groups. Specifically, the impact of the training on learning outcome was the strongest for the active learning group, less strong for the instructor accessibility group, and the least strong for the control group.

Our research contributes towards understandings of the effectiveness of online talent training programs by examining two critical instructional design factors during a time of crisis. Our findings suggest that active learning (interactions with the training materials by purposeful self-reflection) might be a stronger predictor for increasing learning outcome than instructor accessibility (receiving feedback and tutoring sessions from the instructor) for online training programs.

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.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The positive impact of training can be enhanced if learners set appropriate goals at the outset. By opting to pursue a mastery goal, learners are likely to engage more with the deep learning strategies that are associated with a broader range of desirable outcomes for both learners and their organization.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Emphasis on achievement goal orientation can enable firms to realize key earning outcomes from workplace training. Desired outcomes become considerably likelier when learners set a mastery-approach goal as opposed to other goal types at the outset of training.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

A continuous chemical vapor deposition (CVD) method has been used to fabricate pyro-carbon (PyC) coating on continuous silicon carbide (SiC) fibers. The paper aims to evaluate these coated fibers by testing filament tensile and using microstructure characterization.

The continuous SiC fiber-reinforced SiC matrix (SiC/SiC) composite is widely studied in aerospace and nuclear applications. The PyC is the probable option in fusion and fast reactor. However, the conventional fabrication method of PyC coating has some drawbacks influencing performance and efficiency.

The results showed that PyC-coated SiC fibers with continuous CVD method are more straight than conventional ones and residual deformations could not be observed, and these PyC coatings have complete geometry and uniform thickness. In different process conditions, the thickness of PyC coating could control from ∼100 to ∼1,000 nm.

The coated SiC fibers in a lower gas ratio (1:7 to 1:3), lower pressure (500–1,000 Pa) and appropriate winding speed (3 to 5 rpm) have relative high filament tensile strength (∼3.5 to ∼3.9 GPa). And the strength of coated SiC fibers has a negative correlation with the measured thickness of PyC coating. A distinctive growth process was discovered in the continuous CVD method. In a certain range, the quicker growing rate of PyC is obtained in shorter deposition time which means an efficient and quality method could be applied to fabricate coatings.

The purpose of this paper is to reduce the difficulty of model predictive control (MPC) deployment on FPGA so that researchers can make better use of FPGA technology for academic research.

In this paper, the MPC algorithm is written into FPGA by combining hardware with software. Experiments have verified this method.

This paper implements a ZYNQ-based design method, which could significantly reduce the difficulty of development. The comparison with the CPU solution results proves that FPGA has a significant acceleration effect on the solution of MPC through the method.

Due to the limitation of practical conditions, this paper cannot carry out a hardware-in-the-loop experiment for the time being, instead of an open-loop experiment.

This paper proposes a new design method to deploy the MPC algorithm to the FPGA, reducing the development difficulty of the algorithm implementation on FPGA. It greatly facilitates researchers in the field of autonomous driving to carry out FPGA algorithm hardware acceleration research.