Modern technologies have intensified innovations and necessitated changes in public service processes and operations. Continuous employee learning development (CELD) is one means of the molecule-atom that keep employees motivated and sustain competitiveness. The study explored the efficacy of CELD in relation to modern technology in the South African (SA) public service departments between 2014 to 2023 era. Departments are faced with challenge of equipping their employees with adequate professional and technical skills for both the present and the future in order to deliver specific government priorities. Data for the study were gathered utilizing a qualitative semi-structured e-questionnaire. The study sample consisted of 677 human capital development practitioners from national and provincial government departments in SA. The inefficacy CELD and the inadequacy of technological infrastructure and service delivery can be attributed to the failure by executive management and senior managers to invest in CELD to prepare employees for digital world. It is recommended that departments should use Ruggles’s knowledge management, Kirkpatrick’s training, and Becker and Schultz’s human capital models as sound measurement tools in order to gain a true return on investment. The study adds pragmatic insight into the value of CELD in the new technological environment in public service departments.

Taking learning as the basis, practice as the path, and competition as the promotion. In the process of coordination and unity of learning-practice-competition, it can promote students' learning motivation, strengthen students' practice motivation, and promote students' active performance in competition activities. Under the influence of positive self-efficacy performance, active sense of achievement, etc., it can promote students' interest and experience in sports activities, strengthen students' learning effects, and promote the active construction of high-quality sports classrooms in junior high schools. Next, this article will discuss the effective construction of a high-quality junior high school sports classroom under the background of the integration of "learning-practice-competition" based on its own junior high school physical education teaching practice.

This research quantitatively examines how technology-mediated formative assessment techniques affect student learning outcomes in middle school education. The research investigates the correlation between instructors’ technology use, attitudes, and student performance in several academic disciplines using surveys and evaluations conducted with teachers and students. Results show strong positive connections between how often technology is used, the specific digital tools used, how effective technology-mediated formative assessment is judged to be, and the results of student learning. On the other hand, obstacles to implementation were shown to have a negative relationship with student accomplishment. The research emphasizes that technology-mediated formative assessment is more successful in some subjects, emphasizing the necessity to customize teaching methods for each subject’s requirements. The study revealed a positive correlation between student learning outcomes and the frequency of technology use, the types of digital tools used, and the perceived effectiveness of technology-mediated formative assessment. These results suggest ways to improve the use of technology and formative assessment in middle school instruction.

Recognizing the discipline category of the abstract text is of great significance for automatic text recommendation and knowledge mining. Therefore, this study obtained the abstract text of social science and natural science in the Web of Science 2010-2020, and used the machine learning model SVM and deep learning model TextCNN and SCI-BERT models constructed a discipline classification model. It was found that the SCI-BERT model had the best performance. The precision, recall, and F1 were 86.54%, 86.89%, and 86.71%, respectively, and the F1 is 6.61% and 4.05% higher than SVM and TextCNN. The construction of this model can effectively identify the discipline categories of abstracts, and provide effective support for automatic indexing of subjects.