A transition from pre-service training programs to teaching is a dramatic and somehow painful experience for novice teachers. The question is what difficulties novice teachers face and how they negotiate their professional identity to cope with difficulties and find joys in their career. This study is aimed to investigate novice teachers’ professional identity reconstruction, from their imaged-identities to their practiced identities. The use of semi-structured interviews collected data from four Vietnamese English as a foreign language (EFL) novice teachers. According to the data, cue-based was the most common type of novice teachers’ imagined identity. Regarding the practiced identities, the interviewees reported different professional identity reconstructions in the first five years of teaching practice. The participants’ excerpts enlisted some challenges that the novices faced such as students’ learning attitudes, working environments, or unorganized colleagues. Based on the research findings, some solutions were proposed in order to help novice teachers get through their difficult times at the very beginning of their career.

The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development.

Student dropouts led to a squandering of the education budget. The education system and society are significantly affected, particularly in terms of potential development. To ensure vocational students graduate and secure satisfactory employment in line with the field of study. Implementing a comprehensive system that encompasses promoting, supporting, preventing, and resolving various student issues is essential. This system includes close, meticulous care and support, timely and appropriate interventions, enhancement of life skills, guidance, and holistic student development. This research found that the risk factors in the teaching and learning process account for 90.78 percent of the reasons scholarship students drop out of the education system; there are instances of absenteeism, inappropriate behavior, and a dislike for the teacher and the subject they are teaching. Additionally, the care and support system for vocational students at risk of dropping out consists of four components: Component 1: living care; Component 2: dropout protection; Component 3: counseling and advising; and Component 4: transfer to support. The empirical evaluation of the care and support system for students concluded that the overall assessment was highly suitable. The information should be utilized for planning and policymaking in educational institutions.

Academic studies are an important stage in the development of professional identity and perceptions regarding a person’s future occupation. This study examined the relationships between experiences during college studies and professional identity and motivation to work in the studied field among female undergraduates from the Ultra-Orthodox sector. The study employed a cross-sectional survey design with a sample of 93 participants, who were all studying for B.A. degrees on an Ultra-Orthodox college campus. At the end of the 2023 academic year, participants completed anonymous, self-report questionnaires about their attitudes toward academic studies, professional identity, and motivation to work. The data from the questionnaires were analyzed using t-tests, ANOVA, correlation analyses, and multiple regression. The findings revealed significant, positive relationships between satisfaction with one’s studies and professional identity and between professional identity and motivation to work in one’s field of study. Contrary to what had been hypothesized, no relationship was found between the students’ satisfaction with their studies and their motivation to work in their field of study. In addition, the professional identity of students in the later years of their programs was no stronger than that of first-year students. This indicates that perceptions of one’s college studies are not directly related to occupation or motivation for work. The findings show that the year in one’s study program did not affect work motivation, suggesting that the link between academic experience and occupation is weak. In addition, the fact that professional identity did not act as a mediating factor in the examined model suggests that professional identity may develop before individuals begin their college studies.