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Background With the introduction of the new dental licensing regulations (ZApprO) in Germany, preclinical teaching time was substantially reduced, particularly affecting practical training. To support students’ learning under these conditions, a formative Objective Structured Clinical Examination (OSCE) was implemented early in the preclinical curriculum. This study aimed to evaluate the impact of an early formative OSCE on undergraduate dental students’ learning behavior, self-assessment, and exam preparation. Methods A total of 71 undergraduate dental students (mean age 22 years) participated voluntarily in a formative OSCE in preventive dentistry during the summer semester 2022 and winter semester 2022/23. Students were randomly assigned to an intervention group (OSCE halfway through the semester) or a control group (OSCE shortly before the final exam). The OSCE included five stations developed according to the National Competence-Based Learning Objectives Catalog for Dentistry. Students completed pseudonymized questionnaires at two time points (T0: after OSCE; T1: after the final exam). The questionnaire assessed learning behavior (including strategies for dealing with difficult material and use of additional resources), self-assessment (perceived learning status and exam readiness), motivation, and exam preparation. Statistical analyses were performed using Mann–Whitney U and Wilcoxon tests. Results Participation in the formative OSCE enabled students to better evaluate their learning status and identify individual learning needs for the final exam. Although both groups started exam preparation at similar times (T0: p  = 0.422; T1: p  = 0.674), the intervention group reported higher initial motivation and greater awareness of knowledge gaps after the OSCE. Differences were also observed in how students dealt with difficult material and in their use of supplementary learning resources. Conclusions An early formative OSCE fosters undergraduate dental students’ self-assessment, reflection on learning behavior, and awareness of learning needs, thereby supporting more targeted exam preparation. However, it does not necessarily lead to an earlier start of study activities. Implementing formative OSCEs in the middle of the semester, accompanied by structured feedback, may further enhance their educational impact.

Multicultural integration brings opportunities and challenges to the field of education, and the innovation of Civics education in higher vocational colleges and universities becomes increasingly essential. In this paper, the reinforcement learning algorithm is used to construct a Civics teaching mode with precise teaching intervention, which quantifies students’ learning ability and learning status by combining the Civics learning ability level parameter. The difficulty coefficient of Civics exercises determines the students’ mastery of Civics content. Based on the students’ Civics learning ability and learning status, a precise teaching intervention strategy was formulated, and the teaching intervention was used in practice to explore the effectiveness of the accurate intervention Civics teaching model in terms of the change of students’ ability and performance. The results show that under the Precision Intervention Civics Teaching Model, students with low collaborative learning and high problem-solving ability changed from unidirectional PR→RS to bidirectional PA↔PR, indicating that the Precision Intervention Teaching Model can improve students’ learning ability. The passing rate of students under this model were maintained at 70%, the average score was above 70, and the students’ performance in Civics was significantly improved compared with the traditional teaching model.

Awareness of students' learning status, and maintaining students' focus and attention during class are important issues in classroom management. Several observation instruments have been designed for human observers to document students' engagement in class, but the processes are time-consuming and laborious. Recently, with the development of artificial intelligent technologies, artificial intelligence in education (AIED) has become an important research topic. Several studies have applied image recognition technologies to determine students' learning status. However, little research has employed both sensor technology and image recognition technology in learning status analysis. Moreover, it remains unknown if learning status analysis is accurate enough to substitute for human observers. Furthermore, no feedback has been provided individually to students to manage their learning status by maintaining their attention in class. In this paper, a learning status management system in an intelligent classroom is proposed. Several types of information about students were detected and collected by both sensor technology and image recognition technology, and a Bayesian classification network was employed to inference the students' learning status. Moreover, the system includes a feedback mechanism, which not only provides the results of the just-in-time learning status analysis to teachers, but also notifies students who are detected as being unfocused in class. Two experiments were conducted to verify the accuracy and effectiveness of the proposed system. Results showed that the learning status analysis highly corresponded to the observation of human beings, and the students were more attentive in class.

In allusion to the problem that teachers not being able to timely grasp student dynamics during online classroom, resulting in poor teaching quality, this paper proposes an online learning status analysis method that combines facial emotions with fatigue status. Specifically, we use an improved ResNet50 neural network for facial emotion recognition and quantify the detected emotions using the pleasure-arousal-dominance dimensional emotion scale. The improved network model achieved 87.51% and 75.28% accuracy on RAF-DB and FER2013 datasets, respectively, which can better detect the emotional changes of students. We use the Dlib’s face six key points detection model to extract the two-dimensional feature points of the face and judge the fatigue state. Finally, different weights are assigned to the facial emotion and fatigue state to evaluate the students’ learning status comprehensively. To verify the effectiveness of this method, experiments were conducted on the BNU-LSVED teaching quality evaluation dataset. We use this method to evaluate the learning status of multiple students and compare it with the manual evaluation results provided by expert teachers. The experiment results show that the students’ learning status evaluated using this method is basically matched with their actual status. Therefore, the classroom learning status detection method based on facial expression recognition proposed in this study can identify students’ learning status more accurately, thus realizing better teaching effect in online classroom.

This research aims to examine the educational needs of visual arts teachers for the use of museums in line with the out-of-school approach to education in terms of learning status and professional seniority variables. The research was carried out using a scanning model from quantitative methods. Visual arts teachers (n=140) make up the universe of research. The results obtained from the researcher that the educational needs of teachers in terms of learning status variable; organizing pre-implementation activities, teaching strategy, using methods and techniques, communicating effectively, ensuring personal and social development and post-implementation activities has shown that it does not differ in regulatory dimensions. However, it has also been found that the educational needs of teachers differ in the way they regulate the educational environment. However, visual arts teachers' educational needs regarding the out-of-school education approach are regulated according to the professional seniority variable, regulating the environment, teaching strategy, using methods and techniques, communicating effectively, ensuringpersonal and social development and post-implementation it has been determined that it does not differ in the dimensions of organizing activities. However, when the opinions of teachers on all dimensions were examined, it was understood that teachers with professional seniority of 16 years or more had a lower degree of need for the approach. This result indicates that teachers in this group have more knowledge of the approach. However, it was found that teachers who needed more education than other teachers had professional seniority between 6-10 years.

This research examines the effects of a training program, which emphasized the use of social networks and social capital to encourage learning and adoption of a relatively new cash crop, cotton, to female heads of households. The impact of the social network based program is then compared to the impact of a concurrently run standard extension training program. Because both programs were randomized at the village level, I am able to identify and compare their impacts without the potential for confounding or within village spillover effects. The social network based program (SNP) implemented here had two stages in treatment villages: 1) it trained each participating female in one aspect of growing cotton (e.g. thinning, spacing, harvesting2). and 2) it exogenously increased the size of the average woman's social network by randomly pairing cotton growing women in mentoring relationships. The standard training program (TR) consisted of weekly visits and training sessions by agricultural extension agents during the stages of land preparation, weeding, planting, thinning, and harvesting. I show that the SNP increases productivity up to 50% for farmers producing at the average yield of production (200 kilograms of cotton per acre), and that the social network based training program had more significant effects on yields for the poorest performing farmers than the standard training program. Reprinted by permission of the American Agricultural Economics Association

The purpose of this study was to assess the status of e-learning in public universities in Kenya. Data were collected using questionnaires administered to both students and lecturers randomly sampled from seven public universities. Questionnaire responses were triangulated with interviews from key informants and focus group discussions (FGDs). Data were analyzed qualitatively and through use of descriptive statistics. Findings revealed that e-learning is at its infant stage in universities in Kenya. Majority of universities lacked senate approved e-learning policies to guide structured implementation. A few lecturers (32%) and students (35%) used e-learning and few courses (10%) were offered online. Majority of online uploaded modules (87%) were simply lecture notes and not interactive. Again, universities in Kenya lacked requisite ICT infrastructure and skills. The study recommends that universities partner with the private sector to improve ICT infrastructure, build capacity, and standardize e-learning programs in the country.

The paper presents a model of relative consumption where individual utility depends on consumption relative to peers (local status), relative rank of peer group in the economy (global status), and leisure. The resultant model offers an explanation of the increase in the working hours of the more productive US workers relative to the less productive ones over the last decade. The model further predicts that the relative labor supply based on productivities will be higher in the more populated areas. Reprinted by permission of the American Agricultural Economics Association

Peer networks provide their members new information about employment opportunities (Munshi and Rosenzweig 2006), shape available economic opportunities (Skoufias, Lunde, and Patrinos 2009), supply marital partners (Banerjee et al. 2009), facilitate adoption of new technologies (acite[Conley and Udry 2010]bib5; Montgomery and Casterline 1996). Montgomery and Casterline distinguish between two key effects of social networks: information and influence. In both cases, homophily-induced homogeneous networks may limit the network's ability to affect social norms or at least delay the process, since information and social norms are likely already common to the network, and may well presumably be reinforced instead of challenged by network connections. Indeed, economists have found both theoretical and empirical evidence suggesting that homophily slows social learning and therefore convergence in the adoption of new technologies (Behrman, Kohler, and Watkins 2002; Golub and Jackson 2011, 2010). Reprinted by permission of the American Agricultural Economics Association

One key manifestation of educational diversity is low socioeconomic status students and those who are otherwise marginalised from accessing higher education. This exploratory case study outlines and evaluates a long-running Australian pre-undergraduate preparatory program directed at providing maximum life and learning support to students by means that engage with and build on their diversities. Data are drawn from semi-structured focus groups with successive cohorts of students and theoretically-informed reflections by program staff members. The analysis of these data is framed by the conceptual blending of current theorising about transformative learning and capacity-building, which in combination constitute a powerful lens for illuminating student diversity in higher education. Based on that analysis, despite some inevitable limitations, the program is largely successful in its strategies to maximise life and learning support in order to mobilise the students' diversities in ways that enhance their current and prospective learning outcomes. [Author abstract]