Credit and Recognition for Contributions to Data-Sharing Platforms Among Cohort Holders and Platform Developers in Europe: Interview Study

Background The European Commission is funding projects that aim to establish data-sharing platforms. These platforms are envisioned to enhance and facilitate the international sharing of cohort data. Nevertheless, broad data sharing may be restricted by the lack of adequate recognition for those who share data. Objective The aim of this study is to describe in depth the concerns about acquiring credit for data sharing within epidemiological research. Methods A total of 17 participants linked to European Union–funded data-sharing platforms were recruited for a semistructured interview. Transcripts were analyzed using inductive content analysis. Results Interviewees argued that data sharing within international projects could challenge authorship guidelines in multiple ways. Some respondents considered that the acquisition of credit for articles with extensive author lists could be problematic in some instances, such as for junior researchers. In addition, universities may be critical of researchers who share data more often than leading research. Some considered that the evaluation system undervalues data generators and specialists. Respondents generally looked favorably upon alternatives to the current evaluation system to potentially ameliorate these issues. Conclusions The evaluation system might impede data sharing because it mainly focuses on first and last authorship and undervalues the contributor’s work. Further movement of crediting models toward contributorship could potentially address this issue. Appropriate crediting mechanisms that are better aligned with the way science ought to be conducted in the future need to be developed.

Responsive Dashboard as a Component of Learning Analytics System for Evaluation in Emergency Remote Teaching Situations

The pandemic crisis has forced the development of teaching and evaluation activities exclusively online. In this context, the emergency remote teaching (ERT) process, which raised a multitude of problems for institutions, teachers, and students, led the authors to consider it important to design a model for evaluating teaching and evaluation processes. The study objective presented in this paper was to develop a model for the evaluation system called the learning analytics and evaluation model (LAEM). We also validated a software instrument we designed called the EvalMathI system, which is to be used in the evaluation system and was developed and tested during the pandemic. The optimization of the evaluation process was accomplished by including and integrating the dashboard model in a responsive panel. With the dashboard from EvalMathI, six online courses were monitored in the 2019/2020 and 2020/2021 academic years, and for each of the six monitored courses, the evaluation of the curricula was performed through the analyzed parameters by highlighting the percentage achieved by each course on various components, such as content, adaptability, skills, and involvement. In addition, after collecting the data through interview guides, the authors were able to determine the extent to which online education during the COVID 19 pandemic has influenced the educational process. Through the developed model, the authors also found software tools to solve some of the problems raised by teaching and evaluation in the ERT environment.

Fichas para avaliação dos trabalhos de microscopia em Histologia

Considerations, about the evaluation system on microscopy as for the teaching of HISTOLOGY. It is presented the kind of questionary which allows easier application of frequent evaluations of knowledge. Such a test has demonstrated to be faster and more homogenneous thran the usual methods of evaluation.

Research on the Evaluation System of College Students’ Ideological Education Supporting Smoking Banon Campus in the AI Era-Based on the Analysis Framework of AGIL

Objectives: Artificial intelligence has profoundly changed the way of university education. The tobacco ban on university campuses has become a consensus among people. The public smoking ban among university students requires both external constraints and internal ideological education. The evaluation system construction of college students’ ideological education is an effective way to improve the quality of college students’ ideological education, and it has a guiding role in promoting the ideological education of college students. The evaluation system is a whole, so we should start from structural function, and pursue the system construction and implementation path from the inside to the outside. Parsons Structural Function Analysis Framework (AGIL) is a systematic analysis method to study the structural function of social action. Using AGIL for analysis, the evaluation subject, evaluation objective, evaluation organization and evaluation system constitute the functional structure of the evaluation. To build an ideological education evaluation system for college students, the implementation path involves optimizing evaluation subject and promoting result evaluation; focusing on evaluation objectives and enhancing process evaluation; strengthening evaluation organization and breaking through value-added evaluation; improving evaluation system and perfecting comprehensive evaluation.

Network Game of Student Training Quality Assessment System Based on KOHONEN Neural Network

Objectives: In recent years, with the continuous improvement of the requirements of student training quality, the evaluation results of the existing evaluation system of student training quality are mostly unsatisfactory. Therefore, by integrating c-mean algorithm and Kohonen clustering algorithm, a non-sequential artificial neural network is obtained, a student training quality evaluation system based on KOHONEN neural network is designed by automatically adjusting the size of the objective function nodes of the non-sequential artificial neural network. Then the evaluation system is applied to the expected evaluation of the training quality of students in two science classes of Xinghua Middle School in Shenyang, Liaoning Province. The comparison between the test result data and the expected results of the model after the experiment confirms that the evaluation results obtained by using the evaluation system based on KOHONEN neural network have high accuracy.

Evaluation System Transformation of Multi-Scale Cultivated Land Quality and Analysis of Its Spatio-Temporal Variability

To master the transformation method and spatio-temporal variation characteristics of cultivated land quality at multiple scales, this paper constructed three spatial scales (Laixi city, Qingdao city, and Shandong province) and two temporal scales (the second survey (2007) and the third survey (2020)), and used a linear model to transform the evaluation system. Descriptive statistics, area statistics, spatial distribution, and aggregation analysis were used to explore the spatial scale variability, and the dynamic variation characteristics were analyzed. The results showed that (1) the R2 of scale transformation models are more than 0.826, which has a simple structure and strong universality; (2) with the administrative scale increases, the evaluation units’ number decreases, the spatial distribution is generally similar but progressively approximate, the high and low land levels gradually change to medium-level land, and the spatial aggregation degree is county-scale > provincial-scale > city-scale, with significant scale effect; and (3) in the past ten years, the average grade has increased from 6.26 to 6.13 in Laixi city, but still has much room for development. This study puts forward a method of spatio-temporal scale transformation and scale effect analysis for cultivated land quality, which has positive significance for improving the evaluation system, promoting land protection, and regional sustainable development.