Education, Courses, Workshops, Conferences

Energy Guide ◽  
2019 ◽  
pp. 125-132
Author(s):  
Virginia Bemis
Keyword(s):  
Education Courses

Social-Scientific Research Competency

2019 ◽  
Vol 35 (5) ◽  
pp. 737-750 ◽  
Author(s):  
Christopher Gess ◽  
Christoph Geiger ◽  
Matthias Ziegler
Keyword(s):  
Higher Education ◽  
Test Scores ◽  
Research Performance ◽  
Scientific Research ◽  
Research Education ◽  
Crystallized Intelligence ◽  
Research Competency ◽  
Social Scientific Research ◽  
Social Scientific ◽  
Education Courses

Abstract. Although the development of research competency is an important goal of higher education in social sciences, instruments to measure this outcome often depend on the students’ self-ratings. To provide empirical evidence for the utility of a newly developed instrument for the objective measurement of social-scientific research competency, two validation studies across two independent samples were conducted. Study 1 ( n = 675) provided evidence for unidimensionality, expected differences in test scores between differently advanced groups of students as well as incremental validities over and above self-perceived research self-efficacy. In Study 2 ( n = 82) it was demonstrated that the competency measured indeed is social-scientific and relations to facets of fluid and crystallized intelligence were analyzed. Overall, the results indicate that the test scores reflected a trainable, social-scientific, knowledge-related construct relevant to research performance. These are promising results for the application of the instrument in the evaluation of research education courses in higher education.

The Influence of the Established Links between University Courses on the Quality of Teaching

2018 ◽  
Vol 8 (2) ◽  
pp. 35-48
Author(s):  
Jiří Rybička ◽  
Petra Čačková
Keyword(s):  
Higher Education ◽  
Empirical Study ◽  
The Other ◽  
Coherent System ◽  
University Courses ◽  
Quality Of Teaching ◽  
Education Courses ◽  
Initial Knowledge

One of the tools to determine the recommended order of the courses to be taught is to set the prerequisites, that is, the conditions that have to be fulfilled before commencing the study of the course. The recommended sequence of courses is to follow logical links between their logical units, as the basic aim is to provide students with a coherent system according to the Comenius' principle of continuity. Declared continuity may, on the other hand, create organizational complications when passing through the study, as failure to complete one course may result in a whole sequence of forced deviations from the recommended curriculum and ultimately in the extension of the study period. This empirical study deals with the quantitative evaluation of the influence of the level of initial knowledge given by the previous study on the overall results in a certain follow-up course. In this evaluation, data were obtained that may slightly change the approach to determining prerequisites for higher education courses.

scholarly journals Multi-Class Assessment Based on Random Forests

2021 ◽  
Vol 11 (3) ◽  
pp. 92
Author(s):  
Mehdi Berriri ◽  
Sofiane Djema ◽  
Gaëtan Rey ◽  
Christel Dartigues-Pallez
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forests ◽  
Learning Algorithm ◽  
Teaching Staff ◽  
Machine Learning Algorithm ◽  
Process Data ◽  
Training Courses ◽  
Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

Creating Virtual Ensembles: Common Approaches from Research and Practice

2021 ◽  
Vol 107 (3) ◽  
pp. 38-46
Author(s):  
Christopher Cayari
Keyword(s):  
Music Education ◽  
Music Teachers ◽  
Music Educators ◽  
The Internet ◽  
Research And Practice ◽  
Music Production ◽  
Music Making ◽  
Teachers And Students ◽  
Online Music ◽  
Education Courses

A virtual ensemble is a digital musical product that uses multiple recordings edited together to form a musical ensemble. Creating virtual ensembles can be a way for music educators to engage students through online music-making. This article presents eight steps for creating virtual ensembles in music education courses and classrooms. The steps are (1) identifying objectives and desired outcomes, (2) selecting repertoire, (3) developing learning resources, (4) creating an anchor for synchronizing, (5) choosing a recording method, (6) setting up a collection platform, (7) editing in postproduction, and (8) distributing the product. As online music production becomes more prevalent, projects like virtual ensembles can provide creative and exciting experiences for music teachers and students, whether produced in the classroom or through remote means on the Internet.

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