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Abstract: Choosing a course of study is the first challenge at the beginning of an academic career. Once the choice has been made, first-year students have to find their way in the new academic environment and meet the requirements of their field of study. At no other stage of study do so many students drop out. The main reasons for this include performance deficits and a lack of motivation. These, in turn, often stem from the fact that first-year students are surprised by the actual content of their studies. This can result in an overburdening and disappointing study situation. Many higher education institutions try to minimize this discrepancy between study expectations and study reality by offering prospective students online self-assessments (OSA) as an opportunity to intensively examine their desired field of study in advance. If higher education institutions offer such tools, evaluations should be implemented. Depending on the time point and the purpose of the evaluation, different evaluation approaches can be use. In this paper, we provide and critically reflect examples on how to design evaluations if – as in most cases – a direct link between the used OSA performance with administrative student data is not possible. The examples stem from the OSA at the Justus-Liebig-University Gießen (JLU). Firstly, building on data collected in the context of the annual freshmen survey at JLU, we report how many first-year students claimed to have used an OSA and how they rated it. Secondly, we present detailed user experiences and feedback reported by OSA users directly after using the tool. Thirdly, we illustrate an approach to collect data on user acceptance focused on the psychometric instruments used in OSA: study-related cognitive ability tests as well as questionnaires assessing personality – two examples for instruments typically used in the OSA context. We finally resume the conditions for a successful evaluation and derive recommendations for OSA evaluations. [ABSTRACT FROM AUTHOR]

We investigated by means of a simulation study how well methods for factor rotation can identify a two-facet simple structure. Samples were generated from orthogonal and oblique two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet). Samples drawn from orthogonal populations were submitted to factor analysis with subsequent Varimax, Equamax, Parsimax, Factor Parsimony, Tandem I, Tandem II, Infomax, and McCammon's minimum entropy rotation. Samples drawn from oblique populations were submitted to factor analysis with subsequent Geomin rotation and a Promax-based Tandem II rotation. As a benchmark, we investigated a target rotation of the sample loadings toward the corresponding faceted population loadings. The three conditions were sample size (n = 400, 1,000), number of factors (q = 4-12), and main loading size (l = 0.40, 0.50, 0.60). For less than six orthogonal factors Infomax and McCammon's minimum entropy rotation and for six and more factors Tandem II rotation yielded the highest congruence of sample loading matrices with faceted population loading matrices. For six and more oblique factors Geomin rotation and a Promax-based Tandem II rotation yielded the highest congruence with faceted population loadings. Analysis of data of 393 participants that performed a test for the Berlin Model of Intelligence Structure revealed that the faceted structure of this model could be identified by means of a Promax-based Tandem II rotation of task aggregates corresponding to the cross-products of the facets. Implications for the identification of faceted models by means of factor rotation are discussed.

In 2006, a (new) German standard for test reviewing was passed (Testkuratorium, 2006). There was already a European standard in place (European Federation of Psychologists' Associations, 2008). This article presents the German standard for test reviewing and explains how the German test review system was derived from demands in the German standard DIN 33430 for proficiency assessment procedures. Crucial decisions that were made prior to the construction of the standards are clarified, including why other (e.g., European/Dutch) existing standards were not simply translated. Furthermore, advances, difficulties, challenges, and perspectives regarding the test reviewing system in Germany are discussed. (Contains 1 table and 1 note.)

The authors examined whether feedback from student ratings of instruction not augmented with consultation helps college teachers to improve their student ratings on a long-term basis. The study reported was conducted in an institution where no previous teaching-effectiveness evaluations had taken place. At the end of each of four consecutive semesters, student ratings were assessed and teachers were provided with feedback. Data from 3122 questionnaires evaluating 12 teachers were analyzed using polynomial and piecewise random coefficient models. Results revealed that student ratings increased from the no-feedback baseline semester to the second semester and then gradually decreased from the second to the fourth semester, although feedback was provided after each semester. The findings suggest that student ratings not augmented with consultation are far less effective than typically assumed when considered from a long-term perspective.

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1744-6570.2010.01182.x Byline: JONAS W. B. LANG (1), MARTIN KERSTING (2), UTE R. HULSHEGER (3), JESSICA LANG (4) Abstract: The nested-factors model is a well-established structural model of cognitive abilities in cognitive ability research but has not yet been used to investigate the role of cognitive abilities in job performance. Core assumptions of the nested-factors model are that a broad general mental ability (GMA) exists besides narrower abilities and that this GMA differs from the narrower cognitive abilities in breadth but not in subordination. The authors of this article propose that a recently emerging statistical technique-relative importance analysis-corresponds to the assumptions of the nested-factors model. To empirically study the implications of using the nested-factors model, the authors applied relative importance analysis to a meta-analytic matrix linking measures of 7 narrower cognitive abilities from an established ability taxonomy (Thurstone's primary mental abilities), GMA, and job performance. Results revealed that GMA accounted for 10.9% to 28.6% of the total variance explained in job performance and that GMA was not consistently the most important predictor. The discussion focuses on potential theoretical, methodological, and practical implications of the nested-factors model for personnel psychology. Author Affiliation: (1)Department of Work and Social Psychology Maastricht University (2)Federal University of Applied Administrative Sciences (3)Department of Work and Social Psychology Maastricht University (4)Institute of Occupational Medicine RWTH Aachen University Article note: Correspondence and requests for reprints should be addressed to Jonas W. B. Lang, Department of Work and Social Psychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; jonas.lang@maastrichtuniversity.nl.

The Federal Constitutional Court decided in 2017 that the numerus clausus in the field of medicine is not compatible with the fundamental right to freedom of choice in education. Therefore, additional criteria must be used for the selection of candidates. Higher education research has the knowledge and tools to optimize admission procedures. This article presents empirical findings on predicting academic success and discusses online self-assessments (OSAs) as supplementary tools for academic counseling. OSAs can support self-selection and the selection of students. The article discusses the selection criteria for admission to studies and the role of online self-assessments (OSAs) in choosing a course of study. It emphasizes that eligibility criteria that can be assessed before the start of studies and can be measured with a realistic effort should be used as admission criteria. Cross-disciplinary eligibility criteria such as intelligence, personality, and interests are mentioned as important factors for academic success. The article also mentions that OSAs can be used as a tool to support the choice of studies by providing detailed information about potential courses of study and testing the suitability of prospective students. It emphasizes that high-quality OSAs can help strengthen self-selection and reduce the long-term dropout rate. Online self-assessments (OSAs) can help attract the right applicants for courses of study and increase interest in less popular courses of study. The specific features of a course of study and location can be presented through various media elements such as videos or images. OSAs are a resource-saving addition to admission procedures as they are scalable and do not incur additional costs. The use of OSAs can increase the basic quota and is suitable for both study orientation and academic counseling. There is empirical evidence for the positive effects of OSAs, such as an increased basic quota in certain master's programs. OSAs can be integrated into the overall process of counseling and support and should include both status and support diagnostics. The article describes the advantages of online self-assessments (OSAs) as a supplementary tool for academic counseling and admission procedures at universities. OSAs enable automated and individual diagnosis of the needs of prospective students and provide a systematic allocation of support measures. By linking OSAs with counseling services and courses, prospective students can be specifically supported. OSAs can also serve as preparation for admission tests and thus increase the fairness of the selection process. Empirical studies show that OSAs are well-received by prospective students. [Extracted from the article]