Psychometrics in Practice at RCEC

A broad range of topics is dealt with in this volume: from combining the psychometric generalizability and item response theories to the ideas for an integrated formative use of data-driven decision making, assessment for learning and diagnostic testing. A number of chapters pay attention to computerized (adaptive) and classification testing. Other chapters treat the quality of testing in a general sense, but for topics like maintaining standards or the testing of writing ability, the quality of testing is dealt with more specifically.\ud All authors are connected to RCEC as researchers. They present one of their current research topics and provide some insight into the focus of RCEC. The selection of the topics and the editing intends that the book should be of special interest to educational researchers, psychometricians and practitioners in educational assessment

The Application Potential of Data Mining in Higher Education Management: A Case Study Based on German Universities

German universities are facing an intense, competitive environment caused by globalization, digitalization, and public sector reforms. The latter also gave the universities more decision-making autonomy, which goes hand in hand with more responsibilities, but also with the possibility of individualizing their strategy. This thesis examines how German universities can use Data Mining techniques to extract useful information from their available data resources to address these current challenges by supporting management decisions. The use of Data Mining methods in education is called Educational Data Mining. Research in this area has so far focused mainly on supporting students and lecturers. This thesis focuses on researching the benefits of Educational Data Mining for university management, which has been mentioned several times in various Educational Data Mining studies but has not been studied in detail so far. After discussing the most important challenges faced by German universities, their current tasks and objectives were examined. A framework model was then developed that illustrates how the results of two specific Data Mining projects can help universities tackle the challenges and accomplish their tasks. The selected Data Mining projects are dropout analysis and enrollment prediction because the student and applicant data are available to all the German universities. The proposed framework model was verified with two case studies in which the specified analyses were carried out at a German university of applied sciences. To build well-performing models, several Data Mining methods were used and compared. Subsequently, the results were discussed with representatives from the case university, and suggestions were made how the information generated could be included in the decisions of the university administration. It has been shown that German universities can use their data resources to support their management activities. An overview of this support was presented in the form of a framework model that is not only a first attempt to close the existing research gap in the field of EDM but should also mo-tivate university decision-makers to use their existing data resources. Therefore, the presented thesis can stimulate further research that combines the results of EDM projects with managerial decisions to increase the efficiency of educational institutions. In addition, university administrators can be inspired to use all available resources to ensure their long-term success

Applying of Data Mining and Statistical Techniques to Analyze the Impact of Socioeconomic Background on University Admission - A Case Study Using the Iranian Educational Data

The goal of this thesis was to conduct a focused and in-depth comprehensive study of the impact of socioeconomic status of the Iranian Wide Entrance Examination applicants’ family on the educational achievement of their children. To reach this goal we used various statistical methods and data mining techniques. The data over five years made it possible to construct classification and forecasting models for each year, separately. To the best of our knowledge, when dealing with the Iranian educational data, there is no comprehensive study that takes into account dynamic aspects

TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)

This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning

TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)

This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning

Predicting student performance using data mining and learning analysis technique in Libyan Higher Education

The Technology has an increasing impact on all areas of life, including the education sector, and requires developing countries to emulate developed countries and integrate technology into their education systems. Recently schools in Libya are facing an issue trying to figure out why students perform poorly in certain subjects and how can they know how they will perform next in the future in coming semesters in perspective subject. There are several methods proposed to predict the student’s performance, using data mining techniques. In this paper, there are plans to create Data Mining Techniques in Education (i.e., DME) prediction model clustering, classification and association rule mining in many universities and schools in order to provide students and teachers with the most advanced platform. Although relatively late, the Libyan government finally responded to this challenge by investing heavily in rebuilding the education system and launching a national plan to presented method in terms of predicting students’ performance based on their grades in Math and English. The results are divided in to three main sections clustering analysis using k-mean algorithm, classification analysis was done using two rounds first using Gain Ratio Evaluations to find out the top attributes that used by J84 algorithm in second round of classification, and rule association analysis using A priori algorithm. Rule association analysis is applied for the clusters generate by clustering analysis to generate the rules associated with each cluster. For each section, a list of inputs is presented with the scale used for the values followed by the results of the algorithm and explanation for the finding