Educational data mining constitutes a recent research field which gained popularity over the last decade because of its ability to monitor students' academic performance and predict future progression. Numerous machine learning techniques and especially supervised learning algorithms have been applied to develop accurate models to predict…

With technological advances, distance education has been frequently discussed in recent years. The learning environments used in this course usually generates a great deal of data because of the large number of students and the various tasks involving their interaction. In order to facilitate the analysis of the data, the authors researched to…

The number of e-learning platforms and blended learning environments is continuously increasing and has sparked a lot of research around improvements of educational processes. Here, the ability to accurately predict student performance plays a vital role. Previous studies commonly focused on the construction of predictors tailored to a formal…

With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies…

An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student…

Affecting university rankings, school reputation, and financial well-being, student retention has become one of the most important measures of success for higher education institutions. From the institutional perspective, improving student retention starts with a thorough understanding of the causes behind the attrition. Such an understanding is…

The use of data mining for the analysis of data collected in natural settings is increasingly recognized as a legitimate mode of enquiry. This rule-inductive paradigm is an effective means of discovering relationships within large datasets--especially in research that has limited experimental design--and for the subsequent formulation of…

Learning environments aim to deliver efficacious instruction, but rarely take into consideration the motivational factors involved in the learning process. However, motivational aspects like engagement play an important role in effective learning-engaged learners gain more. E-Learning systems could be improved by tracking students' disengagement…

Categorizes different theoretical justifications of data fusion into two approaches, examines their implications, analyzes some unsuccessful data fusion experiments, and proposes two conditions for effective data fusion. Results indicate that the efficacy and inter-scheme dissimilarity are good predictors for effectiveness of data fusion.…

This study, which examined the demographic and educational characteristics of students who leave school before completing the twelfth grade, aimed to identify descriptive and predictive data on the potential school dropout in the Pasco School District of Washington State. The project was conducted as an initial step toward developing a data…

The sample for the 1975 field test of the Barber Scales of Self-Regard for Preschool Children came from Episcopal schools distributed widely by geographical area. The instrumentation included, besides the self-regard scales, the Self-Concept and Motivation Inventory and the Minnesota Personality Profile II, plus a demographic questionnaire. The…

This is one of nine volumes describing the results of extensive research carried out by the International Association for the Evaluation of Educational Achievement (IEA) over a seven-year period. The overall aim of this extensive empirical study is to relate student competence to instructional, economic, and social factors which account for…