Doing assignments is a very important part of learning. Students' assignment submission time provides valuable information on study attitudes and habits which strongly correlate with academic performance. However, the number of assignments and their submission deadlines vary among university courses, making it hard to use assignment submission…

The aims of this study are investigating the attention ability and geometry skills of 60-72-month-old children according to the socio-economic status and determining whether the attention ability significantly predicts the geometry skill when the socio-economic status is controlled. The accessible population of the research in the relational…

It is widely known that the presence of multicollinearity in a dataset can have detrimental effects on determining which predictors are responsible for the variation in the response (e.g. Pedhazur, 1982). There also exist some indication that the presence of multicollinearity does not impact one's ability to accurately estimate/predict the value…

Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is…

Identifying struggling students in real-time provides a virtual learning environment with an opportunity to intervene meaningfully with supports aimed at improving student learning and engagement. In this paper, we present a detailed analysis of quit prediction modeling in students playing a learning game called Physics Playground. From the…

In the paper, we present the results of a case study conducted at Faculty of Administration, University of Ljubljana among 1st year undergraduate students. We investigated the correlations between students' activities in the e-classroom and grades at the final exam. The sample included 92 participants who took part at the final exam in the course…

Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…

With large student enrollment, MOOC instructors face the unique challenge in deciding when to intervene in forum discussions with their limited bandwidth. We study this problem of "instructor intervention." Using a large sample of forum data culled from 61 courses, we design a binary classifier to predict whether an instructor should…

This paper proposes a classification via clustering approach to predict the final marks in a university course on the basis of forum data. The objective is twofold: to determine if student participation in the course forum can be a good predictor of the final marks for the course and to examine whether the proposed classification via clustering…

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 paper focuses on predicting drop-outs and school failures when student data has been enriched with data derived from students social behaviour. These data describe social dependencies gathered from e-mail and discussion board conversations, among other sources. We describe an extraction of new features from both student data and behaviour…

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…

The variables of class attendance and the institution-wide Early Alert Grading System were employed to predict academic success at the end of the semester. Classroom attendance was found to be statistically and significantly related to final average and accounted for 14-16% of the variance in academic performance. Class attendance was found to…

The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program…