## 1 Introduction  
Educational data mining (EDM) has emerged in the past two decades as a highly-growing research field concerning the development and implementation of machine learning (ML) methods for analyzing datasets coming from various educational environments [1]. The key concept is to utilize these methods, extract meaningful knowledge about students' performance, and improve the learning process enriching the insights that the tutor may obtain on time. These methods are grouped into five main categories [2]: Prediction, clustering, relationship mining, discovery with models, and distillation of data for human judgment. The main research interest has been centered on predictive problems primarily concerned with three major questions [3]: (1) What outcome of students will be predicted? (2) Which ML methodology is the most effective for the specific problem? (3) How early can such a prediction be made?Most of the EDM research is mainly focused on implementing supervised methods utilizing only labeled datasets. To this end, a plethora of classification and regression techniques have successfully been applied for predicting various learning outcomes of students, such as dropout, attrition, failure, academic performance, and grades, to name a few. In addition, the main interest concentrates on building efficient predictive models at the end of a course using all available information about students [4]. However, it is of practical importance to provide both accurate and early-step predictions at minimum cost [5]. A review of recent studies and developments in the field of EDM reveals that there is an urgent demand for accurate identification of students at risk of failure as soon as possible during the academic year, since early intervention activities and strategies can be implemented. Preventing academic failure, enhancing student performance, and improving learning outcomes is of utmost importance for higher education institutions that intend to provide high-quality education [6]. Some new directions that have recently been formatted concern the recognition of errors during the composition or the writing of code assessment, usually based on self-attenuation mechanisms for providing high quality automated debugging solutions to undergraduate and post-graduate students, as well as the exportation of remarkable insights about the obstacles that are met by them during such tasks [7]. Apart from supervised methods, semi-supervised learning (SSL) has gained a lot of attention among scientists in the past few years for solving a wide range of problems in various domains [8]. SSL methods exploit a small pool of labeled examples together with a large pool of unlabeled ones for building robust and highly-efficient learning models. However, SSL has not adequately used in the educational domain as easily identified after a thorough literature review. Nevertheless, some notable studies have emerged recently dealing with semi-supervised classification (SSC) tasks, such as student performance prediction or student dropout, while semi-supervised regression (SSR) is uncharted territory. The primal difference between SSC and SSR is that the target attribute is categorical in the former case, while a pure numeric quantity has to be predicted in the latter case. A recent literature review of SSR depicts the most important works in this field [9], separating them into approaches with a common strategy to solve their task, while more related works have been demonstrated on behalf of SSC [10]. Multi-view learning has also attracted the interest of this research community, distilling information from separate views, original or transformed ones, while a search of more appropriate subspaces into the initial feature set always remains a crucial learning task for boosting the performance of SSL methods [11; 12]. Adopting ensemble learners has also been an active research territory concerning SSL [13], while some similar works have been demonstrated by our side [14; 15].