## 1 Introduction
Although some recent advances have taken place--exploiting graph-based solutions [16; 17; 18], or deep learning neural networks (DNNs) [19; 20]--attempting to acquire more and more accurate predictions, or even robust ones in case that noisy inputs/labels have violated the ideal case of compact training data [21], such mechanisms introduce some important defeats:  
* Increased computational issues regarding the size of the provided datasets;
* Operation under transductive mode with inefficient complexity for most real-life cases rejecting at the same time the extraction of an inductive mechanism as a generic solution;
* Inability to facilitate interpretability of the exported decisions/predictions [22; 23]. The main scope of the present study is three-fold. At first, we implement a well-known semi-supervised regression algorithm that is based on multi-view learning, adopting several ML learners into its main kernel, tackling with the early prediction of undergraduate students' final exam grades in a one-year distance learning course. Each student is represented in terms of a plethora of features, which were collected from three different sources, thus producing three distinct sets of attributes: Demographics, academic achievements, and interaction within the course Learning Management System (LMS). Secondly, we investigate the effectiveness of the separate SSR variants that are produced compared with their corresponding supervised performance on the examined EDM task. In this sense, the proposed model may serve as an early alert tool with a view to providing appropriate interventions and support actions to low performers. Finally, we apply a well-established framework for acquiring trustworthy reasoning scores per included attribute/indicator into the original dataset. Hence, interpretable models are created, providing carefully computed explanations about the predicted grades ranking the importance of each indicator without any dimensionality reduction trick and avoiding overconsumption of computational resources under specific cases. To the best of our knowledge, this is the first completed study towards this direction [24], which hopefully will provide the basis for further research in the field of EDM, as it is stated in the relevant and conclusory Sections. The remainder of this paper is organized as follows. In the next section, we discuss the need for explainable artificial intelligence (XAI) solutions to the field of EDM, highlighting some of the most important approaches in interpreting decisions/predictions of various learning models and the assets of the selected interpretability framework. Section 3 presents a brief overview of relevant studies in the EDM field and some recently published works related to the SSR task. The research goal is set in Section 4, together with an analysis of the dataset used in the experimental procedure. The total pipeline for applying a well-known COREG algorithm (CO-training REGressors) [25] as an SSR wrapper along with several ML learners and some DNNs variants is provided in Section 5, also describing the two distinct explaining mechanisms that are based on the computation of Shapley values [26]. The experimental process and results are presented in Section 6. Finally, our conclusions are drawn in Section 7, which also mentions some promising improvements to this seminal work.