## 2 Interpretability in Machine Learning  
Consider the problem of predicting the final exam grade of students enrolled in a distance learning course using ML. In this case, a supervised algorithm is trained over a set of labeled data (the target attribute values are known), and an ML model is produced (supervised learning), which is subsequently deployed for predicting the grade of a previously unknown student for given values of the input attributes (features of students). The predictive model does not know why the student received the specific grade, while, at the same time, it fails to grasp the difference between anticipated grades and actual performance. Decision-makers are often hesitant to trust the results of these models, since their internal functions are primarily hidden in black-boxes [27]. This is quite reasonable, since people outside of the ML field neither can understand the manner that outputs are exported, nor are confident on just consuming some pure decisions without accompanying them with some consistent proofs. There is also a well-known trade-off regarding the predictive ability and the interpretability of ML algorithms, which unfortunately deters the co-existence of both these properties to be highly qualified under the same ML algorithm, in general. Since predictive models play a decisive role in the decision-making process in higher education institutions, the ability to comprehend these models seems to be indispensable. Thus, the interpretability of provided solutions usually needs to be filtered through XAI tools [28; 29]. Model interpretability is the process of understanding the predictions of an ML model. In fact, it is the key point to build both accurate and reliable learning models. In traditional ML problems, the objective is to minimize the predictive error, while interpretability is focused on extracting more valuable information from the model [30]. Commonly, it aims to address questions, such as (Figure 1):  
* What each attribute represents? * Why was a specific prediction was made by the model? * Which are the key factors of a specified prediction? * Why a specific student was assigned a failing grade? * Can we describe what the model has learned? * How confident are we for the decisions of the model?Although several published works have appeared in the literature of XAI recently, the majority of them make assumptions that are not actually consistent with the specifications of an educational task. For example, dimensionality reduction or feature transformations (e.g., semantic embeddings) may lead to incorrect conclusions or reasoning factors that ignore some of the underlying relationships that may be crucial for the real-life problem [31]. Furthermore, DNNs and their variants that operate by manipulating raw-data directly have highly attracted the interest of the XAI community, leading to solutions that are not applicable to our numerical source data. However, this fact does not exclude DNNs from being used as accurate black-boxes to such kind of problems, adopting mainly some model-agnostic approaches [32]. A representative work was done by [PERSON] et al. [22] exploiting extreme learning machines (ELM) trained on sampled subsets of the initial training set for increasing the output variance of the learning model, and later, explaining the information gained thought this strategy via proper confidence intervals for specific confidence levels. Both artificial and real-life datasets were evaluated, performing robust behavior without inducing much computational effort. Besides DNNs, conventional ML algorithms need to overcome the long-standing obstacle of explainable predictions. One of the most popular libraries is LIME (local interpretable model-agnostic explanations) [33], which offers explanations based on local assumptions regarding the contribution of the examined learning model.