## 1 Introduction
The Onlabs assessment mechanism mainly revolves around two factors: the specific actions (such as equipment handling) carried out for the completion of the experiment, as well as the order in which those actions were made. As the various actions are, naturally, not of the same importance, different weights, concerning either the completion rate or the order of chosen actions, are assigned to each one of them. Figure 1: A snapshot of the virtual laboratory (Onlabs). The initialization of such weights has been carried out in close collaboration with subject experts. While these experts (instructors in laboratory-based biology) are central to identifying what is important and what is not, the numbers they give must be treated in the same fashion that knowledge engineers (as early as the 1980s) were treating knowledge harnessed during interviews, which were a central step of expert system development; namely, experts can identify what is important and what is not but, more often than not, they will be prone to taking a lot of things for granted and will be unable to explicitly express their knowledge [1,2]. As a result, weights suggested by experts, while definitely being helpful as anchors and indicative of importance, are only partially accurate; therefore, to improve a weight-based assessment mechanism, we must resort to more nuanced approaches involving, to a certain extent, machine learning based on specific examples provided by experts. To improve on the original weight suggested by the experts, we have utilized Genetic Algorithms (GAs), where we explore alternative weighing schemes based on the experts' original suggestions, and Artificial Neural Networks (ANN), where we attempt to learn an altogether new assessment mechanism which, however, is not too far off from the experts' suggestion, both in terms of the final result and in terms of the assessment of individual steps. Using a GA or an ANN depends on the particular experiment completion metric we want to model (more to follow), but both approaches depend on a training set of experiments which have been carried out by knowledgeable lab users (who could tell the difference between poor and good performance) and which have also been evaluated by subject experts. Additionally, this work presents an improvement attempt that also seeks to utilize domain expert knowledge that can be unequivocally communicated; specifically, the breaking down of an experiment into some (few, several at most) steps, which are conceptually distant to each other (for example, preparing a test specimen for microscoping and actually starting to use the knobs with one's eyes on the lenses, are two steps that no reasonable user would ever do in parallel). Such domain knowledge utilization thus imposes a hierarchy of importance and conceptual similarity of the various steps of an experiment which, in turn, can be used to guide either the GA or the ANN mechanism towards a more effective search (such a \"hierarchical\" approach can be directly compared to the original \"flat\" one). The rest of this paper is structured in six subsequent sections. First, in Section 2, we briefly review how the problem of assessing progress in virtual lab practice can be seen as being a close relative to the more well-established scoring schemes in adventure games. Then, in Section 3, we show how expert guidance can help in developing a proof-of-concept scoring mechanism and proceed to propose that such expert guidance may be more valuable when used in delivering a final assessment as opposed to a step-wise assessment of individual steps. Thus, in Section 4, we utilize some fundamental machine learning techniques viewing the design of an assessment mechanism as an instance of a knowledge acquisition problem. Then, in Section 5, we review the experimental results, while in Section 6, we proceed to adapt our basic mechanism by incorporating some domain knowledge, aiming to improve its explainability aspects.