E-Learning Courses Evaluation on the Basis of Trainees' Feedback on Open Questions Text Analysis

Life-long learning is a necessity associated with the requirements of the fourth industrial revolution. Although distance online education played a major role in the evolution of the modern education system, this share grew dramatically because of the COVID-19 pandemic outbreak and the social distancing measures that were imposed. However, the quick and extensive adoption of online learning tools also highlighted the multidimensional weaknesses of online education and the needs that arise when considering such practices. To this end, the ease of collecting digital data, as well as the overall evolution of data analytics, enables researchers, and by extension educators, to systematically evaluate the pros and cons of such systems. For instance, advanced data mining methods can be used to find potential areas of concern or to confirm elements of excellence. In this work, we used text analysis methods on data that have emerged from participants' feedback in online lifelong learning programmes for professional development. We analysed 1890 Greek text-based answers of participants to open evaluation questions using standard text analysis processes. We finally produced 7-gram tokens from the words in the texts, from which we constructed meaningful sentences and characterized them as positive or negative. We introduced a new metric, called acceptance grade, to quantitatively evaluate them as far as their positive or negative content for the online courses is concerned. We finally based our evaluation on the top 10 sentences of each category (positive, negative). Validation of the results via two external experts and data triangulation showed an accuracy of 80%

ACT-P: A configurable theorem prover.

There has been a considerable amount of research into the provision of explicit representation of control regimes for resolution-based theorem provers. However, most of the existing systems are either not adequate or too inefficient to be of practical use. In this paper a theorem prover, ACT-P, which is adequate but retains satisfactory efficiency is presented. It does so by providing a number of user-changeable heuristics which are called at specific points during the search for a proof. The set of user-changeable heuristics was determined on the basis of a classification of the heuristics used by existing resolution-based theorem provers

Handling inheritance in a system integrating logic in objects.

The inheritance mechanism of SILO, a system integrating a many-sorted logic within an object-based framework, is presented. In order to be adequate for knowledge representation, it comprises two components, a hardwired and a user-definable. Due to use of typed (sorted) terms, a variety of specialisation types between logical formulas (axioms) are introduced and defined. Thus, the hardwired component is able to represent a variety of inheritance/specialisation relations between objects. The notion of a conflict is defined and conflict detection theorems are introduced. Also, consequence retraction is introduced and used alongside attribute/predicate overriding to resolve conflicts. The user-definable component consists of a number of user definable functions, called meta-functions, which are able to implement both global and local inheritance control. It is based on a partial reflection meta-level architecture