Educational institutions are struggling to keep up with the accelerated technological advancements; hence, sustainable and supportive tools have become essential to reshape traditional models into intelligent learning systems. This paper introduces Lib2Life, a digital library that uses advanced Natural Language Processing techniques to facilitate the digital transformation of historical documents provided by Central University Libraries in Romania. The platform enables Central University Libraries in Romania to preserve the cultural heritage of historically valuable documents, facilitating open-source access to old, printed materials such as books, manuscripts, newspapers, or literary magazines no longer protected by copyright. Lib2Life offers comprehensive functionalities, allowing librarians to benefit from automated text processing and indexing workflows that facilitate digitization, ensuring a consistent representation of original documents. For readers, the platform presents a user-friendly interface with semantic search capabilities and a recommendation engine. The system employs an ontology to organize and manage documents in a unified and structured way, contributing to the evolution of intelligent education technologies. The innovative contributions of Lib2Lifeinclude identifying new solutions for cultural heritage preservation, promoting patrimony through modern methodologies, increasing access to documentary resources, enhancing library services, and fostering the transfer of knowledge and technology to society.

The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the ReaderBench framework, grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns. Students' behaviors and interactions are compared before and during COVID-19 using two consecutive yearly instances of an undergraduate course in Algorithm Design, conducted in Romanian using Moodle. The COVID-19 outbreak generated an off-balance, a drastic increase in participation, followed by a decrease towards the end of the semester, compared to the academic year 2018-2019 when lower fluctuations in participation were observed. The prediction model for the 2018-2019 academic year obtained an R2 of 0.27, while the model for the second year obtained a better R2 of 0.34, a value arguably attributable to an increased volume of online activity. Moreover, the best model from the first academic year is partially generalizable to the second year, but explains a considerably lower variance (R2 = 0.13). In addition to the quantitative analysis, a qualitative analysis of changes in student behaviors using comparative sociograms further supported conclusions that there were drastic changes in student behaviors observed as a function of the COVID-19 pandemic.

Dialogism is a philosophical theory centered on the idea that life involves a dialogue among multiple voices in a continuous exchange and interaction. Considering human language, different ideas or points of view take the form of voices, which spread throughout any discourse and influence it. From a computational point of view, voices can be operationlized as semantic chains that contain related words. This study introduces and evaluates a novel method of identifying semantic chains using BERT, a state-of-the-art language model for computational linguistics. The resulting model generalizes to multiple relations including repetitions, semantically related concepts from WordNet (i.e., synonyms, hypernyms, hyponyms, and siblings), as well as pronominal resolutions. By combining the attention scores between words, word pairs are merged into connected components that denote emerging voices from the discourse. The introduced visualization argues for a more dense capturing of inner semantic links between words and even compound words in contrast to classical methods of building lexical chains.

Social media consists of interactive applications which bring together people from different geographical regions through technology. Online communities have become increasingly popular due to their capabilities to virtually connect people with similar interests. Based on their activity, a social rank is computed to measure how users are perceived within the community. The aim of this paper is to perform an in-depth analysis of a debate community from Reddit. Our method provides tailored services capable to analyze user behavior based on regularity measures, model the interactions between participants, and predict a social rank for users based on their participation. The ReaderBench framework has been used to generate multiple indices, including those for textual complexity, reflective of writing style specificities. Various regression models were trained and evaluated in order to predict users' rankings, which are reflected in the number of votes they receive from their peers. The results show that the user ranks are predicted with a precision of 15 votes by using MLP neural networks.

The use of technology as a facilitator in learning environments has become increasingly prevalent with the global pandemic caused by COVID-19. As such, computer-supported collaborative learning (CSCL) gains a wider adoption in contrast to traditional learning methods. At the same time, the need for automated tools capable of assessing and stimulating collaboration between participants has become more stringent, as human monitoring of the increasing volume of conversations becomes overwhelming. This paper introduces a method grounded in dialogism for evaluating students’ involvement in chat conversations based on semantic chains computed using language models. These semantic chains reflect emergent voices from dialogism that span and interact throughout the conversation. Our integrated method uses contextual information captured by BERT transformer models to identify links in a chain that connects semantically related concepts from a voice uttered by one or more participants. Two types of visualizations were generated to depict the longitudinal propagation and the transversal inter-animation of voices within the conversation. In addition, a list of handcrafted features derived from the constructed chains and computed for each participant is introduced. Several machine learning algorithms were tested using these features to evaluate the extent to which semantic chains are predictive of student involvement in chat conversations.

Reading comprehension requires readers to connect ideas within and across texts to produce a coherent mental representation. One important factor in that complex process regards the cohesion of the document(s). Here, we tackle the challenge of providing researchers and practitioners with a tool to visualize text cohesion both within (intra) and between (inter) texts. This tool, Multi-document Cohesion Network Analysis (MD-CNA), expands the structure of a CNA graph with lexical overlap links of multiple types, together with coreference links to highlight dependencies between text fragments of different granularities. We introduce two visualizations of the CNA graph that support the visual exploration of intratextual and intertextual links. First, a hierarchical view displays a tree-structure of discourse as a visual illustration of CNA links within a document. Second, a grid view available at paragraph or sentence levels displays links both within and between documents, thus ensuring ease of visualization for links spanning across multiple documents. Two use cases are provided to evaluate key functionalities and insights for each type of visualization.

Online collaborative learning environments open new research opportunities, for example, the analysis of learning outcomes, the identification of learning patterns, the prediction of students' behaviors, and the modeling and visualization of social relations and trends among students. Moodle is an online educational platform which supports both students and teachers, and can be effectively employed to encourage collaborative learning. Moodle is often used to make inquiries on student homework, exams, to request clarifications, and to make announcements. Our goal is to predict student success based on Cohesion Network Analysis (CNA) and to identify interaction patterns between students (n = 71 who had a sufficient level of participation on the forum) and 4 tutors together with 19 teaching assistants in a Romanian Moodle course. CNA visualizations consider a hierarchical clustering that classifies members into central, active, and peripheral groups. Weekly snapshots are generated to better understand students' evolution throughout the course, while correlating their activities with specific course events (e.g., homework deadlines, tests, holidays, exam, etc.). Several regression models were trained based on the generated CNA indices and the best model achieves a mean average error below.5 points when predicting partial course grades, prior to the final exam, on a 6-point scale.

Leaflets are one of the most straightforward methods for obtaining details regarding how to administer drugs, their composition, warnings, or precautions in their usage. However, they are many times cluttered with information, making them hard to understand by people. At the same time, the provided indications are frequently either too specific or too broad, thus making their understanding even more difficult. This paper presents the architecture and the prototype of an intelligent platform for drug administration with the following key functionalities: adding specific medications to user's profile, searching for potential contraindications or side effects, and defining alerts of drug administration in the provided calendar, based on the physician's prescription. The platform aggregates leaflets from two sources (Biofarm and HelpNet) using Natural Language Processing (NLP) techniques tailored for Romanian language and provides full-text search via Elasticsearch. A friendly mobile application, available for IOS and Android devices, as well as in web browsers, was developed to improve users' healthcare and ensure self-education.