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Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from the articles of the International Conference of Learning Analytics & Knowledge (LAK) in its first decade, this paper proposes a novel method grounded in Cohesion Network Analysis (CNA) to analyze subcommunities of authors based on the semantic similarities between authors and papers, and estimate their global impact. Paper abstracts are represented as embeddings using a fine-tuned SciBERT language model, alongside a custom trained LSA model. The extrapolation between the local LAK community to a worldwide importance was also underlined by the comparison between the rankings obtained from our method and statistics from ResearchGate. The accuracies for binary classifications in terms of high/low impact predictions were around 70% for authors, and around 80% for articles. Our method can guide researchers by providing valuable information on the interactions between the members of a knowledge community and by highlighting central local authors who may potentially have a high global impact.

Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of comprehension (AMoC) simulates the construction of readers' mental representations of text by building syntactic and semantic relations between words, coupled with inferences of related concepts that rely on various automated semantic models. This paper introduces the second version of AMoC that builds upon the initial model with a revised processing pipeline in Python leveraging state-of-the-art NLP models, additional heuristics for improved representations, as well as a new radiant graph visualization of the comprehension model. [This paper was published in: "AIED 2021," edited by I. Roll et al., Springer Nature Switzerland AG, 2021, pp. 119-123.]

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 operationalized 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. [This paper was published in: "AIED 2021," edited by I. Roll et al., Springer Nature Switzerland AG, 2021, pp. 296-301.]

Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of comprehension (AMoC) simulates the construction of readers' mental representations of text by building syntactic and semantic relations between words, coupled with inferences of related concepts that rely on various automated semantic models. This paper introduces the second version of AMoC that builds upon the initial model with a revised processing pipeline in Python leveraging state-of-the-art NLP models, additional heuristics for improved representations, as well as a new radiant graph visualization of the comprehension model.

Reading comprehension is essential for both knowledge acquisition and memory reinforcement. Automated modeling of the comprehension process provides insights into the efficacy of specific texts as learning tools. This paper introduces an improved version of the Automated Model of Comprehension, version 3.0 (AMoC v3.0). AMoC v3.0 is based on two theoretical models of the comprehension process, namely the Construction-Integration and the Landscape models. In addition to the lessons learned from the previous versions, AMoC v3.0 uses Transformer-based contextualized embeddings to build and update the concept graph as a simulation of reading. Besides taking into account generative language models and presenting a visual walkthrough of how the model works, AMoC v3.0 surpasses the previous version in terms of the Spearman correlations between our activation scores and the values reported in the original Landscape Model for the presented use case. Moreover, features derived from AMoC significantly differentiate between high-low cohesion texts, thus arguing for the model's capabilities to simulate different reading conditions. [This paper was published in: "AIED 2023: Artificial Intelligence in Education," edited by N. Wang et al., Springer, Switzerland, 2023, pp. 229-241, 2023.]

The process of identifying relevant scientific papers and authors is becoming more and more tedious both to neophytes, as well as to experts searching for state-of-the-art approaches, due to the high-speed development of recent emerging domains and the exponential increase of the available publications. This paper presents an overarching architecture grounded in Cohesion Network Analysis (CNA) and provides a detailed showcase on the papers published at the Learning Analytics & Knowledge (LAK) conference between 2011 and 2020. We applied our method to LAK because it is the most representative sub-community from the Learning Analytics domain. On the other hand, CNA Sociograms might be applied to journals, academic departments, business groups, etc., having the potential to aid in understanding the past and current foci of a community and its directions. We propose an end to end method, from crawling the ACM Digital Library to insights presentation, using our own Visual 2-Mode CNA Graph and visualizations in Kibana, customizable and interactive, that facilitates individuals' interactions with a new domain. Our method brings valuable insights about the semantic relatedness between authors and other types of associations presented as links within the resulting 2-mode cohesion graph. Moreover, we introduce multiple statistics about top ranked authors, institutions, as well as the most central domain articles. In contrast to previous experiments, we introduce a comprehensive use case, an enhanced processing pipeline with new heuristics, as well as new CNA sociogram visualizations. This semantic-based approach facilitates inferring key authors' and publications' interrelations and trending topics over time. [ABSTRACT FROM AUTHOR]