Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its…

Text comprehension is an essential skill in today's information-rich world, and self-explanation practice helps students improve their understanding of complex texts. This study was centered on leveraging open-source Large Language Models (LLMs), specifically FLAN-T5, to automatically assess the comprehension strategies employed by readers while…

Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning…

Numerous approaches have been introduced to automate the process of text summarization, but only few can be easily adapted to multiple languages. This paper introduces a multilingual text processing pipeline integrated in the open-source "ReaderBench" framework, which can be retrofit to cover more than 50 languages. While considering the…

Transnational youth use digital media to affiliate with diverse cultural and linguistic practices, as demonstrated through the use of multiple languages and hybrid linguistic codes, media genres and multimodal expressions in the youths' online communication and writing (Black, 2009; Domingo, 2014; Kim, 2016). This study introduces a learning…