Agnes Malatinszky’s scientific contributions

The increased reliance on online education and educational technologies more generally has laid bare the need to more deeply consider how researchers, designers, and educators can improve the quality of technology-mediated learning. To address this need, more than two dozen experts from a variety of fields came together to discuss the challenges that educational technology must address in the immediate future. These experts were tasked with identifying barriers to and potential solutions for delivering high-quality and equitable online and remote education. This article examines the themes and topics that emerged from these discussions and proposes a Collaborative Framework for Accelerating Online Education. This framework highlights the need for rapid experimentation within larger design cycles as well as the coordination and cooperation of multiple stakeholders across all phases of research and development. The themes, topics, and framework that emerged from this work serve as a call to action for innovative approaches to developing and studying online education.

This paper introduces the CommonLit Ease of Readability (CLEAR) corpus, which provides unique readability scores for ~ 5000 text excerpts along with information about the excerpt’s year of publishing, genre, and other metadata. The CLEAR corpus will provide researchers interested in discourse processing and reading with a resource from which to develop and test readability metrics and to model text readability. The CLEAR corpus includes a number of improvements in comparison to previous readability corpora including size, breadth of the excerpts available, which cover over 250 years of writing in two different genres, and unique readability criterion provided for each text based on teachers’ ratings of text difficulty for student readers. This paper discusses the development of the corpus and presents reliability metrics for the human ratings of readability.

In this paper, we introduce the Commonlit Ease of Readability (CLEAR) corpus. The corpus provides researchers within the educational data mining community with a resource from which to develop and test readability metrics and to model text readability. The CLEAR corpus improves on previous readability corpora include size (N = ~5,000 reading excerpts), the breadth of the excerpts available, which cover over 250 years of writing in two different genres, and the readability criterion used (teachers' ratings of text difficulty for their students). This paper discusses the development of the corpus and presents reliability metrics as well as initial analyses of readability.