Learning from text and video blogs: comprehension effects on secondary school students

Informational video blogs are a popular method of communication among students that may be fruitful educational tools, but their potential benefits and risks remain unclear. Streaming videos created by YouTubers are often consumed for entertainment, which may lead students to develop habits that hinder in-depth information processing. We aimed to test this hypothesis by comparing students’ perceived attention to task, metacognitive calibration of their level of comprehension, and comprehension outcomes between reading text blogs and watching video blogs. We also examined the influence of notetaking. 188 lower secondary students read two text blog entries and watched two video blog entries, and completed a series of tasks. Results showed no statistically significant effect of blog format and notetaking on students’ perceived on-task attention, metacognitive calibration, and comprehension of blog entries. Nevertheless, we found a triple interaction effect of format, notetaking, and students’ reading comprehension on blog entry comprehension. Only students low in reading comprehension benefited from notetaking and only when they read the text blog entries. These results indicate that video blogs can be as suitable for learning as text blogs and that notetaking can help struggling readers overcome their difficulties when learning from text blogs but not from video blogs.

A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text

Taxonomies provide a structured representation of semantic relations between lexical terms, acting as the backbone of many applications. The research proposed herein addresses the topic of taxonomy enrichment using an ”human-in-the-loop” semi-supervised approach. I will be investigating possible ways to extend and enrich a taxonomy using corpora of unstructured text data. The objective is to develop a methodological framework potentially applicable to any domain.

A modular framework for ontology learning from text in Portuguese

Research on ontology learning has been carried out in many knowledge areas, especially in Artificial Intelligence. Semi-automatic or automatic ontology learning can contribute to the field of knowledge representation. Many semi-automatic approaches to ontology learning from texts have been proposed. Most of these proposals use natural language processing techniques. This paper describes a computational framework construction for semi-automated ontology learning from texts in Portuguese. Axioms are not treated in this paper. The work described here originated from the Philipp Cimiano’s proposal along with text standardization mechanisms, natural language processing, identification of taxonomic relations and techniques for structuring ontologies. In this work, a case study on public security domain was also done, showing the benefits of the developed computational framework. The result of this case study is an ontology for this area.

Mapping and Drawing to Improve Students’ and Teachers’ Monitoring and Regulation of Students’ Learning from Text: Current Findings and Future Directions

For (facilitating) effective learning from texts, students and teachers need to accurately monitor students’ comprehension. Monitoring judgments are accurate when they correspond to students’ actual comprehension. Accurate monitoring enables accurate (self-)regulation of the learning process, i.e., making study decisions that are in line with monitoring judgments and/or students’ comprehension. Yet, (self-)monitoring accuracy is often poor as the information or cues used are not always diagnostic (i.e., predictive) for students’ actual comprehension. Having students engage in generative activities making diagnostic cues available improves monitoring and regulation accuracy. In this review, we focus on generative activities in which text is transformed into visual representations using mapping and drawing (i.e., making diagrams, concept maps, or drawings). This has been shown to improve monitoring and regulation accuracy and is suited for studying cue diagnosticity and cue utilization. First, we review and synthesize findings of studies regarding (1) students’ monitoring accuracy, regulation accuracy, learning, cue diagnosticity, and cue utilization; (2) teachers’ monitoring and regulation accuracy and cue utilization; and (3) how mapping and drawing affect using effort as a cue during monitoring and regulation, and how this affects monitoring and regulation accuracy. Then, we show how this research offers unique opportunities for future research on advancing measurements of cue diagnosticity and cue utilization and on how effort is used as a cue during monitoring and regulation. Improving measures of cue diagnosticity and cue utilization can provide us with more insight into how students and teachers monitor and regulate students’ learning, to help design effective interventions to foster these important skills.