Among other things, learning to write entails learning how to use complex sentences effectively in discourse. Some research has therefore focused on relating measures of syntactic complexity to text quality. Apart from the fact that the existing research on this topic appears inconclusive, most of it has been conducted in English L1 contexts. This is potentially problematic, since relevant syntactic indices may not be the same across languages. The current study is the first to explore which syntactic features predict text quality in Dutch secondary school students’ argumentative writing. In order to do so, the quality of 125 argumentative essays written by students was rated and the syntactic features of the texts were analyzed. A multilevel regression analysis was then used to investigate which features contribute to text quality. The resulting model (explaining 14.5% of the variance in text quality) shows that the relative number of finite clauses and the ratio between the number of relative clauses and the number of finite clauses positively predict text quality. Discrepancies between our findings and those of previous studies indicate that the relations between syntactic features and text quality may vary based on factors such as language and genre. Additional (cross-linguistic) research is needed to gain a more complete understanding of the relationships between syntactic constructions and text quality and the potential moderating role of language and genre.
The present study investigated whether text structure inference skill (i.e., the ability to infer overall text structure) has unique predictive value for expository text comprehension on top of the variance accounted for by sentence reading fluency, linguistic knowledge and metacognitive knowledge. Furthermore, it was examined whether the unique predictive value of text structure inference skill differs between monolingual and bilingual Dutch students or students who vary in reading proficiency, reading fluency or linguistic knowledge levels. One hundred fifty-one eighth graders took tests that tapped into their expository text comprehension, sentence reading fluency, linguistic knowledge, metacognitive knowledge, and text structure inference skill. Multilevel regression analyses revealed that text structure inference skill has no unique predictive value for eighth graders’ expository text comprehension controlling for reading fluency, linguistic knowledge and metacognitive knowledge. However, text structure inference skill has unique predictive value for expository text comprehension in models that do not include both knowledge of connectives and metacognitive knowledge as control variables, stressing the importance of these two cognitions for text structure inference skill. Moreover, the predictive value of text structure inference skill does not depend on readers’ language backgrounds or on their reading proficiency, reading fluency or vocabulary knowledge levels. We conclude our paper with the limitations of our study as well as the research and practical implications.
Research into automatic text simplification aims to promote access to information for all members of society. To facilitate generalizability, simplification research often abstracts away from specific use cases, and targets a prototypical reader and an underspecified content creator. In this paper, we consider a real-world use case – simplification technology for use in Dutch municipalities – and identify the needs of the content creators and the target audiences in this scenario. The stakeholders envision a system that (a) assists the human writer without taking over the task; (b) provides diverse outputs, tailored for specific target audiences; and (c) explains the suggestions that it outputs. These requirements call for technology that is characterized by modularity, explainability, and variability. We argue that these are important research directions that require further exploration
MULTIFILE
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
The textile and clothing sector belongs to the world’s biggest economic activities. Producing textiles is highly energy-, water- and chemical-intensive and consequently the textile industry has a strong impact on environment and is regarded as the second greatest polluter of clean water. The European textile industry has taken significant steps taken in developing sustainable manufacturing processes and materials for example in water treatment and the development of biobased and recycled fibres. However, the large amount of harmful and toxic chemicals necessary, especially the synthetic colourants, i.e. the pigments and dyes used to colour the textile fibres and fabrics remains a serious concern. The limited range of alternative natural colourants that is available often fail the desired intensity and light stability and also are not provided at the affordable cost . The industrial partners and the branch organisations Modint and Contactgroep Textiel are actively searching for sustainable alternatives and have approached Avans to assist in the development of the colourants which led to the project Beauti-Fully Biobased Fibres project proposal. The objective of the Beauti-Fully Biobased Fibres project is to develop sustainable, renewable colourants with improved light fastness and colour intensity for colouration of (biobased) man-made textile fibres Avans University of Applied Science, Zuyd University of Applied Sciences, Wageningen University & Research, Maastricht University and representatives from the textile industry will actively collaborate in the project. Specific approaches have been identified which build on knowledge developed by the knowledge partners in earlier projects. These will now be used for designing sustainable, renewable colourants with the improved quality aspects of light fastness and intensity as required in the textile industry. The selected approaches include refining natural extracts, encapsulation and novel chemical modification of nano-particle surfaces with chromophores.
The textile and clothing sector belongs to the world’s biggest economic activities. Producing textiles is highly energy-, water- and chemical-intensive and consequently the textile industry has a strong impact on environment and is regarded as the second greatest polluter of clean water. The European textile industry has taken significant steps taken in developing sustainable manufacturing processes and materials for example in water treatment and the development of biobased and recycled fibres. However, the large amount of harmful and toxic chemicals necessary, especially the synthetic colourants, i.e. the pigments and dyes used to colour the textile fibres and fabrics remains a serious concern. The limited range of alternative natural colourants that is available often fail the desired intensity and light stability and also are not provided at the affordable cost . The industrial partners and the branch organisations Modint and Contactgroep Textiel are actively searching for sustainable alternatives and have approached Avans to assist in the development of the colourants which led to the project Beauti-Fully Biobased Fibres project proposal. The objective of the Beauti-Fully Biobased Fibres project is to develop sustainable, renewable colourants with improved light fastness and colour intensity for colouration of (biobased) man-made textile fibres Avans University of Applied Science, Zuyd University of Applied Sciences, Wageningen University & Research, Maastricht University and representatives from the textile industry will actively collaborate in the project. Specific approaches have been identified which build on knowledge developed by the knowledge partners in earlier projects. These will now be used for designing sustainable, renewable colourants with the improved quality aspects of light fastness and intensity as required in the textile industry. The selected approaches include refining natural extracts, encapsulation and novel chemical modification of nano-particle surfaces with chromophores.
