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.
BackgroundThere has been an increasing interest in negative or ‘undermining’ motivations for reading. In this study, we aimed to strengthen knowledge on the validity of the distinction between affirming and undermining motivations. First, we examined whether the structure of a questionnaire based on this distinction could be confirmed. Second, we examined the predictive value of undermining motivations for reading comprehension. Third, we studied moderator effects of gender and age.MethodsWe administered a reading motivation questionnaire and a reading comprehension test to 324 low-achieving adolescents. The questionnaire included items on affirming and undermining motivations for school and leisure time reading: intrinsic motivation and avoidance, self-efficacy and perceived difficulty.ResultsConfirmatory factor analyses supported the assumed structure of the questionnaire. Undermining motivations, particularly perceived difficulty, explained unique variance in reading achievement. Gender and age did not moderate effects of motivational variables.ConclusionsEducators need to be aware of the role of undermining motivations. Future research should examine if interventions can lead to the reduction of such motivations.
Gamma-band neuronal synchronization during sentence-level language comprehension has previously been linked with semantic unification. Here, we attempt to further narrow down the functional significance of gamma during language comprehension, by distinguishing between two aspects of semantic unification: successful integration of word meaning into the sentence context, and prediction of upcoming words. We computed eventrelated potentials (ERPs) and frequency band-specific electroencephalographic (EEG) power changes while participants read sentences that contained a critical word (CW) that was (1) both semantically congruent and predictable (high cloze, HC), (2) semantically congruent but unpredictable (low cloze, LC), or (3) semantically incongruent (and therefore also unpredictable; semantic violation, SV). The ERP analysis showed the expected parametric N400 modulation (HC < LC < SV). The time-frequency analysis showed qualitatively different results. In the gamma-frequency range, we observed a power increase in response to the CW in the HC condition, but not in the LC and the SV conditions. Additionally, in the theta frequency range we observed a power increase in the SV condition only. Our data provide evidence that gamma power increases are related to the predictability of an upcoming word based on the preceding sentence context, rather than to the integration of the incoming word's semantics into the preceding context. Further, our theta band data are compatible with the notion that theta band synchronization in sentence comprehension might be related to the detection of an error in the language input.
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"Speak the Future" presents a novel test case at the intersection of scientific innovation and public engagement. Leveraging the power of real-time AI image generation, the project empowers festival participants to verbally describe their visions for a sustainable and regenerative future. These descriptions are instantly transformed into captivating imagery using SDXL Turbo, fostering collective engagement and tangible visualisation of abstract sustainability concepts. This unique interplay of speech recognition, AI, and projection technology breaks new ground in public engagement methods. The project offers valuable insights into public perceptions and aspirations for sustainability, as well as understanding the effectiveness of AI-powered visualisation and regenerative applications of AI. Ultimately, this will serve as a springboard for PhD research that will aim to understand How AI can serve as a vehicle for crafting regenerative futures? By employing real-time AI image generation, the project directly tests its effectiveness in fostering public engagement with sustainable futures. Analysing participant interaction and feedback sheds light on how AI-powered visualisation tools can enhance comprehension and engagement. Furthermore, the project fosters public understanding and appreciation of research. The interactive and accessible nature of "Speak the Future" demystifies the research process, showcasing its relevance and impact on everyday life. Moreover, by directly involving the public in co-creating visual representations of their aspirations, the project builds an emotional connection and sense of ownership, potentially leading to continued engagement and action beyond the festival setting. "Speak the Future" promises to be a groundbreaking initiative, bridging the gap between scientific innovation and public engagement in sustainability discourse. By harnessing the power of AI for collective visualisation, the project not only gathers valuable data for researchers but also empowers the public to envision and work towards a brighter, more sustainable future.
