Abstract: Research in higher education has revealed a significant connection between executive functions (EFs) and study success. Previous investigations have typically assessed EFs using either neuropsychological tasks, which provide direct and objective measures of core EFs components such as inhibition, working memory, and cognitive flexibility, or self-report questionnaires, which offer indirect and subjective assessments. However, studies rarely utilize both assessment methods simultaneously despite their potential to offer complementary insights into EFs. This study aims to evaluate the predictive capabilities of performance-based and self-reported EFs measures on study success. Employing a retrospective cohort design, 748 first-year Applied Psychology students completed performance-based and self-report questionnaires to assess EFs. Maximum likelihood correlations were computed for 474 students, with data from 562-586 first-year students subsequently subjected to hierarchical regression analysis, accommodating pairwise missing values. Our results demonstrate minimal overlap between performance-based and self-reported EFs measures. Additionally, the model incorporating self-reported EFs accounted for 13% of the variance in study success after one year, with the inclusion of performance-based EFs raising this proportion to 16%. Self-reported EFs assessments modestly predict study success. However, monitoring levels of self-reported EFs could offer valuable insights for students and educational institutions, given that EFs play a crucial role in learning. Additionally, one in five students reports experiencing significant EFs difficulties, highlighting the importance of addressing EFs concerns for learning and study success.
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Pauses in speech may be categorized on the basis of their length. Some authors claim that there are two categories (short and long pauses) (Baken & Orlikoff, 2000), others claim that there are three (Campione & Véronis, 2002), or even more. Pause lengths may be affected in speakers with aphasia. Individuals with dementia probably caused by Alzheimer’s disease (AD) or Parkinson’s disease (PD) interrupt speech longer and more frequently. One infrequent form of dementia, non-fluent primary progressive aphasia (PPA-NF), is even defined as causing speech with an unusual interruption pattern (”hesitant and labored speech”). Although human listeners can often easily distinguish pathological speech from healthy speech, it is unclear yet how software can detect the relevant patterns. The research question in this study is: how can software measure the statistical parameters that characterize the disfluent speech of PPA-NF/AD/PD patients in connected conversational speech?
