Maria Psyridou

🚨New paper: We examined the developmental profiles of arithmetic fluency skills from Grades 1–9 and their predictors using kindergarten-age factors. Read it here: doi.org/10.1037/dev000… also open access: psyarxiv.com/cn83p/ @MinnaTorppa @KoponenTuire

Psykologian, kasvatustieteen @JYUedupsy ja informaatioteknologian @it_jyu tutkijat kehittivät koneoppimismalleja ennustamaan toisen asteen tutkinnon saavuttamista. Tutkimus julkaistiin Nature Scientific Reports -tiedelehdessä. r.jyu.fi/ExX

🤩Excited to share our new study in @Nature @SciReports! We've developed machine learning models that predicted upper secondary school dropout as early as the end of primary school. 🔗 nature.com/articles/s4159… (OA) @MinnaTorppa @KatiVasalampi @fabprezja @JYUedupsy @uniofjyvaskyla

✨New Study Published in @SSRJournal ✨ We traced how reading fluently developed from childhood to adulthood. Our growth model included a number of intersting predictors. I am incredibly proud of this study! @mariapsyridou @MinnaTorppa doi.org/10.1080/108884…

📚Discover what predicts reading fluency in adulthood by reading @DKhanolainen ’s study that used a growth curve model tracing the development from Grade 2 to Age 23 📖Dive into the details: osf.io/preprints/psya… #jyulukalenteri #ReadingDevelopment #ResearchInsights

Looking forward to our upcoming symposium where we'll explore how family risk for learning difficulties and the home environment intersect with academic outcomes. Join us tomorrow to be part of this intriguing discussion! @drElsje @DKhanolainen @arianetretow #ECDPTurku

And one was still missing: @mariapsyridou et al. are presenting a poster "Home environment factors and children’s and adolescents’ critical reading skills: A systematic review" on Session S19 Saturday 26th at 12-13.30 placed on UOM_R02

We are @EARLI2023 ! @Akatemia_STN @TampereUni_EDU @jyu_okl

Finally sharing this work feels so good📣 In this study, we traced how different types of academic difficulties developed over 9 years of compulsory schooling. We also tested several family-related predictors @MinnaTorppa @mariapsyridou @GGerike @neoprismc authors.elsevier.com/sd/article/S10…

Now Available! @mariapsyridou et al., aimed to use kindergarten-age variables to predict Grade 9 reading difficulties. Authors compared three model types and found neural networks as the most accurate method, suggesting strong promise for the field. tinyurl.com/722SSR