KaydedilenlerDerginizi ekleyin

Dergiler / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2019 / Cilt: 10 - Sayı: 4

The Effects of Log Data on Students’ Performance

Hatice Cigdem YAVUZ
Sayfa
378–390
Yayın Tarihi
2019-12-13
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Moreover, the interaction only with reading and the number of actions was found to have a positive effect on the students’ overall performance.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:This study aimed to assess the relationships between response times (RTs), the number of actions taken to solve a given item, and student performance.
📊
Temel Bulgular & Veri:In addition, the interaction between the students’ information and communications technology (ICT) competency, reading literacy, and log data (time and number of actions) were examined in order to gain additional insights regarding the relations between student performance and log data.
💡
Sonuç & Katkı:Moreover, the interaction only with reading and the number of actions was found to have a positive effect on the students’ overall performance.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Test-taking behaviors · explanatory item response modelling · log data

Abstract

This study aimed to assess the relationships between response times (RTs), the number of actions taken to solve a given item, and student performance. In addition, the interaction between the students’ information and communications technology (ICT) competency, reading literacy, and log data (time and number of actions) were examined in order to gain additional insights regarding the relations between student performance and log data. The sample consisted of 2 348 students who participated in the triennial international large-scale assessment of the Programme for International Student Assessment (PISA). For the current study, 18 items in the one cluster of the 91st booklet were chosen. To achieve the aim of the study, explanatory item response modeling (EIRM) framework based on generalized linear mixed modeling (GLMM) was used. The results of this study showed that students who spent more time on items and those that took more actions on items were more likely to answer the items correctly. However, this effect did not have variability across items and students. Moreover, the interaction only with reading and the number of actions was found to have a positive effect on the students’ overall performance.

🔖

Bu Makaleye Atıf Yap

YAVUZ, H. C. (2019). The Effects of Log Data on Students’ Performance. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Cilt: 10 - Sayı: 4, 378–390. https://ajindex.com/articles/137288

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (8)

  1. Azzolini, D., Bazoli, N., Lievore, I., Schizzerotto, A., & Vergolini, L. (2019). Beyond achievement. A comparative look into 15-year-olds’ school engagement, effort and perseverance in the European Union. Luxembourg: Publications Office of the European Union. Bates, D., & Maechler, M. (2014). lme4: Linear mixed-effects models using S4 classes. R package. Available from http://CRAN.R-project.org/package=lme4.
  2. Briggs, D. C. (2008). Using explanatory item response models to analyze group differences in science achievement. Applied Measurement in Education, 21(2), 89-118. doi: 10.1080/08957340801926086 De Ayala, R. J. (2009). The theory and practice of Item Response Theory. New York: The Guilford Press.
  3. De Boeck, P., & Wilson, M. (Eds.). (2004). Explanatory item response models: A generalized linear and nonlinear approach. New York, NY: Springer, doi: 10.1007/978-1-4757-3990-9
  4. De Boeck, P., Bakker, M., Zwitser, R., Nivard, M., Hofman, A., Tuer- linckx, F., & Partchev, I. (2011). The estimation of item response models with the lmer function from the lme4 package in R. Journal of Statistical Software, 39, 1-28. Dodonova Y. A., & Dodonov Y. S. (2013). Faster on easy items, more accurate on difficult ones: Cognitive ability and performance on a task of varying difficulty. Intelligence, 41, 1–10. doi: 10.1016/j.intell.2012.10.003 Goldhammer, F., Naumann, J., & Greiff, S. (2015). More is not always better: The relation between item response and item response time in Raven’s matrices. Journal of Intelligence, 3(1), 21-40.
  5. Goldhammer, F., Naumann, J., Stelter, A., Tóth, K., Rölke, H., & Klieme, E. (2014). The time on task effect in reading and problem solving is moderated by task difficulty and skill: Insights from a computer-based large-scale assessment. Journal of Educational Psychology, 106(3), 608. Goldhammer, F., & Klein Entink, R. (2011). Speed of reasoning and its relation to reasoning ability. Intelligence, 39, 108-119.
  6. Greiff, S., Niepel, C., Scherer, R., & Martin, R. (2016). Understanding students' performance in a computer-based assessment of complex problem solving: An analysis of behavioral data from computer-generated log files. Computers in Human Behavior, 61, 36-46.
  7. Greiff, S., Wüstenberg, S., & Avvisati, F. (2015). Computer-generated log-file analyses as a window into students' minds? A showcase study based on the PISA 2012 assessment of problem solving. Computers & Education, 91, 92-105.
  8. Greiff, S., Wüstenberg, S., Csapo, B., Demetriou, A., Hautamaki