@article{ChevalereLazaridesYunetal.2023,
  author    = {Cheval{\`e}re, Johann and Lazarides, Rebecca and Yun, Hae Seon and Henke, Anja and Lazarides, Claudia and Pinkwart, Niels and Hafner, Verena V.},
  title     = {Do instructional strategies considering activity emotions reduce students' boredom in a computerized open-ended learning environment?},
  series = {Computers and education},
  volume    = {196},
  journal   = {Computers and education},
  publisher = {Elsevier},
  issn      = {1873-782X},
  doi       = {10.1016/j.compedu.2023.104741},
  year      = {2023},
  abstract  = {Providing students with efficient instruction tailored to their individual characteristics in the cognitive and affective domains is an important goal in research on computer-based learning. This is especially important when seeking to enhance students' learning experience, such as by counteracting boredom, a detrimental emotion for learning. However, studies comparing instructional strategies triggered by either cognitive or emotional characteristics are surprisingly scarce. In addition, little research has examined the impact of these types of instructional strategies on performance and boredom trajectories within a lesson. In the present study, we compared the effectiveness of an intelligent tutoring system that adapted variable levels of hint details to a combination of students' dynamic, self-reported emotions and task performance (i.e., the experimental condition) to a traditional hint delivery approach consisting of a progressive, incremental supply of details following students' failures (i.e., the control condition). Linear mixed models of time-related changes in task performance and the intensity of boredom over two 1-h sessions showed that students (N = 104) in the two conditions exhibited equivalent progression in task performance and similar trajectories in boredom intensity. However, a consideration of students' achievement levels in the analyses (i.e., their final performance on the task) revealed that higher achievers in the experimental condition showed a reduction in boredom during the first session, suggesting possible benefits of using emotional information to increase the contingency of the hint delivery strategy and improve students' learning experience.},
  language  = {en}
}
@article{SorensenHohensteinVasishth2016,
  author    = {Sorensen, Tanner and Hohenstein, Sven and Vasishth, Shravan},
  title     = {Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists},
  series = {Tutorials in Quantitative Methods for Psychology},
  volume    = {12},
  journal   = {Tutorials in Quantitative Methods for Psychology},
  publisher = {University of Montreal, Department of Psychology},
  address   = {Montreal},
  issn      = {2292-1354},
  doi       = {10.20982/tqmp.12.3.p175},
  pages     = {175 -- 200},
  year      = {2016},
  abstract  = {With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in experimentally-driven areas like psychology, linguistics, and cognitive science. This tutorial provides a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. We choose Stan (rather than WinBUGS or JAGS) because it provides an elegant and scalable framework for fitting models in most of the standard applications of LMMs. We ease the reader into fitting increasingly complex LMMs, using a two-condition repeated measures self-paced reading study.},
  language  = {en}
}
@article{MassonKliegl2013,
  author    = {Masson, Michael E. J. and Kliegl, Reinhold},
  title     = {Modulation of additive and interactive effects in lexical decision by Trial History},
  series = {Journal of experimental psychology : Learning, memory, and cognition},
  volume    = {39},
  journal   = {Journal of experimental psychology : Learning, memory, and cognition},
  number    = {3},
  publisher = {American Psychological Association},
  address   = {Washington},
  issn      = {0278-7393},
  doi       = {10.1037/a0029180},
  pages     = {898 -- 914},
  year      = {2013},
  abstract  = {Additive and interactive effects of word frequency, stimulus quality, and semantic priming have been used to test theoretical claims about the cognitive architecture of word-reading processes. Additive effects among these factors have been taken as evidence for discrete-stage models of word reading. We present evidence from linear mixed-model analyses applied to 2 lexical decision experiments indicating that apparent additive effects can be the product of aggregating over- and underadditive interaction effects that are modulated by recent trial history, particularly the lexical status and stimulus quality of the previous trial's target. Even a simple practice effect expressed as improved response speed across trials was powerfully modulated by the nature of the previous target item. These results suggest that additivity and interaction between factors may reflect trial-to-trial variation in stimulus representations and decision processes rather than fundamental differences in processing architecture.},
  language  = {en}
}