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Creators/Authors contains: "Ostrow, K. S."

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  1. ; ; ( , 11th International Conference on Computer Supported Education)
    Because Chinese reading and writing systems are not phonetic, Mandarin Chinese learners must construct six-way mental connections in order to learn new words, linking characters, meanings, and sounds. Little research has focused on the difficulties inherent to each specific component involved in this process, especially within digital learning environments. The present work examines Chinese word acquisition within ASSISTments, an online learning platform traditionally known for mathematics education. Students were randomly assigned to one of three conditions in which researchers manipulated a learning assignment to exclude one of three bi-directional connections thought to be required for Chinese language acquisition (i.e., sound-meaningmore »and meaning-sound). Researchers then examined whether students’ performance differed significantly when the learning assignment lacked sound-character, character-meaning, or meaning-sound connection pairs, and whether certain problem types were more difficult for students than others. Assessment of problems by component type (i.e., characters, meanings, and sounds) revealed support for the relative ease of problems that provided sounds, with students exhibiting higher accuracy with fewer attempts and less need for system feedback when sounds were included. However, analysis revealed no significant differences in word acquisition by condition, as evidenced by next-day post-test scores or pre- to post-test gain scores. Implications and suggestions for future work are discussed.« less
    Accepted Manuscript Full Text Available
  2. ; ( , Proceedings of the Nineteenth International Conference on Artificial Intelligence in Education)
    Online learning environments allow for the implementation of psychometric scales on diverse samples of students participating in authentic learning tasks. One such scale, the Intrinsic Motivation Inventory (IMI) can be used to inform stakeholders of students’ subjective motivational and regulatory styles. The IMI is a multidimensional scale developed in support of Self-Determination Theory [1, 2, 3], a strongly validated theory stating that motivation and regulation are moderated by three innate needs: autonomy, belonging, and competence. As applied to education, the theory posits that students who perceive volition in a task, those who report stronger connections with peers and teachers, andmore »those who perceive themselves as competent in a task are more likely to internalize the task and excel. ASSISTments, an online mathematics platform, is hosting a series of randomized controlled trials targeting these needs to promote integrated learning. The present work supports these studies by attempting to validate four subscales of the IMI within ASSISTments. Iterative factor analysis and item reduction techniques are used to optimize the reliability of these subscales and limit the obtrusive nature of future data collection efforts. Such scale validation efforts are valuable because student perceptions can serve as powerful covariates in differentiating effective learning interventions.« less
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ( , Proceedings of the Eleventh International Conference on Educational Data Mining)
    A substantial amount of research has been conducted by the educational data mining community to track and model learning. Previous work in modeling student knowledge has focused on predicting student performance at the problem level. While informative, problem-to-problem predictions leave little time for interventions within the system and relatively no time for human interventions. As such, modeling student performance at higher levels, such as by assignment, may provide a better opportunity to develop and apply learning interventions preemptively to remedy gaps in student knowledge. We aim to identify assignment-level features that predict whether or a not a student will finishmore »their next homework assignment once started. We employ logistic regression models to test which features best predict whether a student will be a “starter” or a “finisher” on the next assignment.« less
    Accepted Manuscript Full Text Available