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Creators/Authors contains: "Li, Tianyu"

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  1. Free, publicly-accessible full text available October 1, 2025
  2. Free, publicly-accessible full text available May 13, 2025
  3. The present study tested the learning avoidance model by examining the degree to which learning avoidance in various afterschool settings mediated the negative association between math anxiety and math achievement. Participants consisted of 207 third to sixth graders. Using a path model, findings showed that students’ math anxiety was negatively associated with both standardized math achievement test scores and parent-reported math school grades. Additionally, higher math anxiety was associated with more negative homework behaviors and less frequent participation in math-related extracurricular activities. Finally, the association between math anxiety and math achievement was partially mediated by negative math homework behaviors and participation in math extracurricular activities. Effort in math exam preparation did not contribute to explaining the association between math anxiety and math achievement. Overall, these findings support the learning avoidance model and suggest that avoidance behaviors in everyday learning in the afterschool setting may contribute to explaining the undesired math achievement among highly math anxious students.

     
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  4. The proliferation of modern data processing tools has given rise to open-source columnar data formats. These formats help organizations avoid repeated conversion of data to a new format for each application. However, these formats are read-only, and organizations must use a heavy-weight transformation process to load data from on-line transactional processing (OLTP) systems. As a result, DBMSs often fail to take advantage of full network bandwidth when transferring data. We aim to reduce or even eliminate this overhead by developing a storage architecture for in-memory database management systems (DBMSs) that is aware of the eventual usage of its data and emits columnar storage blocks in a universal open-source format. We introduce relaxations to common analytical data formats to efficiently update records and rely on a lightweight transformation process to convert blocks to a read-optimized layout when they are cold. We also describe how to access data from third-party analytical tools with minimal serialization overhead. We implemented our storage engine based on the Apache Arrow format and integrated it into the NoisePage DBMS to evaluate our work. Our experiments show that our approach achieves comparable performance with dedicated OLTP DBMSs while enabling orders-of-magnitude faster data exports to external data science and machine learning tools than existing methods. 
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  5. Background

    Math anxiety (MA) and math achievement are generally negatively associated.

    Aims

    This study investigated whether and how classroom engagement behaviors mediate the negative association between MA and math achievement.

    Sample

    Data were drawn from an ongoing longitudinal study that examines the roles of affective factors in math learning. Participants consisted of 207 students from 4th through 6th grade (50% female).

    Methods

    Math anxiety was measured by self‐report using the Mathematics Anxiety Scale for Children (Chiu & Henry, 1990,Measurement and valuation in Counseling and Development, 23, 121). Students self‐reported their engagement in math classrooms using a modified version of the Math and Science Engagement Scale (Wang et al., 2016,Learning and Instruction, 43, 16). Math achievement was assessed using the Applied Problem, Calculations, and Number Matrices subtests from the Woodcock‐Johnson IV Tests of Achievement (Schrank et al., 2014,Woodcock‐Johnson IV Tests of Achievement. Riverside). Mediation analyses were conducted to examine the mediating role of classroom engagement in the association between MA and math achievement.

    Results

    Students with higher MA demonstrated less cognitive‐behavioral and emotional engagement compared to students with lower MA. Achievement differences among students with various levels of MA were partly accounted for by their cognitive‐behavioral engagement in the math classroom.

    Conclusions

    Overall, students with high MA exhibit avoidance patterns in everyday learning, which may act as a potential mechanism for explaining why high MA students underperform their low MA peers.

     
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  6. Fast track article for IS&T International Symposium on Electronic Imaging 2020: Computational Imaging proceedings. 
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