Much of the economic education literature suggests that the principles of economics class size does not significantly affect student performance. However, study methods have varied in terms of the aggregation level (student or class), the measure of performance (TUCE or course letter grade), and the class size measure (e.g., students who completed both the TUCE pretest and posttest). The author...

We investigated the effects of teacher human and social capital on growth in student performance in a sample of 1,013 teachers organized into 239 grade teams. We found that teacher human capital that is specific to a setting and task, and some indicators of teacher social capital, predicted student performance improvement. At the team level, average educational attainment and horizontal tie str...

Much of the power of a computer tutor comes from its ability to assess students. In some domains, including oral reading, assessing the proficiency of a student is a challenging task for a computer. Our approach for assessing student reading proficiency is to use data that a computer tutor collects through its interactions with a student to estimate his performance on a human-administered test ...

Providing students with continuous and personalized feedback on their performance is an important part of encouraging self regulated learning. As part of our higher education platform, we built a set of data visualizations to provide feedback to students on their assignment performance. These visualizations give students information about how they are doing compared to the rest of the class, an...

To review the state of research on the association between physical activity among school-aged children and academic outcomes, the author reviewed published studies on this topic. A table includes brief descriptions of each study's research methodology and outcomes. A review of the research demonstrates that there may be some short-term improvements of physical activity (such as on concentratio...

Predicting student performance, one of the tasks in educational data mining, has been taken into account recently [Toscher and Jahrer 2010; Yu et al. 2010; Cetintas et al. 2010; Thai-Nghe et al. 2011]. It was selected as a challenge task for the KDD Cup 2010 [Koedinger et al. 2010]. Concretely, predicting student performance is the task where we would like to know how the students learn (e.g. g...

In this paper we consider discrimination-aware classification of educational data. Mining and using rules that distinguish groups of students based on sensitive attributes such as gender and nationality may lead to discrimination. It is desirable to keep the sensitive attributes during the training of a classifier to avoid information loss but decrease the undesirable correlation between the se...

Many tutoring systems currently in use provide a wealth of information pertaining to student learning over long periods of time. Providing meaningful representations of student performance can indicate levels of knowledge and understanding that can alert instructors to potential struggling students in order to provide aid where it is needed; it is the goal of many researchers to even provide su...

This or/ide IPUS acce/J/!!d lur /JUbIiC(l/ir)17 }/I/I' I~ 1994 F ieldwork is an lmportant aspect of occupational therapy education. It proVides an opportunity for students to cJemonstrate their understanding of didactic classroom material by aprlying their knowledge in;] controlled pr;]ctice setting with sU[lervision. Students must demonstrate an acceptahle level of clinical skill by meeting pr...

The educational data mining community has not been paying much attention to how much assistance a student needs. Feng and Heffernen[1] showed that we can predict student performance better by accounting for amount of assistance, but didn't reduce it to a value that could be shared with students. In this paper we want to see if we can better model student performance by replacing traditional bin...