Using educational data mining methods to assess field-dependent and field-independent learners’ complex problem solving

The present study investigated the problem-solving performance of 101 university students and their interactions with a computer modeling tool in order to solve a complex problem. Based on their performance on the hidden figures test, students were assigned to three groups of field-dependent (FD), field-mixed (FM), and field-independent (FI) learners, and were instructed to use integrated-format materials and Model-It^® in order to solve a problem about immigration policy. The results showed that there were significant differences among the three groups of learners in terms of their problem-solving performance. Consequently, the study employed educational data mining (EDM) methods in order to examine how FD and FI learners actually interacted with Model-It^® in order to solve the problem. The EDM methods provided rich analytical information and details about learners’ interactions with the computer tool. Implications for designing effective joint cognitive systems are discussed.     

America’s Democracy Colleges: The Civic Engagement of Community College Students

This study explored the civic engagement of current two- and four-year students to explore whether differences exist between the groups and what may explain the differences. Using binary logistic regression and Ordinary Least Squares regression it was found that community-based engagement was lower for two- than four-year students, though community-based engagement increased as two-year students spent more time on campus, measured by enrolling full-time, living on campus, and working on campus. For political protests, two-year students were similar to four-year students, but when they spent more time on campus they were more likely to participate in political protests. For discussing politics, time spent on campus did not explain the differences between two- and four-year students. These findings indicate that while community college students arrive at college predisposed to lower rates of engagement than four-year students, these differences may be reduced by spending more time in educational settings by enrolling full-time, living on campus, and working on campus. Therefore, attending a community college does help students become productive members of society. Community college administrators and practitioners can use these findings to develop policies and practices that encourage students to develop the skills, abilities, and motivation necessary to be civically engaged.     

Effect of Robotics on Elementary Preservice Teachers’ Self-Efficacy,Science Learning,and Computational Thinking

The current impetus for increasing STEM in K-12 education calls for an examination of how preservice teachers are being prepared to teach STEM. This paper reports on a study that examined elementary preservice teachers’ (n = 21) self-efficacy, understanding of science concepts, and computational thinking as they engaged with robotics in a science methods course. Data collection methods included pretests and posttests on science content, prequestionnaires and postquestionnaires for interest and self-efficacy, and four programming assignments. Statistical results showed that preservice teachers’ interest and self-efficacy with robotics increased. There was a statistically significant difference between preknowledge and postknowledge scores, and preservice teachers did show gains in learning how to write algorithms and debug programs over repeated programming tasks. The findings suggest that the robotics activity was an effective instructional strategy to enhance interest in robotics, increase self-efficacy to teach with robotics, develop understandings of science concepts, and promote the development of computational thinking skills. Study findings contribute quantitative evidence to the STEM literature on how robotics develops preservice teachers’ self-efficacy, science knowledge, and computational thinking skills in higher education science classroom contexts.     

Stretching of DNA confined in nanochannels with charged walls

There is currently a growing interest in control of stretching of DNA inside nanoconfined regions due to the possibility to analyze and manipulate single biomolecules for applications such as DNA mapping and barcoding, which are based on stretching the DNA in a linear fashion. In the present work, we couple Finite Element Methods and Monte Carlo simulations in order to study the conformation of DNA molecules confined in nanofluidic channels with neutral and charged walls. We find that the electrostatic forces become more and more important when lowering the ionic strength of the solution. The influence of the nanochannel cross section geometry is also studied by evaluating the DNA elongation in square, rectangular, and triangular channels. We demonstrate that coupling electrostatically interacting walls with a triangular geometry is an efficient way to stretch DNA molecules at the scale of hundreds of nanometers. The paper reports experimental observations of λ-DNA molecules in poly(dimethylsiloxane) nanochannels filled with solutions of different ionic strength. The results are in good agreement with the theoretical predictions, confirming the crucial role of the electrostatic repulsion of the constraining walls on the molecule stretching.