Interview with ACPA–College Student Educators International Executive Director,Cindi Love     

Cindi Love 《About Campus》2015,20(3):3-7

  相似文献   

Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression     

Cindi Mason  Janet Twomey  David Wright  Lawrence Whitman 《Research in higher education》2018,59(3):382-400

As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm relationships between student attributes and attrition. Methods of prediction have also been evaluated and compared. Utilizing the attributes found in previous studies to have correlation with student attrition, this study considers the results of three different prediction methods—logistic regression, a multi-layer perceptron artificial neural network, and a probabilistic neural network (PNN)—to predict engineering student retention at a case study university. The purpose of this study was to introduce the PNN to the study of engineering student retention prediction and compare the results of the PNN to other commonly used methods in this field of study. The accuracy, sensitivity, specificity and overall results for each method are reported, compared, and discussed as the major contribution of this paper.  相似文献   

Student Motivation from and Resistance to Active Learning Rooted in Essential Science Practices     

Owens  David C.  Sadler  Troy D.  Barlow  Angela T.  Smith-Walters  Cindi 《Research in Science Education》2020,50(1):253-277

Several studies have found active learning to enhance students’ motivation and attitudes. Yet, faculty indicate that students resist active learning and censure them on evaluations after incorporating active learning into their instruction, resulting in an apparent paradox. We argue that the disparity in findings across previous studies is the result of variation in the active learning instruction that was implemented. The purpose of this study was to illuminate sources of motivation from and resistance to active learning that resulted from a novel, exemplary active-learning approach rooted in essential science practices and supported by science education literature. This approach was enacted over the course of 4 weeks in eight sections of an introductory undergraduate biology laboratory course. A plant concept inventory, administered to students as a pre-, post-, and delayed-posttest indicated significant proximal and distal learning gains. Qualitative analysis of open-response questionnaires and interviews elucidated sources of motivation and resistance that resulted from this active-learning approach. Several participants indicated this approach enhanced interest, creativity, and motivation to prepare, and resulted in a challenging learning environment that facilitated the sharing of diverse perspectives and the development of a community of learners. Sources of resistance to active learning included participants’ unfamiliarity with essential science practices, having to struggle with uncertainty in the absence of authoritative information, and the extra effort required to actively construct knowledge as compared to learning via traditional, teacher-centered instruction. Implications for implementation, including tips for reducing student resistance to active learning, are discussed.

  相似文献