An examination of the influence of Powerpoint lectures in higher education upon student assigned reading completion

Title from PDF of title page (University of Missouri--Columbia, viewed on October 19, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Sandy HutchinsonVita.Ed. D. University of Missouri--Columbia 2011."May 2011"This mixed methods research examined the influence of PowerPoint presentation as a means of delivering content in higher education courses and the influence of this instructional mode on assigned student reading completion. Participants included faculty members and students enrolled in one program discipline area using separate student and faculty member online surveys, two student focus group sessions, a faculty focus group session, and separate student and faculty interview sessions and document analysis to collect data. The study findings revealed several emerging themes: (a) an informational sifting generation, (b) differing faculty philosophies of teaching/learning theory, and (c) co-dependence of student motivation and teacher reflective instruction. Overall the research discovered that reflective use of PowerPoint and other student centered learning perspectives could positively impact assigned reading and other characteristics of active learning in the classroom.Includes bibliographical reference

Competence-based Curriculum Learning for Neural Machine Translation

Current state-of-the-art NMT systems use large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch sizes. This is undesirable as it requires extensive hyperparameter tuning. In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance. Our framework consists of a principled way of deciding which training samples are shown to the model at different times during training, based on the estimated difficulty of a sample and the current competence of the model. Filtering training samples in this manner prevents the model from getting stuck in bad local optima, making it converge faster and reach a better solution than the common approach of uniformly sampling training examples. Furthermore, the proposed method can be easily applied to existing NMT models by simply modifying their input data pipelines. We show that our framework can help improve the training time and the performance of both recurrent neural network models and Transformers, achieving up to a 70% decrease in training time, while at the same time obtaining accuracy improvements of up to 2.2 BLEU

The Roles of Symbols in Neural-based AI: They are Not What You Think!

We propose that symbols are first and foremost external communication tools used between intelligent agents that allow knowledge to be transferred in a more efficient and effective manner than having to experience the world directly. But, they are also used internally within an agent through a form of self-communication to help formulate, describe and justify subsymbolic patterns of neural activity that truly implement thinking. Symbols, and our languages that make use of them, not only allow us to explain our thinking to others and ourselves, but also provide beneficial constraints (inductive bias) on learning about the world. In this paper we present relevant insights from neuroscience and cognitive science, about how the human brain represents symbols and the concepts they refer to, and how today's artificial neural networks can do the same. We then present a novel neuro-symbolic hypothesis and a plausible architecture for intelligent agents that combines subsymbolic representations for symbols and concepts for learning and reasoning. Our hypothesis and associated architecture imply that symbols will remain critical to the future of intelligent systems NOT because they are the fundamental building blocks of thought, but because they are characterizations of subsymbolic processes that constitute thought.Comment: 28 page

Exploring Self-Efficacy and Anxiety in First-Year Nursing Students Enrolled in a Discipline-Specific Scholarly Writing Course

Background: Very few studies measuring writing self-efficacy or anxiety in undergraduate nursing students exist in the education literature. The purpose of the present investigation was to identify if changes to writing self-efficacy and writing anxiety will occur in first-year baccalaureate nursing students who are exposed to a discipline-specific scholarly writing course employing scaffolding strategies as the primary instructional method. Concurrently, this study was the pilot test for a new measure assessing writing self-efficacy, The Self-Efficacy Scale for Academic Writing. Method: A one-group pre-test/posttest design was employed. Sixty-four (64) paired questionnaires were available for analysis. Bandura’s self-efficacy theory and a scaffolding process guided the study. Results: Anxiety was significantly reduced from pre-test to posttest (p = .005). Writing self-efficacy improved and was near but not significant (p = .051). Writing self-efficacy at pre-test predicted 15.4% of the variance in final self-reported grade on the scholarly paper (p = .001). Students who reported writing their paper late or last minute reported significantly higher writing self-efficacy compared to students who reported adhering to the paper task schedule (p = .021). There were no differences in writing self-efficacy scores based on student past experience with writing or their help seeking activities. Conclusion: First year nursing students can benefit from taking a discipline-specific writing course incorporating scaffolding as an instructional method as both writing anxiety and writing self-efficacy can potentially be improved in this population. However additional research is required to support this claim. Résumé Contexte : Il existe très peu d’études publiées en formation qui mesurent l’auto-efficacité ou l’anxiété quant à la rédaction chez les étudiantes de premier cycle en sciences infirmières. Le but de la présente étude était de déterminer s’il y aurait des changements au niveau de l’auto-efficacité et de l’anxiété chez des étudiantes de première année de baccalauréat inscrites à un cours de rédaction scientifique particulière à la discipline utilisant des stratégies de soutien décroissant comme principale méthode d’enseignement. Simultanément, cette recherche servait d’étude pilote pour une nouvelle échelle de mesure d’évaluation de l’auto-efficacité quant à la rédaction scientifique : The Self-Efficacy Scale for Academic Writing [l’échelle d’auto-efficacité pour la rédaction académique]. Méthode : Un modèle prétest/post-test a été utilisé auprès d’un groupe. Soixante-quatre (64) questionnaires appariés ont été analysés. L’étude a été guidée par la théorie de l’auto-efficacité de Bandura et le processus de soutien décroissant. Résultats : Le niveau d’anxiété a diminué de manière significative entre le prétest et le post-test (p = 0,005). L’auto-efficacité quant à la rédaction, s’est améliorée s’approchant mais n’atteignant pas le seuil de signification (p = 0,051). L’auto-efficacité en matière de rédaction au prétest a prédit 15,4 % de la variance de la note finale autodéclarée sur le travail universitaire (p = 0,001). Les étudiantes qui ont affirmé avoir écrit leur travail en retard ou à la dernière minute ont déclaré une auto-efficacité en matière de rédaction significativement supérieure à celle des étudiantes qui ont affirmé avoir respecté l’horaire des activités reliées au travail (p = 0,021). Il n’y avait aucune différence dans les résultats d’auto-efficacité selon l’expérience de l’étudiante en écriture ou ses activités de recherche d’aide. Conclusion : Les étudiantes de première année en sciences infirmières peuvent bénéficier d’un cours de rédaction particulière à leur discipline, qui intègre le soutien décroissant comme méthode d’enseignement, car il a le potentiel de diminuer l’anxiété chez cette population et améliorer leur auto-efficacité en matière de rédaction. Toutefois, des recherches supplémentaires seront nécessaires pour appuyer cette affirmation

A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3–4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind