Search

Past research has shown that interleaved presentation produces better recall when compared to blocked presentation. However, the structure of the study session can alter the effectiveness of interleaved over blocked study. Kost, Carvalho, and Goldstone (2015) found that repeating study items resulted in a benefit of blocked over interleaved presentation when items were studied actively. However, the effect of repetition during passive study, as well as the combination of both blocked and interleaved presentation during repeated study, remains unknown.The present experiment replicated the method of Kost and colleagues’ (2015) to evaluate a combined method which involves the presentation of study items in blocks followed by the repeated presentation of the same study items in interleaved order. The experiment also aimed to extend the findings of Kost and colleagues by evaluating the effectiveness of interleaved versus blocked presentation when participants study items passively. Combined presentation and interleaved-only presentation resulted in higher test performance than blocked-only presentation, but there was no significant difference between interleaved presentation and combined presentation.

An emphasis on marketing ethics instruction in higher education may be needed now more than ever. The Ethics Resource Center (ERC) reports that employees of the millennial generation are less cognizant of unethical practices in the workplace than previous generations, and suggests that the millennials' exposure and frequency to social media contributes to their disregard of unethical workplace behavior (―2011 National Business Ethics Survey,‖ 2012). Social media communication is popular among the millennial generation and is a requirement for modern-day businesses; yet, the nature of social media seems to be affecting this new generation of employees negatively. Could social media be used positively in marketing ethics instruction to enhance inductive learning of the millennial generation and encourage ethical workplace behavior? This quasi-experimental study sought to answer that question through a control and treatment group research design. Both groups received the same marketing ethics instruction, but the treatment group engaged in instruction through social media while the control group‘s instruction was delivered in class. A comparison of pre- and post-surveys of both groups sought to evaluate if social media could be used to make a positive impact on millennials‘ ethical workplace behavior. Noteworthy findings of the study included: (1) The preference of a closed Facebook page for academic use rather than other social media formats; (2) The tendency of frequent YouTube users to respond unethically to workplace behavior and marketing ethics scenarios; and (3) The support for marketing ethics instruction as a standalone course.

In this dissertation, two central problems in computer science are considered:(1) ranking n items from pairwise comparisons focussing on - (1a) handling adversarial noise (1b) incorporating feature information(2) robust matrix factorization algorithms focussing on - (2a) space-efficient computation (2b) using feature informationMotivated by several open theoretical and practical questions, novel solutions to the above problems are explored in detail; to be specific, the contributions are summarized below:(1) Ranking:(Part-1a) In the presence of adversarial noise, many popular ranking algorithms, such as maximum likelihood and rank centrality, for estimating standard models, fail to work well. Robustifying many existing models such as the binary choice models, an algorithm is devised to provably recover an epsilon-accurate ranking with high probability while allowing as large as O(n) grossly corrupted comparison results per item.(Part-1b) Typically, items have associated features. Subsuming several models including the classic Bradley-Terry-Luce and Thurstone models, a feature-based low-rank model is proposed and characterized. In learning this model, the O(n log n) sorting barrier is provably improved upon due to the proposed sample-efficient polynomial time algorithm that systematically uses features and feature correlations. Via empirical benchmarking, it is shown that the proposed approach consistently outperforms the state-of-the-art algorithms that do not use feature information.(2) Matrix factorization:(Part-2a) While principal component analysis and its variants are very well-studied, existing algorithms are either space-efficient or noise-tolerant; in contrast, incorporating both these aspects in solving eigen-problems, a space-efficient noise-tolerant algorithm with finite sample guarantees is developed and analyzed in the framework of the perturbed spiked covariance model under weaker assumptions than previous works; a key differentiating aspect from previous works is that the algorithm is streaming. Moreover, the derived computational and statistical guarantees are near optimal (up to logarithmic factors).(Part-2b) While robust subspace identification methods, both convex and non-convex, are well-studied, they are often oblivious to utilizing feature information that is available in practice; in contrast to existing (feature-oblivious) convex and non-convex approaches, a simple iterative method with a fast (linear) convergence property is proposed and studied - a key aspect that differentiates this from previous works is how side information is incorporated in achieving better robust subspace recovery.

Inductive learning refers to learning categories and concepts from exemplars of those categories or concepts. Such learning, which is a fundamental component of human cognition, enables us to classify never-before-encountered examples as instances of a given category or concept. How, though, should the exemplars of different categories, such as pictures of different species of butterflies, be presented in order to enhance inductive learning? The prevailing view has been that exemplars of a given category should be presented close together in time to highlight the commonalities that define that category, but Kornell and Bjork (2008)--to their surprise and against participants' intuitions--found that interleaving, not blocking, the exemplars of separate to-be-learned categories enhanced inductive learning. These findings, together with results from subsequent studies, suggest that the opportunity interleaving provides to contrast exemplars of different categories, such as an Admiral butterfly versus an Elfin butterfly, may be key to optimizing inductive learning. The eight experiments reported in this dissertation, which involved having participants learn species of butterflies, families of birds, the handwriting styles of different individuals, and the styles of different artists, were designed to clarify the roles of contrast processing (encoding differences between exemplars of different categories) and commonality processes (encoding commonalities across the exemplars within a category). Overall, the results obtained suggest that contrast processing and commonality processing are both critical, but that contrast processes occur automatically, whereas noticing commonalities is a deliberate and conscious process in category learning. Importantly, interleaving exemplars of different categories appears not only to facilitate contrast processing, but also, under some circumstances, commonality processing. Finally, it appears that learners--even when they know about the benefits of interleaving and the importance of contrast processing for achieving category learning--cannot effectively engage in contrast processing as a self-initiated study strategy without the aid of an interleaved presentation schedule. The present findings help to provide a more complete picture of the processes involved in feature extraction and category generalization. From a practical standpoint, the results also have implications for enhancing category learning in educational settings, where such learning is prevalent and critical to mastery and achievement.