The effect of student self -described learning styles within two models of teaching in an introductory data mining course

This dissertation examines the roles of learning styles and teaching methodologies within a data mining educational program designed for non-Computer Science undergraduate college students. The experimental design is framed by a discussion of the history and development of data mining and education, as well as a vision for its future.;Data mining is a relatively new discipline which has grown out of the fields of database management and data warehousing, statistics, logic, and decision sciences. Over the course of its approximately 15 year history, data mining has emerged from its genesis within the academic and commercial research and development arenas to become a widely accepted and utilized method of exploratory data analysis for management, strategic planning and decision support. Over the first several years of its development, data mining remained the province of computer scientists and professional statisticians at large corporations and research universities around the world. Beginning in about 1989, these data mining pioneers developed many of data mining\u27s standards and methodologies on large datasets using mainframe computing systems. Throughout the 1990s, as both the hardware and software tools required for the realization of data mining have become increasingly accessible, powerful and affordable, the pool of potential data miners has expanded rapidly. Today, even individuals and small businesses can exploit the power of data mining using freely acquirable open source software packages capable of running on personal computers.;During the growth and development of data mining methodologies however, little research has been dedicated specifically to the pedagogical approaches used in teaching data mining. Educational programs that have evolved have largely remained within Computer Science departments and have often targeted graduate students as an audience. This dissertation seeks to examine the possibility of successful teaching data mining concepts and techniques to a non-Computer Science undergraduate audience. The study approached this research question by delivering a lesson on the data mining topic of Association Rules to 86 participants who are representative of the target audience. These participants were randomly assigned to receive the Association Rules lesson through either a Direct Instruction or a Concept Attainment teaching approach. The students completed Kolb\u27s Learning Styles Inventory, participated in the data mining lesson, and then completed a quiz on the concepts and techniques of Association Rules. A t-test was used to determine if significant differences existed between the scores generated under the two teaching models, and an ANOVA was conducted to identify significant differences between the four learning style groups from Kolb\u27s instrument. In addition to these two statistical tests, the data were also mined using Association Rules and Decision Tree methods.;In both statistical tests, we failed to reject the null hypothesis, finding no significant differences in quiz scores between the two teaching models or among the four learning style groups. Further investigation into the differences among learning styles within teaching models however did reveal that the Assimilator learning style students who received their instruction via Direct Instruction did score significantly higher on the quiz than did their learning style counterparts who received the lesson via Concept Attainment. This finding suggests that although we cannot rely solely on one instructional approach as consistently more effective than the other, there may be instances where the correct instructional choice will positively benefit some learners with certain learning styles. The results of the data mining activities also support this assertion. Association Rules mining yielded no strong relationships between teaching models, learning styles and quiz scores, but Decision Tree mining did reveal a similar pattern of higher scores earned by Assimilator learners within Direct Instruction.;The findings of this study show that effectively teaching data mining concepts to undergraduate non-Computer Science students will not be as simple as choosing one teaching methodology over another or targeting a specific learning style group. Rather, designing instructional activities using teaching methodologies which closely align with predominant learning styles in a classroom should prove more effective. Perhaps the most significant finding of the study is that elementary data mining concepts and techniques can be effectively taught to the target audience. Finally, we recommend that additional teaching methodologies and perhaps different learning style assessments could be tested in the same way as those selected for this study

Functional Toxicogenomics: Mechanism-Centered Toxicology

Traditional toxicity testing using animal models is slow, low capacity, expensive and assesses a limited number of endpoints. Such approaches are inadequate to deal with the increasingly large number of compounds found in the environment for which there are no toxicity data. Mechanism-centered high-throughput testing represents an alternative approach to meet this pressing need but is limited by our current understanding of toxicity pathways. Functional toxicogenomics, the global study of the biological function of genes on the modulation of the toxic effect of a compound, can play an important role in identifying the essential cellular components and pathways involved in toxicity response. The combination of the identification of fundamental toxicity pathways and mechanism-centered targeted assays represents an integrated approach to advance molecular toxicology to meet the challenges of toxicity testing in the 21st century

A Comparative Study of the Degree of Self-directedness In High School Career and Technical Student Organizations Within A Southeast Regional Educational Service Agency

Career and Technical Student Organizations and Career and Technical Education programs in secondary education claim to prepare its students and members for post-secondary success. However, these claims are inherently difficult to study and quantify. By using a synthesis of the literature on emerging adulthood, self-directed learning, and self-leadership, this study explores the relationships between the presence of learner choice and readiness to engage in self-direction. Learners’ readiness to self-direct in learning was assessed using the SDLRS and analyzed using a comparative quantitative methods design based on involvement in a Career and Technical Student Organization or completion of a Career and Technical Education pathway. This study also explores the roles that student leadership and gender may play in self-directedness. The results of this study are intended to bring about a deeper understanding of the relationship between self-directed learning practices in CTE and CTSOs to aid advisors and leaders in optimizing the organization's operation and implementation of opportunities toward postsecondary success.Ott, KennethHeather, MorinDowney, SteveEd.D.Adult & Career Educatio

Monitoring the response of roads and railways to seasonal soil movement with persistent scatterers interferometry over six UK sites

Road and rail networks provide critical support for society, yet they can be degraded by seasonal soil movements. Currently, few transport network operators monitor small-scale soil movement, but understanding the conditions contributing to infrastructure failure can improve network resilience. Persistent Scatterers Interferometry (PSI) is a remote sensing technique offering the potential for near real-time ground movement monitoring over wide areas. This study tests the use of PSI for monitoring the response of major roads, minor roads, and railways to ground movement across six study sites in England, using Sentinel 1 data in VV polarisation in ascending orbit. Some soils are more stable than others—a national soil map was used to quantify the relationships between infrastructure movement and major soil groups. Vertical movement of transport infrastructure is a function of engineering design, soil properties, and traffic loading. Roads and railways built on soil groups prone to seasonal water-logging (Ground-water Gley soils, Surface-water Gley soils, Pelosols, and Brown soils) demonstrated seasonal subsidence and heave, associated with an increased risk of infrastructure degradation. Roads and railways over Podzolic soils demonstrated relative stability. Railways on Peat soils exhibited the most extreme continual subsidence of up to 7.5 mm year−1. Limitations of this study include the short observation period (~13 months, due to satellite data availability) and the regional scale of the soil map—mapping units contain multiple soil types with different ground movement potentials. Future use of a higher resolution soil map over a longer period will advance this research. Nevertheless, this study demonstrates the viability of PSI as a technique for measuring both seasonal soil-related ground movement and the associated impacts on road and rail infrastructure

Appraising the capability of a land biosphere model as a tool in modelling land surface interactions: results from its validation at selected European ecosystems

In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model in estimating key parameters characterising land surface interactions was evaluated. Specifically, SimSphere's performance in predicting Net Radiation (<i>R</i>_net), Latent Heat (LE), Sensible Heat (<i>H</i>) and Air Temperature (<i>T</i>_air) at 1.3 and 50 m was examined. Model simulations were validated by ground-based measurements of the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites. These included a variety of biomes, environmental and climatic conditions in the models evaluation. <br><br> Overall, model performance can largely be described as satisfactory for most of the experimental sites and evaluated parameters. For all model parameters compared, predicted <i>H</i> fluxes consistently obtained the highest agreement to the in-situ data in all ecosystems, with an average RMSD of 55.36 W m⁻². LE fluxes and <i>R</i>_net also agreed well with the in-situ data with RSMDs of 62.75 and 64.65 W m⁻² respectively. A good agreement between modelled and measured LE and <i>H</i> fluxes was found, especially for smoothed daily flux trends. For both <i>T</i>_air 1.3 m and <i>T</i>_air 50 m a mean RMSD of 4.14 and 3.54 °C was reported respectively. <br><br> This work presents the first all-inclusive evaluation of SimSphere, particularly so in a European setting. Results of this study contribute decisively towards obtaining a better understanding of the model's structure and its correspondence to the real world system. Findings also further establish the model's capability as a useful teaching and research tool in modelling Earth's land surface interactions. This is of considerable importance in the light of the rapidly expanding use of the model worldwide, including ongoing research by various Space Agencies examining its synergistic use with Earth Observation data towards the development of operational products at a global scale

Context Affects Quiet Eye Duration and Motor Performance Independent of Cognitive Effort

© 2021 Human Kinetics. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1123/jsep.2020-0026Extensive literature has shown the effect of ‘Quiet Eye’ (QE) on motor performance. However, little attention has been paid to the context in which tasks are executed(independent of anxiety) and the mechanisms that underpin the phenomenon. Here, we aimed to investigate the effects of context (independent of anxiety) on QE and performance while examining if the mechanisms underpinning QE are rooted in cognitive effort. In this study, 21novice participants completed golf putts while pupil dilation, QE duration, and putting accuracy were measured. Results showed putting to win was more accurate compared to the control (no context) condition and QE duration was longer when putting to win or tie a hole compared to control. There was no effect of context on pupil dilation. Results suggest that,while the task was challenging, performance scenarios can enhance representativeness of practice without adding additional load to cognitive resources, even for novice performers.Peer reviewe

Rapid rotation in be stars, (testing the null hypothesis: Be stars are all 'near-critical' rotators)

The purpose of this thesis is to test the null hypothesis that all Be stars rotate close to their critical velocities. To do this, a grid of synthetic Be-star spectra is constructed, for Veq/Krit = 0.95, accounting for gravity darkening, limb darkening and viewing angle. This grid explores the full parameter space of the B star domain, subject to a minimum equatorial temperature constraint (TLOCai > 6000i*T), for a range of equatorial rotational velocities. The models are compared to 95 of the 116 Be stars in the Chauville et al. (2001) atlas. Of the stars modelled, 79 are fit acceptably, 12 show minor mismatches, the general cause of which is attributed to the presence of emission in the spectra. The four remaining fits are unacceptable, three due to a high degree of shell spectrum contamination, another as a result of a binary companion. In essence, the null hypothesis is believed to survive. One important implication of this is that velocities on the order of the sound speed are sufficient to promote the formation of Be-star circumstellar disks

Pedagogically-driven Ontology Network for Conceptualizing the e-Learning Assessment Domain

The use of ontologies as tools to guide the generation, organization and personalization of e-learning content, including e-assessment, has drawn attention of the researchers because ontologies can represent the knowledge of a given domain and researchers use the ontology to reason about it. Although the use of these semantic technologies tends to enhance technology-based educational processes, the lack of validation to improve the quality of learning in their use makes the educator feel reluctant to use them. This paper presents progress in the development of an ontology network, called AONet, that conceptualizes the e-assessment domain with the aim of supporting the semi-automatic generation of assessment, taking into account not only technical aspects but also pedagogical ones.Fil: Romero, Lucila. Universidad Nacional del Litoral; ArgentinaFil: North, Matthew. The college of Idabo; Estados UnidosFil: Gutierrez, Milagros. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; ArgentinaFil: Caliusco, Maria Laura. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentin