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THE ANALYSIS OF THE RELATIONSHIP BETWEEN LEARNING STYLES AND THE LEARNERS' KNOWLEDGE ACQUISITION AND REACTIONS THROUGH THE ONLINE CASE STUDY
Title
THE ANALYSIS OF THE RELATIONSHIP BETWEEN LEARNING STYLES AND THE LEARNERS' KNOWLEDGE ACQUISITION AND REACTIONS THROUGH THE ONLINE CASE STUDY.
Creator
ZENG, RUI, Blasi, Laura, University of Central Florida
Abstract / Description
The purpose of this study was to examine the relationship between learning styles and student performance on a pre and post test, using an online case study, while also documenting their reactions to the case study. The case studies used in this research contained different storylines that showed multiple perspectives of case scenarios, giving students more choices to see what may happen in real school situations. Working with undergraduate students (N = 138) from the College of Education at...
Show moreThe purpose of this study was to examine the relationship between learning styles and student performance on a pre and post test, using an online case study, while also documenting their reactions to the case study. The case studies used in this research contained different storylines that showed multiple perspectives of case scenarios, giving students more choices to see what may happen in real school situations. Working with undergraduate students (N = 138) from the College of Education at a southeastern university, the researcher examined how students learned and responded to an online case study relative to their learning styles. Kolb's learning style inventory and a learner feedback survey questionnaire were administered respectively before and after the case study. Scores on Kolb's learning style inventory were used to classify the students' learning style preferences. A paired samples t-test was used to analyze the learners' knowledge test scores before and after the case study. The data revealed that the mean of students' post-test scores was significantly higher than the mean of their pre-test scores. Using descriptive methods, students' responses to the feedback questionnaire were analyzed. There was no difference shown between students with different learning style preferences, their overall reactions to the case study, and their reactions to certain elements (e.g., the content map, the assistants, and the navigation) included in the case study. Overall, most students' reactions to the case study were positive. Open-ended questions in the feedback questionnaire were analyzed and three assertions were generated. Of the optional features included within the case study, eighty two percent of students used the practice quizzes to self-check whether they understood the concepts and content covered in the cases. Students' post-test scores were congruent with their reactions to the online case study (with higher scoring students expressing more positive responses); and students' preferences regarding the use of online cases for study emerged in patterns relative to their career background. The study results showed that case studies can be used effectively in teacher education programs, while many learners (74%) favored using the case study and developed positive reactions through their case study experiences.
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Date Issued
2006
Identifier
CFE0001279, ucf:46884
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001279
SYNTAX-BASED CONCEPT EXTRACTION FOR QUESTION ANSWERING
Title
SYNTAX-BASED CONCEPT EXTRACTION FOR QUESTION ANSWERING.
Creator
Glinos, Demetrios, Gomez, Fernando, University of Central Florida
Abstract / Description
Question answering (QA) stands squarely along the path from document retrieval to text understanding. As an area of research interest, it serves as a proving ground where strategies for document processing, knowledge representation, question analysis, and answer extraction may be evaluated in real world information extraction contexts. The task is to go beyond the representation of text documents as "bags of words" or data blobs that can be scanned for keyword combinations and word...
Show moreQuestion answering (QA) stands squarely along the path from document retrieval to text understanding. As an area of research interest, it serves as a proving ground where strategies for document processing, knowledge representation, question analysis, and answer extraction may be evaluated in real world information extraction contexts. The task is to go beyond the representation of text documents as "bags of words" or data blobs that can be scanned for keyword combinations and word collocations in the manner of internet search engines. Instead, the goal is to recognize and extract the semantic content of the text, and to organize it in a manner that supports reasoning about the concepts represented. The issue presented is how to obtain and query such a structure without either a predefined set of concepts or a predefined set of relationships among concepts. This research investigates a means for acquiring from text documents both the underlying concepts and their interrelationships. Specifically, a syntax-based formalism for representing atomic propositions that are extracted from text documents is presented, together with a method for constructing a network of concept nodes for indexing such logical forms based on the discourse entities they contain. It is shown that meaningful questions can be decomposed into Boolean combinations of question patterns using the same formalism, with free variables representing the desired answers. It is further shown that this formalism can be used for robust question answering using the concept network and WordNet synonym, hypernym, hyponym, and antonym relationships. This formalism was implemented in the Semantic Extractor (SEMEX) research tool and was tested against the factoid questions from the 2005 Text Retrieval Conference (TREC), which operated upon the AQUAINT corpus of newswire documents. After adjusting for the limitations of the tool and the document set, correct answers were found for approximately fifty percent of the questions analyzed, which compares favorably with other question answering systems.
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Date Issued
2006
Identifier
CFE0000985, ucf:46711
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000985
THE EFFECT OF IMMEDIATE FEEDBACK AND AFTER ACTION REVIEWS (AARS) ON LEARNING, RETENTION AND TRANSFER
Title
THE EFFECT OF IMMEDIATE FEEDBACK AND AFTER ACTION REVIEWS (AARS) ON LEARNING, RETENTION AND TRANSFER.
Creator
Sanders, Michael, Williams, Kent, University of Central Florida
Abstract / Description
An After Action Review (AAR) is the Army training system's performance feedback mechanism. The purpose of the AAR is to improve team (unit) and individual performance in order to increase organizational readiness. While a large body of knowledge exists that discusses instructional strategies, feedback and training systems, neither the AAR process nor the AAR systems have been examined in terms of learning effectiveness and efficiency for embedded trainers as part of a holistic training system...
Show moreAn After Action Review (AAR) is the Army training system's performance feedback mechanism. The purpose of the AAR is to improve team (unit) and individual performance in order to increase organizational readiness. While a large body of knowledge exists that discusses instructional strategies, feedback and training systems, neither the AAR process nor the AAR systems have been examined in terms of learning effectiveness and efficiency for embedded trainers as part of a holistic training system. In this thesis, different feedback methods for embedded training are evaluated based on the timing and type of feedback used during and after training exercises. Those feedback methodologies include: providing Immediate Directive Feedback (IDF) only, the IDF Only feedback condition group; using Immediate Direct Feedback and delayed feedback with open ended prompts to elicit self-elaboration during the AAR, the IDF with AAR feedback condition group; and delaying feedback using opened ended prompts without any IDF, the AAR Only feedback condition group. The results of the experiment support the hypothesis that feedback timing and type do effect skill acquisition, retention and transfer in different ways. Immediate directive feedback has a significant effect in reducing the number of errors committed while acquiring new procedural skills during training. Delayed feedback, in the form of an AAR, has a significant effect on the acquisition, retention and transfer of higher order conceptual knowledge as well as procedural knowledge about a task. The combination of Immediate Directive Feedback with an After Action Review demonstrated the greatest degree of transfer on a transfer task.
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Date Issued
2005
Identifier
CFE0000441, ucf:46411
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000441
SINBAD AUTOMATION OF SCIENTIFIC PROCESS: FROM HIDDEN FACTOR ANALYSIS TO THEORY SYNTHESIS
Title
SINBAD AUTOMATION OF SCIENTIFIC PROCESS: FROM HIDDEN FACTOR ANALYSIS TO THEORY SYNTHESIS.
Creator
KURSUN, OLCAY, Favorov, Oleg V., University of Central Florida
Abstract / Description
Modern science is turning to progressively more complex and data-rich subjects, which challenges the existing methods of data analysis and interpretation. Consequently, there is a pressing need for development of ever more powerful methods of extracting order from complex data and for automation of all steps of the scientific process. Virtual Scientist is a set of computational procedures that automate the method of inductive inference to derive a theory from observational data dominated by...
Show moreModern science is turning to progressively more complex and data-rich subjects, which challenges the existing methods of data analysis and interpretation. Consequently, there is a pressing need for development of ever more powerful methods of extracting order from complex data and for automation of all steps of the scientific process. Virtual Scientist is a set of computational procedures that automate the method of inductive inference to derive a theory from observational data dominated by nonlinear regularities. The procedures utilize SINBAD – a novel computational method of nonlinear factor analysis that is based on the principle of maximization of mutual information among non-overlapping sources (Imax), yielding higher-order features of the data that reveal hidden causal factors controlling the observed phenomena. One major advantage of this approach is that it is not dependent on a particular choice of learning algorithm to use for the computations. The procedures build a theory of the studied subject by finding inferentially useful hidden factors, learning interdependencies among its variables, reconstructing its functional organization, and describing it by a concise graph of inferential relations among its variables. The graph is a quantitative model of the studied subject, capable of performing elaborate deductive inferences and explaining behaviors of the observed variables by behaviors of other such variables and discovered hidden factors. The set of Virtual Scientist procedures is a powerful analytical and theory-building tool designed to be used in research of complex scientific problems characterized by multivariate and nonlinear relations.
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Date Issued
2004
Identifier
CFE0000043, ucf:46124
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000043
Automatically Acquiring a Semantic Network of Related Concepts
Title
Automatically Acquiring a Semantic Network of Related Concepts.
Creator
Szumlanski, Sean, Gomez, Fernando, Wu, Annie, Hughes, Charles, Sims, Valerie, University of Central Florida
Abstract / Description
We describe the automatic acquisition of a semantic network in which over 7,500 of the most frequently occurring nouns in the English language are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from lexical co-occurrence in Wikipedia texts using a novel adaptation of an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among these semantic associates to...
Show moreWe describe the automatic acquisition of a semantic network in which over 7,500 of the most frequently occurring nouns in the English language are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from lexical co-occurrence in Wikipedia texts using a novel adaptation of an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among these semantic associates to automatically disambiguate them to their corresponding WordNet noun senses (i.e., concepts). The resultant concept-to-concept associations, stemming from 7,593 target nouns, with 17,104 distinct senses among them, constitute a large-scale semantic network with 208,832 undirected edges between related concepts. Our work can thus be conceived of as augmenting the WordNet noun ontology with RelatedTo links.The network, which we refer to as the Szumlanski-Gomez Network (SGN), has been subjected to a variety of evaluative measures, including manual inspection by human judges and quantitative comparison to gold standard data for semantic relatedness measurements. We have also evaluated the network's performance in an applied setting on a word sense disambiguation (WSD) task in which the network served as a knowledge source for established graph-based spreading activation algorithms, and have shown: a) the network is competitive with WordNet when used as a stand-alone knowledge source for WSD, b) combining our network with WordNet achieves disambiguation results that exceed the performance of either resource individually, and c) our network outperforms a similar resource, WordNet++ (Ponzetto (&) Navigli, 2010), that has been automatically derived from annotations in the Wikipedia corpus.Finally, we present a study on human perceptions of relatedness. In our study, we elicited quantitative evaluations of semantic relatedness from human subjects using a variation of the classical methodology that Rubenstein and Goodenough (1965) employed to investigate human perceptions of semantic similarity. Judgments from individual subjects in our study exhibit high average correlation to the elicited relatedness means using leave-one-out sampling (r = 0.77, ? = 0.09, N = 73), although not as high as average human correlation in previous studies of similarity judgments, for which Resnik (1995) established an upper bound of r = 0.90 (? = 0.07, N = 10). These results suggest that human perceptions of relatedness are less strictly constrained than evaluations of similarity, and establish a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness. We also contrast the performance of a variety of similarity and relatedness measures on our dataset to their performance on similarity norms and introduce our own dataset as a supplementary evaluative standard for relatedness measures.
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Date Issued
2013
Identifier
CFE0004759, ucf:49767
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004759