Knowledge and Training in Language Sample Analysis of US Speech-Language Pathology Graduate Students

Purpose: Speech-language pathologists (SLPs) play an integral role in identification and treatment of developmental language disorders (DLD). Best practices include the use of language sample analysis (LSA) as part of a comprehensive evaluation. However, LSA requires a specific set of foundational morphological and syntactic knowledge. Previous studies have shown a knowledge gap for both SLPs and SLP graduate students for other areas of morphosyntax and phonology. This study examined the language analysis skills of current SLP graduate students on a test of Mean Length of Utterance (MLU) analysis and Clausal Density (CD) and whether there were possible factors associated with performance outcomes. Method: A national web-based survey was distributed to accredited US SLP graduate programs to disseminate to their students. From the 37 programs which participated, 239 individual students completed they survey. Respondents answered questions about their experiences with LSA, didactic course instruction, and completed a skills test that examined their knowledge of MLU, grammatical morphemes, independent and dependent clauses, and CD. The students’ previous experiences with LSA were examined as potential factors affecting performance outcomes. Results: The majority of students (88.3%) failed to obtain a mastery level of 80% on MLU skills and none of the students achieved a mastery level of 80% in the CD skills. Previous coursework and general LSA experience had no effect on scores while the use of specific LSA tools and protocols had a significant relationship. Conclusion: The lack of mastery for MLU and CD skills by the SLP graduate students indicate that the ability to reliably analyze language samples is not present. Current instructional practices at the undergraduate and graduate level would indicate that students lack the clinical skills to accurately evaluate language samples for the morphosyntactic structures that are clinical markers of DLD. Implications include the examination of current graduate education and continuing development for practicing SLPs

Comparative Characteristic Lifestyle Approaches of Persisters and Dropouts in Adult High Schools in Tennessee

The problem of this study was that no data existed on characteristics of life style management as related to persisters and dropouts among adult high school students in the state of Tennessee. The purpose of the study was to determine the characteristic life style approaches most prevalent among persistent enrollees, graduates, and dropouts of selected adult high school programs in Tennessee. Five hundred fifty-nine persisters and 868 dropouts were surveyed by mail, by telephone, or by school site visits. There were 419 participants in the study, 311 persisters and 108 dropouts. The research was descriptive in nature and utilized data gathered from a survey instrument entitled, Life Style Approaches (LSA) Scale. The instrument was developed by Williams and Long (1991) based on a collection of self-management strategies. Six self-management strategies were identified in the 22 item instrument, and respondents were asked to report to what degree each item was or was not similar to their life styles. The instrument was piloted on 50 adult high school students in Hamblen and Greene Counties who were not in the study sample. Pilot results indicated that reading and comprehension levels were adequate for the students surveyed. Findings were divided into two categories, demographics and the findings as a result of hypothesis testing. Seventy-three percent of respondents were born after 1960, 88% were Caucasian, and 52% lacked one year or less to graduate. An equal number were married and single, and 55% were employed. Incomes of respondents ranged from less than $5,000 to \$40,000; however, 31$5,000. With regard to hypothesis testing, no significant differences were found between dropouts and persisters in the demographic areas of age gender, race, marital status, or occupational status. There was a significant difference between dropouts and persisters in the number of years needed to graduate. Of the self-management practices (performance focus and efficiency, goal directedness, timeliness of task accomplishment, organization of physical space, written plans for change, and verbal support for self-management), only performance focus and efficiency was found to be significantly different between dropouts and persisters. The performance focus and efficiency factor is closely related to self-efficacy, and persisters had a greater degree of self-efficacy than did the dropouts reported in this study

Product and Process in Toefl iBT Independent and Integrated Writing Tasks: A Validation Study

This study was conducted to compare the writing performance (writing products and writing processes) of the TOEFL iBT integrated writing task (writing from source texts) with that of the TOEFL iBT independent writing task (writing from prompt only). The study aimed to find out whether writing performance varies with task type, essay scores, and academic experience of test takers, thus clarifying the link between the expected scores and the underlying writing abilities being assessed. The data for the quantitative textual analysis of written products was provided by Educational Testing Service (ETS). The data consisted of scored integrated and independent essays produced by 240 test takers. Coh-Metrix (an automated text analysis tool) was used to analyze the linguistic features of the 480 essays. Statistic analysis results revealed the linguistic features of the essays varied with task type and essay scores. However, the study did not find significant impact of the academic experience of the test takers on most of the linguistic features investigated. In analyzing the writing process, 20 English as a second language students participated in think-aloud writing sessions. The writing tasks were the same tasks used in the textual analysis section. The writing processes of the 20 participants was coded for individual writing behaviors and compared across the two writing tasks. The writing behaviors identified were also examined in relation to the essay scores and the academic experience of the participants. Results indicated that the writing behaviors varied with task type but not with the essay scores or the academic experience of the participants in general. Therefore, the results of the study provided empirical evidence showing that the two tasks elicited different writing performance, thus justifying the concurrent use of them on a test. Furthermore, the study also validated the scoring rubrics used in evaluating the writing performance and clarified the score meaning. Implications of the current study were also discussed

Analyzing the effects of individual and team attributes on new product design outcomes - Experimental Protocol Development and Feasibility Assessment

Rapidly changing markets demand quick turnaround from creative concepts into final products. This requires firms to have extensive collaboration in their New Product Development (NPD) teams. However effective management of teams can be difficult. In order to understand the challenges of multidisciplinary product development, this study focuses on student design teams conducting engineering design projects at RIT. This study utilizes a modified team effectiveness model based on existing literature for identifying hypothesized associations using a limited number of teams enrolled in senior design. It proposes an experimental protocol for conducting this study at a larger scale and identifies the appropriate tools needed to measure team constructs. The study provides experimental techniques to collect team characteristic data and it also develops techniques to quantify the design process. This study concludes that the experimental protocol is feasible, but that the use of latent semantic analysis is not a feasible approach to measure team mental models at the scale of the size of the MSD program. In addition, a novel method to measure product development project outcomes is proposed that is based on Axiomatic Design principles. Finally, a preliminary assessment of the expected associations suggests that five out of eight propositions behave as predicted by the team effectiveness model; however, the number of project teams used in the study are too small for these results to be conclusive

A Machine Learning Approach for Plagiarism Detection

Plagiarism detection is gaining increasing importance due to requirements for integrity in education. The existing research has investigated the problem of plagrarim detection with a varying degree of success. The literature revealed that there are two main methods for detecting plagiarism, namely extrinsic and intrinsic. This thesis has developed two novel approaches to address both of these methods. Firstly a novel extrinsic method for detecting plagiarism is proposed. The method is based on four well-known techniques namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The LSA application was fine-tuned to take in the stylometric features (most common words) in order to characterise the document authorship as described in chapter 4. The results revealed that LSA based stylometry has outperformed the traditional LSA application. Support vector machine based algorithms were used to perform the classification procedure in order to predict which author has written a particular book being tested. The proposed method has successfully addressed the limitations of semantic characteristics and identified the document source by assigning the book being tested to the right author in most cases. Secondly, the intrinsic detection method has relied on the use of the statistical properties of the most common words. LSA was applied in this method to a group of most common words (MCWs) to extract their usage patterns based on the transitivity property of LSA. The feature sets of the intrinsic model were based on the frequency of the most common words, their relative frequencies in series, and the deviation of these frequencies across all books for a particular author. The Intrinsic method aims to generate a model of author “style” by revealing a set of certain features of authorship. The model’s generation procedure focuses on just one author as an attempt to summarise aspects of an author’s style in a definitive and clear-cut manner. The thesis has also proposed a novel experimental methodology for testing the performance of both extrinsic and intrinsic methods for plagiarism detection. This methodology relies upon the CEN (Corpus of English Novels) training dataset, but divides that dataset up into training and test datasets in a novel manner. Both approaches have been evaluated using the well-known leave-one-out-cross-validation method. Results indicated that by integrating deep analysis (LSA) and Stylometric analysis, hidden changes can be identified whether or not a reference collection exists

Hierarchical Classification and its Application in University Search

Web search engines have been adopted by most universities for searching webpages in their own domains. Basically, a user sends keywords to the search engine and the search engine returns a flat ranked list of webpages. However, in university search, user queries are usually related to topics. Simple keyword queries are often insufficient to express topics as keywords. On the other hand, most E-commerce sites allow users to browse and search products in various hierarchies. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined for university search engines. The main difficulty is to automatically classify and rank a massive number of webpages into the topic hierarchies for universities. In this thesis, we use machine learning and data mining techniques to build a novel hybrid search engine with integrated hierarchies for universities, called SEEU (Search Engine with hiErarchy for Universities). Firstly, we study the problem of effective hierarchical webpage classification. We develop a parallel webpage classification system based on Support Vector Machines. With extensive experiments on the well-known ODP (Open Directory Project) dataset, we empirically demonstrate that our hierarchical classification system is very effective and outperforms the traditional flat classification approaches significantly. Secondly, we study the problem of integrating hierarchical classification into the ranking system of keywords-based search engines. We propose a novel ranking framework, called ERIC (Enhanced Ranking by hIerarchical Classification), for search engines with hierarchies. Experimental results on four large-scale TREC (Text REtrieval Conference) web search datasets show that our ranking system with hierarchical classification outperforms the traditional flat keywords-based search methods significantly. Thirdly, we propose a novel active learning framework to improve the performance of hierarchical classification, which is important for ranking webpages in hierarchies. From our experiments on the benchmark text datasets, we find that our active learning framework can achieve good classification performance yet save a considerable number of labeling effort compared with the state-of-the-art active learning methods for hierarchical text classification. Fourthly, based on the proposed classification and ranking methods, we present a novel hierarchical classification framework for mining academic topics from university webpages. We build an academic topic hierarchy based on the commonly accepted Wikipedia academic disciplines. Based on this hierarchy, we train a hierarchical classifier and apply it to mine academic topics. According to our comprehensive analysis, the academic topics mined by our method are reasonable and consistent with the real-world topic distribution in universities. Finally, we combine all the proposed techniques together and implement the SEEU search engine. According to two usability studies conducted in the ECE and the CS departments at our university, SEEU is favored by the majority of participants. To conclude, the main contribution of this thesis is a novel search engine, called SEEU, for universities. We discuss the challenges toward building SEEU and propose effective machine learning and data mining methods to tackle them. With extensive experiments on well-known benchmark datasets and real-world university webpage datasets, we demonstrate that our system is very effective. In addition, two usability studies of SEEU in our university show that SEEU has a great promise for university search