M.A.(Psychology) / Please refer to full text to view abstract

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The primary purpose of the project is to develop a web site where students can practice entry level mathematics questions.

The objective of the present project was to propose a new methodology for measuring misconceptions and subskills simultaneously using diagnostic information available from incorrect alternatives in multiple-choice tests designed for that purpose. Misconceptions are systematic and persistent errors that represent a learned intentional incorrect response (Brown & VanLehn, 1980; Ozkan & Ozkan, 2012). In prior research, Lee and Corter (2011) found that classification accuracy for their Bayesian Network misconception diagnosis models improved when latent higher-order subskills and specific wrong answers were included. Here, these contributions are adapted to a cognitively diagnostic measurement approach using the multiple-choice Deterministic Inputs Noisy “And” Gate (MC-DINA) model, first developed by de la Torre (2009b), by specifying dependencies between attributes to measure latent misconceptions and subskills simultaneously. A simulation study was conducted employing the proposed methodology (referred to as MC-DINA-H) across sample sizes (500, 1000, 2,000, and 5,000 examinees) and test lengths (15, 30, and 60 items) conditions. Eight attributes (4 misconceptions and 4 subskills) were included in the main simulation study. Attribute classification accuracy of the MC-DINA-H was compared to four less complex models and was found to more accurately classify attributes only when the attributes were relatively frequently required by multiple-choice options in the diagnostic assessment. The findings suggest that each attribute should be required by at least 15-20 percent of options in the diagnostic assessment.

This thesis presents an overview and analysis of tests conducted to measure the quality of clerical and secreterial skills obtained by commerce education students and concludes with suggestions for future testing.

Determining which students are at-risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of both research and practice in K-12 education. The models produced from this type of predictive modeling research are increasingly used by high schools in Early Warning Systems to identify which students are at risk and intervene to support better outcomes. It has become common practice to re-build and validate these detectors, district-by-district, due to different data semantics and various risk factors for students in different districts. As these detectors become more widely used, however, a new challenge emerges in applying these detectors across a broad spectrum of school districts with varying availability of past student data. Some districts have insufficient high-quality past data for building an effective detector. Novel approaches that can address the complex data challenges a new district presents are critical for advancing the field. Using an ensemble-based algorithm, I develop a modeling approach that can generate a useful model for a previously unseen district. During the ensembling process, my approach, District Similarity Ensemble Extrapolation (DSEE), weights districts that are more similar to the Target district more strongly during ensembling than less similar districts. Using this approach, I can predict student-at-risk status effectively for unseen districts, across a range of grade ranges, and achieve prediction goodness but ultimately fails to perform better than the previously published Knowles (2015) and Bowers (2012) EWS models proposed for use across districts.

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Postpartum depression (PPD) refers to the onset of depressive symptoms anytime within first year following the birth of a child. PPD affects approximately 10-20% of new mothers and often goes underdiagnosed and untreated. Left untreated, PPD can predispose women to more severe and frequent future depressive episodes. Literature suggests depression in mothers may have long-term negative effects on infants’ and children’s psychosocial development. The American Academy of Pediatrics (AAP) has identified pediatric primary care as the ideal location to screen and refer mothers possibly suffering from depression. Routinely assessing PPD in mothers during well-child checks is not only recommended, it is increasingly being considered a best practice standard. The AAP calls for further research to improve the feasibility of assessing and treating PPD in mothers in pediatric primary care. This paper is part of a larger study that will be evaluating the effectiveness of screening new mothers for PPD in pediatric primary care settings and providing a brief same day interventions. The current aims of this portion of the study will be to evaluate 1) referral results, and 2) mothers’ level of satisfaction with the protocol. Research assistants (RA) will approach mothers of infants, birth to 6 months of age in the waiting rooms of ETSU Pediatrics in Johnson City, TN. Mothers will be given a brief description of PPD, the study and will be asked to participate by signing a voluntary informed consent document. As part of the visit, nurses will distribute and score the Edinburg Postpartum Depression Scale (EPDS). Mothers scoring 9 or above will receive a brief educational brochure about PPD, a brief intervention and a one week follow-up phone call with an onsite behavioral health consultant (BCH) or social worker (SW). At mothers’ discretion, an appropriate outside referral to preferred provider will be made, if necessary. Approximately two weeks post- intervention, a satisfaction survey by phone will be administered by RAs. The survey will examine referral results (e.g., of high scores, what recommendations were made, did mothers follow through, treatments received, was there improvement in EPDS score) and the mothers’ level of satisfaction with the protocol (e.g., satisfaction with how protocol was handled by staff and how well mothers felt their needs were addressed). Satisfaction will be noted on a likert-scale ranging from 0 (no satisfaction) to 10 (very satisfied). Data is pending and collection will start during the first week of March showing EDPS uptake, referral results and mother satisfaction. Data is expected for approximately 60-100 new mothers.