The Effects of Metacognitive Training on Algebra Students’ Calibration Accuracy, Achievement, and Mathematical Literacy

This dissertation describes an empirical study that investigated how metacognitive training influenced lower achieving Algebra students’ calibration accuracy, achievement, and development of mathematics literacy. Multiple methods were used to collect and analyze the data. Close analysis of students’ work and classroom observations revealed that students that were exposed to the metacognitive training had significantly higher prediction accuracy and made gains in their understanding of the mathematics word problems than did students who did not receive the metacognitive training. Overall, however, both the intervention and comparison groups improved in their academic performance and became more mathematically literate and accurate in their metacognitive judgments. These findings suggested that explicit instruction of self-regulation strategies was beneficial for improving metacognitive judgments among lower achieving Algebra students in this study. Results further suggest that the problem-solving strategy enhanced mathematics learning for both groups. Further research is warranted to better understand students’ metacognitions as they engage in the problem-solving process

The Effects of Self-Regulation Strategies on Middle School Students\u27 Calibration Accuracy and Achievement

This study investigated the impact that self-regulation strategies have on metacognitive judgements (calibration) and mathematics achievement of typical and advanced achieving 7th grade mathematics students over a period of seven weeks. Self-regulation strategies, four square graphic organizers and vocabulary games were implemented with the treatment condition while online games were implemented with the control condition. The results revealed that participants in the treatment condition were more accurate in their calibrations than participants in the control condition, more specifically for postdiction accuracy. Although the participants in the treatment condition scored higher on their achievement tests than the participants in the control condition, there were no significant differences between the conditions

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Collaborative Research in Higher Education

Finding your groove in a collaborative research group in higher education can be an obstacle for some. However, when relationships are nurtured, roles and responsibilities are defined, and clear communication is practiced by all in the group, a collaborative research group can be successful and a way to support your research endeavors. Three colleagues will discuss how they make their collaborative research group work effectively and provide templates to help along the way

Professional Learning for Interested Faculty When Initially Using Mixed Reality Simulations

Over the last decade, teacher preparation programs have turned to mixed reality simulation to offer support to teacher candidates. However, teacher education faculty can be apprehensive about the use of the technology. This study provides the beginning stages of providing professional learning to faculty interested in using the technology to support their students\u27 learning in teacher education. Data indicated that the professional learning was welcomed by faculty involved and felt that the targeted one-on-one professional learning allowed the faculty member to be more open to trying a new technology that would have been daunting if tried in a siloed environment

Cone Beam CT Based Delta-Radiomics of Prostate Cancer Patients and Their Correlation to Quality of Life, Genitourinary and Gastrointestinal Toxicities

For patients receiving external beam radiotherapy (EBRT) for prostate cancer (PCa), acute and late radiation-related toxicities can significantly impact short and long-term quality-of-life (QOL). Radiomics, an analytic technique that extracts patterns from medical imaging, has been incorporated into predictive models in diverse cancers. Daily cone beam CT (CBCT) images during EBRT allow for the study of changes in radiomics over time, known as delta-radiomics. We hypothesize that delta-radiomics of the prostate extracted from daily CBCTs could be predictive of genitourinary toxicity (GUT), gastrointestinal toxicity (GIT), and patient QOL. From an IRB approved database, 31 PCa patients were identified who received EBRT with daily CBCTs. Patients were treated on dose fractionation schemes of 76-91 Gy in 37-40 fractions and some received a 12 Gy boost. The prostate was contoured on all daily CBCTs and 42 radiomics features were extracted for analysis. Radiomic features included intensity-based, gray-level run length (GLRLM), gray-level co-occurrence (GLCM), neighborhood gray-level difference (NGTDM), and gray-level size zone (GLSZM) matrix classes. These radiomic features were averaged per week to decrease data granularity. Patient outcomes analyzed included the total International Prostate Symptom QOL score GUT, GIT, combined GU/GIT grades. Logistic and linear regression models were used to estimate effects of radiomics features on later occurring toxicity and QOL, respectively. Odds ratios (ORs), regression coefficients, 95% confidence intervals, and P-value were estimated. Area under the receiver operating characteristic curve (AUC) and R2 were obtained as a measure of models’ goodness-of-fit. The statistical significance threshold was P < 1.5e-04 using an alpha level of 0.05 and applying the Bonferroni correction for multiple comparisons. All tests were two-sided. Parallel data sets were generated using standard (sCBCT) and iterative (iCBCT) CBCT reconstructions. Radiomic feature were preprocessed using Lloyd quantization with Collewet normalization. Five CBCT delta-radiomic features significantly correlated on week two with QOL occurring after. The results are summarized in Table 1. The R2 values indicate a moderate effect size. GUT and GIT were not found to correlate with the radiomic features. All significant radiomic features corresponded to sCBCT. Future short-term changes to QOL were detected as early as week two with CBCT delta-radiomics. However, the GUT, GIT, and GU/GIT toxicities were not predicted. These results can serve as preselection criteria of radiomic features to develop more sophisticated machine learning models with larger sample and improve statistical power

Increasing the efficiency of cone-beam CT based delta-radiomics using automated contours to predict radiotherapy-related toxicities in prostate cancer

Extracting longitudinal image quantitative data, known as delta-radiomics, has the potential to capture changes in a patient's anatomy throughout the course of radiation treatment for prostate cancer. Some of the major challenges of delta-radiomics studies are contouring the structures for individual fractions and accruing patients' data in an efficient manner. The manual contouring process is often time consuming and would limit the efficiency of accruing larger sample sizes for future studies. The problem is amplified because the contours are often made by highly trained radiation oncologists with limited time to dedicate to research studies of this nature. This work compares the use of automated prostate contours generated using a deformable image-based algorithm to make predictive models of genitourinary and changes in total international prostate symptom score in comparison to manually contours for a cohort of fifty patients. Area under the curve of manual and automated models were compared using the Delong test. This study demonstrated that the delta-radiomics models were similar for both automated and manual delta-radiomics models

Cone-beam CT delta-radiomics to predict genitourinary toxicities and international prostate symptom of prostate cancer patients: a pilot study

For prostate cancer (PCa) patients treated with definitive radiotherapy (RT), acute and late RT-related genitourinary (GU) toxicities adversely impact disease-specific quality of life. Early warning of potential RT toxicities can prompt interventions that may prevent or mitigate future adverse events. During intensity modulated RT (IMRT) of PCa, daily cone-beam computed tomography (CBCT) images are used to improve treatment accuracy through image guidance. This work investigated the performance of CBCT-based delta-radiomic features (DRF) models to predict acute and sub-acute International Prostate Symptom Scores (IPSS) and Common Terminology Criteria for Adverse Events (CTCAE) version 5 GU toxicity grades for 50 PCa patients treated with definitive RT. Delta-radiomics models were built using logistic regression, random forest for feature selection, and a 1000 iteration bootstrapping leave one analysis for cross validation. To our knowledge, no prior studies of PCa have used DRF models based on daily CBCT images. AUC of 0.83 for IPSS and greater than 0.7 for CTCAE grades were achieved as early as week 1 of treatment. DRF extracted from CBCT images showed promise for the development of models predictive of RT outcomes. Future studies will include using artificial intelligence and machine learning to expand CBCT sample sizes available for radiomics analysis