The LAIR at East Texas A&M
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Bill Martin Jr.
No full textA black and white photograph of Bill Martin Jr. standing on a stage in front of a setpiece.https://lair.etamu.edu/scua-martin-photos/1006/thumbnail.jp
Bill Martin Jr. Portrait, Front
No full textThe front side of a black and white portrait of Bill Martin Jr.https://lair.etamu.edu/scua-martin-photos/1004/thumbnail.jp
Student Mindset Changes in a Flipped Class Environment: A Quasi-Experimental Study
No full textMany students enrolled as a STEM major do not complete their introductory courses. As the years go by, many things around us change except for the way students receive their lecture. The purpose of this study was to look to see if student\u27 s mindsets differ in a flipped calculus classroom compared to a CLEAR calculus classroom. The research hypothesis was that there is an improved mindset in a flipped classroom compared to the CLEAR calculus classroom while the null hypothesis was that there is no difference. Participants are calculus II students in a flipped calculus classroom as well as a CLEAR calculus classroom setting who volunteered to take a survey at the start and the end of the semester
Generative Poisoning Attacks on Neural Network Models in Autonomous Driving
No full textPoisoning attacks have been described as a grave danger to neural networks, they tend to change the complete meaning of the model and also define how the model interprets the data provided to it. Certain inputs are retrained after which poisoned data can be induced into the model. As the popularity of self-driving cars are growing exponentially, we need to look into every major flaw in the system and have security measures in place for the safety of the passengers. There have been limited studies on progressive poisoning attacks especially when it comes to autonomous driving vehicles. In this work, we first generate the poisoned data and also propose a generative method that will increase the generation of the poisoned data. This method of poisoning helps us determine the extent of inaccuracy and unexpected side effects when the poisoned data is fed to the model
A Comparison Study of Looping and Non-Looping Students in Reading from Third to Fourth Grades
No full textThe researcher conducted a quasi-experimental quantitative study to determine whether there was a significant difference in reading achievement between students who looped and students who did not loop. Looping is when a group of students stay with the same teacher and classmates for two or more consecutive years. The students in this study were from a charter school in a northeast suburb of Dallas, Texas. The goal of this study was to determine whether students in the looping classroom or non-looping classroom have a better chance for academic gain in reading as determined by the STAAR (State of Texas Assessment of Academic Readiness) Reading test. The researcher also sought to determine whether there was a difference in reading achievement by gender between students who looped and students who did not loop. Although the data did not show a statistically significant difference between the looping and non-looping classes nor between the males and females in looping or non-looping classes the researcher determined through the literature review that there are benefits to looping that non-looping students do not have to opportunity to experience
Predicting Financial Recession using Machine Learning
No full textFinancial decision plays an important role in the society and therefore, a crash in the market or a major economic recession can lead to social anxiety. Many statistical models have been developed to forecast the financial recession but there is always human input required which leads to human judgement and can eventually lead to wrong results. More recently, machine learning algorithms are built to predict the crisis of asset classes like currency and stocks. In this study, we take these models to one step further and try to predict the financial recession using machine learning classification algorithms for next six-months period. To ensure financial stability in the economy, policy makers using this model can intervene and adjust the monetary policy. Moreover, asset managers / investors can adjust their portfolio by increasing allocation in less risky assets (like cash and government bonds) and decreasing allocation in more risky assets (like stocks) if the crisis is imminent
Do Co-Requisite Developmental Mathematics Courses Increase Course Sequence Completion at a Community College?
No full textThe purpose of this study was to examine sequence completion rates and grade point average (GPA) of co-requisite developmental mathematics and non-co-requisite developmental mathematics courses GPA of the students enrolled in co-requisite developmental mathematics courses compared to students enrolled in non-co-requisite developmental mathematics courses. To test the specific hypotheses, the researcher employed a non-experimental, quantitative design and thereby utilizing chi-square test of independence. The findings for the first hypothesis showed varied sequence completion percentages, where 33.8% of students enrolled in non-co-requisite program completed their courses and 66.2% of students enrolled in co-requisite program completing their courses. A simple linear regression was used to compare the GPA of students upon sequence completion of the non-co-requisite courses and co-requisite courses. The course type did not explain a significant amount of variance in sequence completion GPA, which upheld the hypothesis. The hypothesis stated that no significant difference between non-co-requisite developmental mathematics sequence completion GPA compared to co-requisite developmental mathematics sequence completion GPA
The Effect of Filler Selection Methods on Lineup Fairness and Eyewitness Identification
No full textEyewitness identification researchers consider procedures utilized by police as system variables, which is due to the criminal justice system being capable of controlling which procedures are recommended as optimal for gathering eyewitness evidence. Lineups are commonly used to facilitate an eyewitness identification decision pertaining to a suspect. The method with which a lineup is constructed is an important system variable to investigate, considering lineup composition has been shown to affect eyewitness identification performance. Fair lineups are recommended, and produce the best eyewitness identification accuracy (Wells, 1993; Wetmore et al., 2015, 2016). However, which method for creating fair lineups that best enhance eyewitness identification performance remains unclear. Filler selection methods may lead to differences in eyewitness ability to differentiate between innocent and guilty suspects, and are in need of further investigation, as some evidence has supported an advantage for matching fillers to the description of the suspect over matching them to the suspect’s appearance (e.g., Carlson et al., 2019; Wells, Rydell, & Seelau, 1993). In the current study, I investigated the effects of filler selection method on eyewitness identification, and I sought to determine whetherfiller selection methods produce differing estimates of lineup fairness. Participants took part in a multi-block face recognition paradigm consisting of multiple targets presented during encoding followed by various target-present and target-absent lineup tasks created using differing filler selection methods. Target-matched lineups were compared to four description-matched lineups differing in description quality: (a) a vague description using general characteristics, (b) with additional internal facial features, (c) with additional external facial features, or (d) with a mixture of both internal and external facial features. I hypothesized an increase in discriminability for description-matched compared to target-matched lineups regardless of the quality of description (Carlson et al., 2019; Wells et al., 1993; Wixted & Mickes, 2014), but I failed to find a difference between these two filler-selection methods. Matching fillers to internal and external features increased discriminability relative to matching fillers to no internal information. I also found that lineups with the best discriminability produced the lowest lineup fairness estimates. Further research is needed to determine the effect of filler-selection, feature type, and lineup fairness on eyewitness ID performance
Middle School Principals and Family Engagement: A Multifaceted Three-Dimensional Narrative Inquiry
No full textAt the local level of the problem, the District (pseudonym) had three middle school campuses with Title I funding status. There was little or no research related to middle school principals’ perceptions of family engagement. The purpose of this multifaceted three-dimensional narrative inquiry was to explore middle school principals’ perceptions of their experiences with family engagement within the professional knowledge landscape of leading a middle school within a predominantly African American setting. Three principals were recruited to share their narratives about leading urban middle schools not rated as high-performing and serving predominantly African American students. By conducting a multifaceted three-dimensional narrative inquiry, a thorough understanding of middle school administrators’ backward, forward, inward, outward, and situated in place experiences and perspectives was documented. This study aligned directly with Epstein’s overlapping spheres of influence that formed the conceptual framework
Gaze Based Mind Wandering Detection Using Deep Learning
No full textMind wandering (MW) is a phenomenon where a person shifts their attention from task-related to task-unrelated information. Mind Wandering is an omnipresent phenomenon for human beings. The consequence of mind wandering can impact a person’s performance negatively. Reorienting the mind’s attention using technology shows great promise to improve the performance and productivity of people in learning or other performative tasks. In this research, we investigate 62 eye gaze features by dividing them into four sets of global features: eye movement descriptive features, pupil diameter descriptive features, blink features, and miscellaneous features to detect mind wandering during reading from a computer interface. Our dataset, which was collected from a previous study, contains a mind wandering report where 135 participants were recorded “mind wandering” or “not mind wandering” using self-reporting during a computerized reading task. During this process, a remotely placed eye tracker tool recorded eye gaze data. Models were created using six supervised conventional machine learning (ML) algorithms: logistic regression, k-nearest neighbors (k-NN), support vector machine (SVM), decision tree, random forest and naive Bayes. Machine learning models were trained on eye gaze dataset and evaluated using 5-fold cross validation. We measured the performance using area under the receiver operating characteristics (AUC-ROC) score, AUC-ROC curve, and confusion-matrix. To further improve the AUC-ROC score and other evaluation metrics, we trained standard neural networks and deep learning models using the data. Four sets of deep learning architectures were trained and evaluated. We found that dense neural network with one dimensional convolutional layer (DNN+Conv1D) outperformed the performance of conventional machine learning models. Naïve Bayes achieved mean test AUC-ROC score of 0.6595 and mean test accuracy of 0.6416. DNN+Conv1D beat the AUC-ROC score and achieved a score of 0.8024 and mean test accuracy of 0.7278. Our implementation used missing data values rather than discarding them which in fact improved our results. Our findings also showed that an automated mind wandering detection using deep learning models generalize well for new participants. This finding may help laboratory studies of mind wandering and for building systems to detect attention of inattentive drivers, students, or people in other work contexts that need focus to improve performance on a task