Personalized brain stimulation for effective neurointervention across participants

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

CTE Teacher Licensure and Long-Term Student Outcomes

We use longitudinal data from Massachusetts that link high school course-taking records in career and technical education (CTE) to postsecondary student outcomes to provide the first empirical evidence linking characteristics of CTE teachers to later student outcomes. We find that CTE teachers who received better scores on subject performance tests required for licensure tend to have students with higher longer-term earnings than CTE teachers who received lower scores on these tests, controlling for other factors. Specifically, we estimate that a 1 standard deviation increase in teacher performance on these tests is associated with about a $1,000 increase in average expected earnings for the teacher's students 5 years after their expected graduation date, controlling for licensure test area and observable differences between students.

Personalized Closed-Loop Brain Stimulation for Effective Neurointervention Across Participants

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, stimulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

The Effect of Integrative Pedagogy on Academic Achievements of Chemistry Students

The study in hand aimed to determine the effects of integrative pedagogy over the academic performance of secondary school students in the subject of Chemistry. The study was conducted in an experimental framework following the Solomon Four Group Design. A total sample of 120 students of 10th class was randomly taken from 4 private sector Secondary Schools of Khyber Pakhtunkhwa, Pakistan. The sample was divided into 4 groups, each having 30 students. Giving a randomized treatment to the groups, two groups were taken as experimental while two were taken as controlled. One experimental and one controlled group were pre-tested for Chemistry subject performance through objective achievement tests, while others were not pre-tested. Both experimental groups were taught through integrative pedagogy while the controlled groups were taught through the traditional method for three months. After regular monthly post-testing, the triplicate data revealed an 11-point average increase in the academic performance of chemistry students in both the experimental groups in comparison to the controlled groups.

A-247 Overall Test Battery Means from ANAM4 TBI-MIL and D-KEFS: A Within-Subject Comparison of a Traumatic Brain Injury Cognition Composite Score

Objective Several cognition composite scores have been developed for potential use in traumatic brain injury clinical trials. This analysis examined the equivalence of overall test battery mean (OTBM) from two different test batteries administered consecutively to the same subjects. Methods Soldiers were administered the Automated Neuropsychological Assessment Metrics (version 4) TBI-MIL (ANAM4) computerized battery and D-KEFS as part of a larger study comparing within-subject performance from different neuropsychological test batteries. Data from 121 soldiers with complete and valid data on both ANAM4 and D-KEFS and no recent TBI were used in this analysis. OTBMs were calculated for ANAM4 and the seven D-KEFS achievement scores. The OTBMs from the 121 soldiers were ranked from lowest to highest and the percentile rankings from each battery were compared. For each soldier, the differences between the percentile ranks from each battery were also calculated. Results Only 53.8% of soldiers who scored below the 20th percentile on ANAM4 also scored below the 20th percentile on D-KEFS. Furthermore, only 47.8% of soldiers who scored at or above the 80th percentile on ANAM4 also scored in that range on D-KEFS. Some soldiers’ performance on each battery diverged by large amounts, for example from 59.5 to 82.6 percentage points. Correlation analysis revealed that the OTBMs and percentile rankings from both batteries were modestly correlated (OTBM r = 0.515, p < 0.001, percentile rank r = 0.499, p < 0.001). Conclusion These results suggest that comparing similar cognition composites from different neuropsychological test batteries from different studies in a meta-analytic manner may not be feasible due to psychometric difference between batteries.

Development of Classification Algorithms for the Detection of Postures Using Non-Marker-Based Motion Capture Systems

The rapid development of algorithms for skeletal postural detection with relatively inexpensive contactless systems and cameras opens up the possibility of monitoring and assessing the health and wellbeing of humans. However, the evaluation and confirmation of posture classifications are still needed. The purpose of this study was therefore to develop a simple algorithm for the automatic classification of human posture detection. The most affordable solution for this project was through using a Kinect V2, enabling the identification of 25 joints, so as to record movements and postures for data analysis. A total of 10 subjects volunteered for this study. Three algorithms were developed for the classification of different postures in Matlab. These were based on a total error of vector lengths, a total error of angles, multiplication of these two parameters and the simultaneous analysis of the first and second parameters. A base of 13 exercises was then created to test the recognition of postures by the algorithm and analyze subject performance. The best results for posture classification were shown by the second algorithm, with an accuracy of 94.9%. The average degree of correctness of the exercises among the 10 participants was 94.2% (SD1.8%). It was shown that the proposed algorithms provide the same accuracy as that obtained from machine learning-based algorithms and algorithms with neural networks, but have less computational complexity and do not need resources for training. The algorithms developed and evaluated in this study have demonstrated a reasonable level of accuracy, and could potentially form the basis for developing a low-cost system for the remote monitoring of humans.

Multiplexing of EEG signatures for temporal and spatial distance estimates

The perception and measurement of spatial and temporal dimensions have been widely studied. However, whether these two dimensions are processed independently is still being debated. Additionally, whether EEG components are uniquely associated with time or space, or whether they reflects a more general measure of magnitude remains unknown. While undergoing EEG, subjects traveled a randomly predetermined spatial or temporal interval and were then instructed to reproduce the interval traveled. In the task, the subject’s travel speed varied for the estimation and reproduction phases of each trial, so that one dimension could not inform the other. Behaviorally, subject performance was more variable when reproducing time than space, but overall, just as accurate; notably, behavior was not correlated between tasks. EEG data revealed during estimation the contingent negative variation (CNV) tracked the probability of the upcoming interval, regardless of dimension. However, during reproduction, the CNV exclusively oriented to the upcoming temporal interval at the start of reproduction. Further, a dissociation between relatively early frontal beta and late posterior alpha oscillations was observed for time and space reproduction, respectively. Our findings indicate that time and space are neurally separable dimensions, yet are hierarchically organized across task contexts within the CNV signal.

Using the Angoff method to set a standard on mock exams for the Korean Nursing Licensing Examination

Purpose: This study explored the possibility of using the Angoff method, in which panel experts determine the cut score of an exam, for the Korean Nursing Licensing Examination (KNLE). Two mock exams for the KNLE were analyzed. The Angoff standard setting procedure was conducted and the results were analyzed. We also aimed to examine the procedural validity of applying the Angoff method in this context.Methods: For both mock exams, we set a pass-fail cut score using the Angoff method. The standard setting panel consisted of 16 nursing professors. After the Angoff procedure, the procedural validity of establishing the standard was evaluated by investigating the responses of the standard setters.Results: The descriptions of the minimally competent person for the KNLE were presented at the levels of general and subject performance. The cut scores of first and second mock exams were 74.4 and 76.8, respectively. These were higher than the traditional cut score (60% of the total score of the KNLE). The panel survey showed very positive responses, with scores higher than 4 out of 5 points on a Likert scale.Conclusion: The scores calculated for both mock tests were similar, and were much higher than the existing cut scores. In the second simulation, the standard deviation of the Angoff rating was lower than in the first simulation. According to the survey results, procedural validity was acceptable, as shown by a high level of confidence. The results show that determining cut scores by an expert panel is an applicable method.

E’ntries All the Way using Online Reflective Journal Writing As Innovative Tool to Enhance Student Understanding and Performance in Ethics Courses for Information Age

This paper looks into the possible use of reflective journal writing assessments for teaching ethics for information age course to students from business and computing backgrounds in order to increase deeper learning and enhance subject performance. Although reflective journal writing has not been used previously as a learning tool to teach ethics, this paper compares the results from a sample population of more than 300 students across four semesters to a comparison group from previous semesters taught by the same instructor. Results highlight significant impact of using reflective journal writing on students’ understanding of ethics concepts through recorded increase in grades and reduction in fail rates