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1. Comparison of two alternative approaches to quality STEM teacher preparation: Fast‐track licensure and embedded residency programs

   https://doi.org/10.1111/ssm.12314  

   Mentzer, Gale A. ; Czerniak, Charlene M. ; Duckett, T. Ryan ( January 2019 , School Science and Mathematics)

   Alternative pathways to teaching licensure were developed to address teacher shortages. These programs differ widely, making it difficult to generalize the effects. This study compares the impact of two alternative licensure programs on the development of fundamental elements of science teacher preparation and persistence. The fast‐track programs include a 6‐month teacher preparation program and a one‐year residency teacher preparation program. The study concluded that licensure type was unassociated with the impact on teaching self‐efficacy, beliefs about teacher‐focused/student‐focused teaching, preferences for inquiry instructional practices, and experiences with student misbehavior. However, the study revealed that licensure type was associated with a number of other variables: residency students had more confidence in their ability to provide quality instruction; preferred inquiry‐based instruction more often; and may be better prepared for the high‐needs classroom. Those in the 6‐month program were more likely to score higher on practical versus theoretical approaches to teaching, and while they had a more realistic idea of how to measure success in the high‐needs classroom, the residency students had more knowledge of educational theory and how to apply it. Findings suggest that more traditionally licensed teachers may be more inclined to use inquiry‐based methods suggested in current reforms.

    

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2. The Impact of Forecast Inconsistency and Probabilistic Forecasts on Users’ Trust and Decision-Making

   https://doi.org/10.1175/WCAS-D-22-0064.1  

   Burgeno, Jessica N. ; Joslyn, Susan L. ( July 2023 , Weather, Climate, and Society)

   When forecasts for a major weather event begin days in advance, updates may be more accurate but inconsistent with the original forecast. Evidence suggests that resulting inconsistency may reduce user trust. However, adding an uncertainty estimate to the forecast may attenuate any loss of trust due to forecast inconsistency, as has been shown with forecast inaccuracy. To evaluate this hypothesis, this experiment tested the impact on trust of adding probabilistic snow-accumulation forecasts to single-value forecasts in a series of original and revised forecast pairs (based on historical records) that varied in both consistency and accuracy. Participants rated their trust in the forecasts and used them to make school-closure decisions. One-half of the participants received single-value forecasts, and one-half also received the probability of 6 in. or more (decision threshold in the assigned task). As with previous research, forecast inaccuracy was detrimental to trust, although probabilistic forecasts attenuated the effect. Moreover, the inclusion of probabilistic forecasts allowed participants to make economically better decisions. Surprisingly, in this study inconsistency increased rather than decreased trust, perhaps because it alerted participants to uncertainty and led them to make more cautious decisions. Furthermore, the positive effect of inconsistency on trust was enhanced by the inclusion of probabilistic forecast. This work has important implications for practical settings, suggesting that both probabilistic forecasts and forecast inconsistency provide useful information to decision-makers. Therefore, members of the public may benefit from well-calibrated uncertainty estimates and newer, more reliable information.

   The purpose of this study was to clarify how explicit uncertainty information and forecast inconsistency impact trust and decision-making in the context of sequential forecasts from the same source. This is important because trust is critical for effective risk communication. In the absence of trust, people may not use available information and subsequently may put themselves and others at greater-than necessary risk. Our results suggest that updating forecasts when newer, more reliable information is available and providing reliable uncertainty estimates can support user trust and decision-making.

    

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3. Lived Experiences of African American Engineering Students at a PWI Through the Lens of Navigational Capital

   Damas, S. ; Benson, L. ( February 2022 , 2022 CoNECD (Collaborative Network for Engineering & Computing Diversity))

   There are significant disparities between the conferring of science, technology, engineering, and mathematics (STEM) bachelor’s degrees to minoritized groups and the number of STEM faculty that represent minoritized groups at four-year predominantly White institutions (PWIs). Studies show that as of 2019, African American faculty at PWIs have increased by only 2.3% in the last 20 years. This study explores the ways in which this imbalance affects minoritized students in engineering majors. Our research objective is to describe the ways in which African American students navigate their way to success in an engineering program at a PWI where the minoritized faculty representation is less than 10%. In this study, we define success as completion of an undergraduate degree and matriculation into a Ph.D. program. Research shows that African American students struggle with feeling like the “outsider within” in graduate programs and that the engineering culture can permeate from undergraduate to graduate programs. We address our research objective by conducting interviews using navigational capital as our theoretical framework, which can be defined as resilience, academic invulnerability, and skills. These three concepts come together to denote the journey of an individual as they achieve success in an environment not created with them in mind. Navigational capital has been applied in education contexts to study minoritized groups, and specifically in engineering education to study the persistence of students of color. Research on navigational capital often focuses on how participants acquire resources from others. There is a limited focus on the experience of the student as the individual agent exercising their own navigational capital. Drawing from and adapting the framework of navigational capital, this study provides rich descriptions of the lived experiences of African American students in an engineering program at a PWI as they navigated their way to academic success in a system that was not designed with them in mind. This pilot study took place at a research-intensive, land grant PWI in the southeastern United States. We recruited two students who identify as African American and are in the first year of their Ph.D. program in an engineering major. Our interview protocol was adapted from a related study about student motivation, identity, and sense of belonging in engineering. After transcribing interviews with these participants, we began our qualitative analysis with a priori coding, drawing from the framework of navigational capital, to identify the experiences, connections, involvement, and resources the participants tapped into as they maneuvered their way to success in an undergraduate engineering program at a PWI. To identify other aspects of the participants’ experiences that were not reflected in that framework, we also used open coding. The results showed that the participants tapped into their navigational capital when they used experiences, connections, involvement, and resources to be resilient, academically invulnerable, and skillful. They learned from experiences (theirs or others’), capitalized on their connections, positioned themselves through involvement, and used their resources to achieve success in their engineering program. The participants identified their experiences, connections, and involvement. For example, one participant who came from a blended family (African American and White) drew from the experiences she had with her blended family. Her experiences helped her to understand the cultures of Black and White people. She was able to turn that into a skill to connect with others at her PWI. The point at which she took her familial experiences to use as a skill to maneuver her way to success at a PWI was an example of her navigational capital. Another participant capitalized on his connections to develop academic invulnerability. He was able to build his connections by making meaningful relationships with his classmates. He knew the importance of having reliable people to be there for him when he encountered a topic he did not understand. He cultivated an environment through relationships with classmates that set him up to achieve academic invulnerability in his classes. The participants spoke least about how they used their resources. The few mentions of resources were not distinct enough to make any substantial connection to the factors that denote navigational capital. The participants spoke explicitly about the PWI culture in their engineering department. From open coding, we identified the theme that participants did not expect to have role models in their major that looked like them and went into their undergraduate experience with the understanding that they will be the distinct minority in their classes. They did not make notable mention of how a lack of minority faculty affected their success. Upon acceptance, they took on the challenge of being a racial minority in exchange for a well-recognized degree they felt would have more value compared to engineering programs at other universities. They identified ways they maneuvered around their expectation that they would not have representative role models through their use of navigational capital. Integrating knowledge from the framework of navigational capital and its existing applications in engineering and education allows us the opportunity to learn from African American students that have succeeded in engineering programs with low minority faculty representation. The future directions of this work are to outline strategies that could enhance the path of minoritized engineering students towards success and to lay a foundation for understanding the use of navigational capital by minoritized students in engineering at PWIs. Students at PWIs can benefit from understanding their own navigational capital to help them identify ways to successfully navigate educational institutions. Students’ awareness of their capacity to maintain high levels of achievement, their connections to networks that facilitate navigation, and their ability to draw from experiences to enhance resilience provide them with the agency to unleash the invisible factors of their potential to be innovators in their collegiate and work environments. 

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4. Family planning providers and contraceptive users in Rwanda employ strategies to prevent discontinuation

   https://doi.org/10.1186/s12905-021-01503-1  

   Schwandt, Hilary ; Boulware, Angel ; Corey, Julia ; Herrera, Ana ; Hudler, Ethan ; Imbabazi, Claudette ; King, Ilia ; Linus, Jessica ; Manzi, Innocent ; Merritt, Madelyn ; et al ( October 2021 , BMC Women's Health)

   In Rwanda, nearly a third of contraceptive users discontinue within the first year of use. Family planning programs often focus more on recruitment of new users as opposed to maintaining use among current users. A focus on sustaining users and minimizing discontinuation is imperative for long-term family planning program success. This study explores the efforts providers and contraceptive users in Rwanda employ to prevent one of the greatest challenges to family planning programs: contraceptive discontinuation.

   This was a qualitative study conducted in Rwanda between February and July 2018. It included eight focus group discussions with 88 family planning providers and 32 in-depth interviews with experienced modern contraceptive users. Data were collected in two districts with the highest (Musanze) and lowest (Nyamasheke) rates of contraceptive use. Data were analyzed using thematic content approach.

   Family planning providers in this study used the following strategies to prevent discontinuation: counseling new users on the potential for side effects and switching, reminding clients about appointments for resupply, as well as supporting dissatisfied users by providing counseling, medicine for side effects, and discussing options for switching methods. Users, on the other hand, employed the following strategies to prevent discontinuation: having an understanding that experiences of side effects vary by individuals, supporting peers to sustain use, persisting with use despite experiences of side effects, and switching methods.

   The strategies used by family planning providers and users in Rwanda to prevent discontinuation suggest the possibility of long-term sustained use of contraception in the country. Harnessing and supporting such strategies could contribute to sustaining or improving further contraceptive use in the country.

    

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5. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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