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1. Intelligence Augmentation for Collaborative Learning

   Roschelle, Jeremy ( January 2021 , HCI International)

   Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps usmore »see the complexity of today’s classrooms and within this complexity, the opportunities for augmentation or “assistance to become important and even essential. An overarching design concept has emerged in the past 5 years in response to this complexity, the idea of intelligent augmentation for “orchestrating” classrooms (Dillenbourg, et al, 2013). As a metaphor, orchestration can suggest the need for a coordinated performance among many agents who are each playing different roles or voicing different ideas. Practically speaking, orchestration suggests that “intelligence augmentation” could help many smaller things go well, and in doing so, could enable the overall intention of the learning experience to succeed. Those smaller things could include helping the teacher stay aware of students or groups who need attention, supporting formation of groups or transitions from one activity to the next, facilitating productive social interactions in groups, suggesting learning resources that would support teamwork, and more. A recent panel of AI experts identified orchestration as an overarching concept that is an important focus for near-term research and development for intelligence augmentation (Roschelle, Lester & Fusco, 2020). Tackling this challenging area of collaborative learning could also be beneficial for advancing AI technologies overall. Building AI agents that better understand the social context of human activities has broad importance, as does designing AI agents that can appropriately interact within teamwork. Collaborative learning has trajectory over time, and designing AI systems that support teams not just with a short term recommendation or suggestion but in long-term developmental processes is important. Further, classrooms that are engaged in collaborative learning could become very interesting hybrid environments, with multiple human and AI agents present at once and addressing dual outcome goals of learning to collaborate and collaborating to learn; addressing a hybrid environment like this could lead to developing AI systems that more robustly help many types of realistic human activity. In conclusion, the opportunity to make a societal impact by attending to collaborative learning, the availability of growing science of computer-supported collaborative learning and the need to push new boundaries in AI together suggest collaborative learning as a challenge worth tackling in coming years.« less
2. 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 EEGmore »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.« less
3. Highlights and Outcomes of the 2021 Global Community Consultation

   https://doi.org/10.3897/biss.5.72716  

   Ellwood, Elizabeth R. ; Bentley, Andrew ; Buschbom, Jutta ; Hardisty, Alex ; Mast, Austin ; Miller, Joe ; Monfils, Anna ; Nelson, Gil ; Paul, Deborah L ( August 2021 , Biodiversity Information Science and Standards)

   International collaboration between collections, aggregators, and researchers within the biodiversity community and beyond is becoming increasingly important in our efforts to support biodiversity, conservation and the life of the planet. The social, technical, logistical and financial aspects of an equitable biodiversity data landscape – from workforce training and mobilization of linked specimen data, to data integration, use and publication – must be considered globally and within the context of a growing biodiversity crisis. In recent years, several initiatives have outlined paths forward that describe how digital versions of natural history specimens can be extended and linked with associated data. In the United States, Webster (2017) presented the “extended specimen”, which was expanded upon by Lendemer et al. (2019) through the work of the Biodiversity Collections Network (BCoN). At the same time, a “digital specimen” concept was developed by DiSSCo in Europe (Hardisty 2020). Both the extended and digital specimen concepts depict a digital proxy of an analog natural history specimen, whose digital nature provides greater capabilities such as being machine-processable, linkages with associated data, globally accessible information-rich biodiversity data, improved tracking, attribution and annotation, additional opportunities for data use and cross-disciplinary collaborations forming the basis for FAIR (Findable, Accessible, Interoperable,more »Reproducible) and equitable sharing of benefits worldwide, and innumerable other advantages, with slight variation in how an extended or digital specimen model would be executed. Recognizing the need to align the two closely-related concepts, and to provide a place for open discussion around various topics of the Digital Extended Specimen (DES; the current working name for the joined concepts), we initiated a virtual consultation on the discourse platform hosted by the Alliance for Biodiversity Knowledge through GBIF. This platform provided a forum for threaded discussions around topics related and relevant to the DES. The goals of the consultation align with the goals of the Alliance for Biodiversity Knowledge: expand participation in the process, build support for further collaboration, identify use cases, identify significant challenges and obstacles, and develop a comprehensive roadmap towards achieving the vision for a global specification for data integration. In early 2021, Phase 1 launched with five topics: Making FAIR data for specimens accessible; Extending, enriching and integrating data; Annotating specimens and other data; Data attribution; and Analyzing/mining specimen data for novel applications. This round of full discussion was productive and engaged dozens of contributors, with hundreds of posts and thousands of views. During Phase 1, several deeper, more technical, or additional topics of relevance were identified and formed the foundation for Phase 2 which began in May 2021 with the following topics: Robust access points and data infrastructure alignment; Persistent identifier (PID) scheme(s); Meeting legal/regulatory, ethical and sensitive data obligations; Workforce capacity development and inclusivity; Transactional mechanisms and provenance; and Partnerships to collaborate more effectively. In Phase 2 fruitful progress was made towards solutions to some of these complex functional and technical long-term goals. Simultaneously, our commitment to open participation was reinforced, through increased efforts to involve new voices from allied and complementary fields. Among a wealth of ideas expressed, the community highlighted the need for unambiguous persistent identifiers and a dedicated agent to assign them, support for a fully linked system that includes robust publishing mechanisms, strong support for social structures that build trustworthiness of the system, appropriate attribution of legacy and new work, a system that is inclusive, removed from colonial practices, and supportive of creative use of biodiversity data, building a truly global data infrastructure, balancing open access with legal obligations and ethical responsibilities, and the partnerships necessary for success. These two consultation periods, and the myriad activities surrounding the online discussion, produced a wide variety of perspectives, strategies, and approaches to converging the digital and extended specimen concepts, and progressing plans for the DES -- steps necessary to improve access to research-ready data to advance our understanding of the diversity and distribution of life. Discussions continue and we hope to include your contributions to the DES in future implementation plans.« less
4. ABET & Engineering Accreditation - History, Theory, Practice: Initial Findings from a National Study on the Governance of Engineering Education

   Akera, A. ( January 2019 , ASEE Annual Conference proceedings)

   Who and by what means do we ensure that engineering education evolves to meet the ever changing needs of our society? This and other papers presented by our research team at this conference offer our initial set of findings from an NSF sponsored collaborative study on engineering education reform. Organized around the notion of higher education governance and the practice of educational reform, our open-ended study is based on conducting semi-structured interviews at over three dozen universities and engineering professional societies and organizations, along with a handful of scholars engaged in engineering education research. Organized as a multi-site, multi-scale study, our goal is to document differences in perspectives and interest the exist across organizational levels and institutions, and to describe the coordination that occurs (or fails to occur) in engineering education given the distributed structure of the engineering profession. This paper offers for all engineering educators and administrators a qualitative and retrospective analysis of ABET EC 2000 and its implementation. The paper opens with a historical background on the Engineers Council for Professional Development (ECPD) and engineering accreditation; the rise of quantitative standards during the 1950s as a result of the push to implement an engineering science curriculum appropriate tomore »the Cold War era; EC 2000 and its call for greater emphasis on professional skill sets amidst concerns about US manufacturing productivity and national competitiveness; the development of outcomes assessment and its implementation; and the successive negotiations about assessment practice and the training of both of program evaluators and assessment coordinators for the degree programs undergoing evaluation. It was these negotiations and the evolving practice of assessment that resulted in the latest set of changes in ABET engineering accreditation criteria (“1-7” versus “a-k”). To provide an insight into the origins of EC 2000, the “Gang of Six,” consisting of a group of individuals loyal to ABET who used the pressure exerted by external organizations, along with a shared rhetoric of national competitiveness to forge a common vision organized around the expanded emphasis on professional skill sets. It was also significant that the Gang of Six was aware of the fact that the regional accreditation agencies were already contemplating a shift towards outcomes assessment; several also had a background in industrial engineering. However, this resulted in an assessment protocol for EC 2000 that remained ambiguous about whether the stated learning outcomes (Criterion 3) was something faculty had to demonstrate for all of their students, or whether EC 2000’s main emphasis was continuous improvement. When it proved difficult to demonstrate learning outcomes on the part of all students, ABET itself began to place greater emphasis on total quality management and continuous process improvement (TQM/CPI). This gave institutions an opening to begin using increasingly limited and proximate measures for the “a-k” student outcomes as evidence of effort and improvement. In what social scientific terms would be described as “tactical” resistance to perceived oppressive structures, this enabled ABET coordinators and the faculty in charge of degree programs, many of whom had their own internal improvement processes, to begin referring to the a-k criteria as “difficult to achieve” and “ambiguous,” which they sometimes were. Inconsistencies in evaluation outcomes enabled those most discontented with the a-k student outcomes to use ABET’s own organizational processes to drive the latest revisions to EAC accreditation criteria, although the organization’s own process for member and stakeholder input ultimately restored much of the professional skill sets found in the original EC 2000 criteria. Other refinements were also made to the standard, including a new emphasis on diversity. This said, many within our interview population believe that EC 2000 had already achieved much of the changes it set out to achieve, especially with regards to broader professional skills such as communication, teamwork, and design. Regular faculty review of curricula is now also a more routine part of the engineering education landscape. While programs vary in their engagement with ABET, there are many who are skeptical about whether the new criteria will produce further improvements to their programs, with many arguing that their own internal processes are now the primary drivers for change.« less
5. Building a Networked Improvement Community: Lessons in Organizing to Promote Diversity, Equity, and Inclusion in Science, Technology, Engineering, and Mathematics

   https://doi.org/10.3389/fpsyg.2021.732347  

   Noble, Chelsea E. ; Amey, Marilyn J. ; Colón, Luis A. ; Conroy, Jacqueline ; De Cheke Qualls, Anna ; Deonauth, Kamla ; Franke, Jeffrey ; Gardner, Alex ; Goldberg, Bennett ; Harding, Thelma ; et al ( November 2021 , Frontiers in Psychology)

   null (Ed.)

   In 2016, 10 universities launched a Networked Improvement Community (NIC) aimed at increasing the number of scholars from Alliances for Graduate Education and the Professoriate (AGEP) populations entering science, technology, engineering, and mathematics (STEM) faculty careers. NICs bring together stakeholders focused on a common goal to accelerate innovation through structured, ongoing intervention development, implementation, and refinement. We theorized a NIC organizational structure would aid understandings of a complex problem in different contexts and accelerate opportunities to develop and improve interventions to address the problem. A distinctive feature of this NIC is its diverse institutional composition of public and private, predominantly white institutions, a historically Black university, a Hispanic-serving institution, and land grant institutions located across eight states and Washington, DC, United States. NIC members hold different positions within their institutions and have access to varied levers of change. Among the many lessons learned through this community case study, analyzing and addressing failed strategies is as equally important to a healthy NIC as is sharing learning from successful interventions. We initially relied on pre-existing relationships and assumptions about how we would work together, rather than making explicit how the NIC would develop, establish norms, understand common processes, and manage changing relationships.more »We had varied understandings of the depth of campus differences, sometimes resulting in frustrations about the disparate progress on goals. NIC structures require significant engagement with the group, often more intensive than traditional multi-institution organizational structures. They require time to develop and ongoing maintenance in order to advance the work. We continue to reevaluate our model for leadership, climate, diversity, conflict resolution, engagement, decision-making, roles, and data, leading to increased investment in the success of all NIC institutions. Our NIC has evolved from the traditional NIC model to become the Center for the Integration of Research, Teaching and Learning (CIRTL) AGEP NIC model with five key characteristics: (1) A well-specified aim, (2) An understanding of systems, including a variety of contexts and different organizations, (3) A culture and practice of shared leadership and inclusivity, (4) The use of data reflecting different institutional contexts, and (5) The ability to accelerate infrastructure and interventions. We conclude with recommendations for those considering developing a NIC to promote diversity, equity, and inclusion efforts.« less