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1. Virtual Intensive Training for Experimental Centric Pedagogy Team Members: Effectiveness During COVID-19 Pandemic

   Owolabi, O. A. ( July 2021 , 2021 ASEE Virtual Annual Conference Content Access)

   null (Ed.)

   The COVID-19 pandemic grounded the implementation of many research projects. However, with the intervention of the NSF research grant awarded to a Historically Black College and University (HBCU), with a specific goal to increase students’ achievement in multiple STEM disciplines, the pandemic challenges provided opportunities to effectively achieve the project objectives. The Adapting an Experiment-centric Teaching Approach to Increase Student Achievement in Multiple STEM Disciplines (ETA-STEM) project aims to implement an evidence-based, experiment-focused teaching approach called Experimental Centric Pedagogy (ECP) in multiple STEM disciplines. The ECP has been shown to motivate students and increase the academic success of minority students in electrical engineering in various institutions. During the Summer of 2020, the ETA-STEM Trainees engaged in research activities to develop three instruments in their respective disciplines. This paper highlights the strategic planning of the project management team, the implementation of the ECP, a comprehensive breakdown of activities and an evaluation of effectiveness of the virtual training. The 13-week intensive virtual training using Canvas learning management system and zoom virtual platform provided the opportunity to effectively interact and collaborate with project team members. Some of the summer training activities and topics included: instrumentation and measurements in STEM fields, sensors and signal conditioning, assessing the performance of instruments and sensors, effective library and literature search, introduction to education research, writing excellent scientific papers, as well as the implementation and development of ECP curriculum with focus on home-based experiment. Prior to the training, ECP kits were shipped to the team and facilitators fully utilized the virtual platform to collaborate with team members. Overall, there was a great satisfaction and confidence with the participants designing three home-based experiments using the M1K and M2K analog devices. 

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2. Scholars From Underrepresented Groups in Engineering and the Social Sciences (SURGE) Capacity in Disasters: The Benefits and Challenges of Mentoring for Racial and Ethnic Minority Graduate Students

   Villarreal, Melissa ; Campbell, Nnenia ( January 2023 , Higher learning research communications)

   Objective: The purpose of this study was to evaluate the mentoring program of the Minority Scholars from Under-Represented Groups in Engineering and the Social Sciences (SURGE) Capacity in Disasters initiative, a pilot program that aimed to address the challenges that graduate students of color face in academic programs. SURGE promotes mentoring and professional development through its mentoring program for Science, Technology, Engineering, and Mathematics (STEM) students. Methods: Data collection involved distributing online surveys designed in Qualtrics to mentors and mentees five months after the SURGE program’s initiation. Separate surveys were created for student mentees and faculty mentors in order to collect feedback about the mentoring program. Mentees and mentors were also asked to rate their satisfaction with the specific individuals in their mentoring network so that the evaluation team could identify issues that arose across participants. Results: We found that students had several motivations for and expectations from SURGE. A majority of the students found the SURGE mentoring program to have been at least somewhat valuable in helping them achieve these expectations. Nonetheless, students did identify a few challenges, namely lack of swift responsiveness from some mentors, not enough guidance on navigating the mentor-mentee relationship, and little to no in-person interaction. While half of the students mentioned that some individuals within their mentoring team were hard to reach, a majority remained satisfied with the overall responsiveness of their mentors. This suggests that the many-to-many mentoring model helped to ensure none were entirely dissatisfied on this measure. Conclusions: These findings support previous research and show promise for mentoring as an effective intervention to the challenges that underrepresented students face in their academic programs and for their retention and representation, particularly in hazards and disaster-related fields. 

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3. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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4. A Diversity Index to assess college engineering team performance

   Rodriguez, Joaquin ; Dukes, April ; Keith, John A. ( January 2022 , 2022 ASEE - North Central Section Conference)

   A Diversity Index (DI) was developed to quantify eight minority categories (“Women”, “Non Male/Female”, “Afro-American”, “Hispanic”, “Asian/other ethnicity” “LGBQT”, “Disabilities”, and “First Generation”) in contrast to the standard “White American male”. This index is compared with a Minority Index (MI) based only on the ratio of “Non White American male” to the total of group members, which exhibits poor representation for diversity when teams are heavily conformed by minority representatives. In addition, the Diversity Index includes a tuning parameter to adjust for the impact of multiple diversities on the same individual. The Diversity Index has been calculated for four junior courses on Reactive Process Engineering and four senior capstone courses on Process Control and Process Design during the last three years (2019-21). Each course included at least two semester-long projects for 4-6 member teams. The Diversity Index was used to assess the performance of 69 self-selected teams, performing 37 technical projects and 101 outreach projects total. Assessments included relations with grades, peer-grading, team experience, and scope of activities. The analysis provides a quantitative approach to the impact of diversity on team performance. Reliability on some data is still difficult to validate. This study has relied mainly on the instructor interactions with students. In order to protect the students’ personal information, the proposed Diversity Index outputs a quantitative value without exposing the diversity source, and thus promoting more honest, secure and respectful participation. A new step is in progress to offer a “diversity rewarded” option to motivate students to select team members providing for larger inclusion and diversity. 

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5. Diversity, inclusion and equity in the engineering curriculum: Evaluating the efficacy of a new teaching module, was published in Advances in Engineering Education, Vol 11:3, 2023 (Available at https://advances.asee.org/wp-content/uploads/vol11/Issue3/4_11.3_Sabat.pdf)

   Sabat, Isacc ; Nault, Evan ; Fortney, Susan ; Peterson, Martin ; and Banerjee, Debjyoti ( July 2023 , Advances in engineering education)

   Matusovich, Holly (Ed.)

   Current diversity and sexual harassment trainings often take an informative approach that results in a gap between theoretical knowledge and practical resolutions of ethical dilemmas. Available research suggests that diversity training programs often elicit the greatest amount of change among people with a minority identity and can result in hostility from majority group members. To better prepare all engineering students for modern-day organizations, it’s imperative that universities develop effective approaches at the student level to mitigate these discrepancies. We propose that our novel ethics-based training will elicit positive diversity-related outcomes overall. We inductively explore the differential impact of diversity training across majority and minority identities. Longitudinal quantitative data were collected to examine changes in participant attitudes and behaviors in response to the diversity module. Undergraduate engineering students enrolled in an introductory engineering ethics course at a large Southwestern university were presented with a week-long teaching module on diversity. Survey results were evaluated to measure differences in effectiveness among majority and minority students. The diversity training successfully decreased levels of sexism, acceptance of sexual harassment myths, and increased intentions to confront discrimination. Differences in outcome variables between majority and minority members were found with regard to political orientation, race, and physical and mental disability status. Overall, this study presents a promising new avenue for diversity training scholarship. Specifically, we find that an ethics-based approach to diversity training may be particularly effective for majority group students. 

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