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1. (Re)Engaging Novice Online Learners in an Educational Programming Game

   Lee, Michael J ( January 2020 , Journal of computing sciences in colleges)

   Many people are learning programming on their own using various online resources such as educational games. Unfortunately, little is known about how to keep online educational game learners motivated throughout their game play, especially if they become disengaged or frustrated with their task. Keeping online learners engaged is essential for learning programming, as it may have lasting effects on their views and self-efficacy towards computer science. To address this issue, we created a coarse-grained frustration detector that provided users with customized, adaptive feedback to help (re)engage them with the game content. We ran a controlled experiment with 400 participants over the course of 1.5 months, with half of the players playing the original game, and the other half playing the game with the frustration detection and adaptive feed- back. We found that the users who received the adaptive feedback when frustrated completed more levels than their counterparts who did not receive this customized feedback. Based on these findings, we believe that adaptive feedback is essential in keeping educational game learners engaged, and propose future work for researchers and designers of online educational games to better support their users. 

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2. Autonomy-Supportive Game Benefits Both Inexperienced and Experienced Programmers

   https://doi.org/doi/abs/10.5555/3503984.3503993  

   Lee, Michael J. ; Shen, Ruiqi ( January 2021 , Journal of computing sciences in colleges)

   As more people turn to discretionary online tools to learn new skills such as computer programming, exploring how to better support a wide range of learners is becoming increasingly essential to train the next generation of highly skilled technology workers. In our prior work, users with high learner autonomy complained that most online resources they used to learn more programming did not provide them with the flexibility they preferred to navigate through learning materials, locking them into a set sequence of topics/concepts. To explore this, we implemented a level-jumping feature into an online educational programming game. We tested it with 350 new users, tracking their progress through the game for 7 days each. We found that those with high learner autonomy did use the level jumping feature more than those with low learner autonomy. We also found that males were more likely to use this new feature, regardless of learner autonomy level, compared to their female counterparts. Finally, we found that those with low learner autonomy ultimately completed more levels than their high autonomy counterparts, and that this was particularly true of female learners (who completed the most levels overall). Based on these findings, we believe that autonomous-supportive features such as flexible navigation may be beneficial to all users of online educational tools, and that encouraging its use by a wider group of users (particularly females), may increase positive effects. 

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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. Work in Progress: Interactive Introductory Online Modules on Wireless Communications and Radio-frequency Spectrum Sharing

   Dietrich, C. B. ; Polys, N. F. ; Reid, K. ; García Sheridan, J. A. ; Emenonye, D. ; Gomez, X. ; Tolley, J. ; Makin, C. ; Gaeddert, J. ( July 2021 , ASEE Annual Conference proceedings)

   1. Description of the objectives and motivation for the contribution to ECE education The demand for wireless data transmission capacity is increasing rapidly and this growth is expected to continue due to ongoing prevalence of cellular phones and new and emerging bandwidth-intensive applications that encompass high-definition video, unmanned aerial systems (UAS), intelligent transportation systems (ITS) including autonomous vehicles, and others. Meanwhile, vital military and public safety applications also depend on access to the radio frequency spectrum. To meet these demands, the US federal government is beginning to move from the proven but inefficient model of exclusive frequency assignments to a more-efficient, shared-spectrum approach in some bands of the radio frequency spectrum. A STEM workforce that understands the radio frequency spectrum and applications that use the spectrum is needed to further increase spectrum efficiency and cost-effectiveness of wireless systems over the next several decades to meet anticipated and unanticipated increases in wireless data capacity. 2. Relevant background including literature search examples if appropriate CISCO Systems’ annual survey indicates continued strong growth in demand for wireless data capacity. Meanwhile, undergraduate electrical and computer engineering courses in communication systems, electromagnetics, and networks tend to emphasize mathematical and theoretical fundamentals and higher-layer protocols, with less focus on fundamental concepts that are more specific to radio frequency wireless systems, including the physical and media access control layers of wireless communication systems and networks. An efficient way is needed to introduce basic RF system and spectrum concepts to undergraduate engineering students in courses such as those mentioned above who are unable to, or had not planned to take a full course in radio frequency / microwave engineering or wireless systems and networks. We have developed a series of interactive online modules that introduce concepts fundamental to wireless communications, the radio frequency spectrum, and spectrum sharing, and seek to present these concepts in context. The modules include interactive, JavaScript-based simulation exercises intended to reinforce the concepts that are presented in the modules through narrated slide presentations, text, and external links. Additional modules in development will introduce advanced undergraduate and graduate students and STEM professionals to configuration and programming of adaptive frequency-agile radios and spectrum management systems that can operate efficiently in congested radio frequency environments. Simulation exercises developed for the advanced modules allow both manual and automatic control of simulated radio links in timed, game-like simulations, and some exercises will enable students to select from among multiple pre-coded controller strategies and optionally edit the code before running the timed simulation. Additionally, we have developed infrastructure for running remote laboratory experiments that can also be embedded within the online modules, including a web-based user interface, an experiment management framework, and software defined radio (SDR) application software that runs in a wireless testbed initially developed for research. Although these experiments rely on limited hardware resources and introduce additional logistical considerations, they provide additional realism that may further challenge and motivate students. 3. Description of any assessment methods used to evaluate the effectiveness of the contribution, Each set of modules is preceded and followed by a survey. Each individual module is preceded by a quiz and followed by another quiz, with pre- and post-quiz questions drawn from the same pool. The pre-surveys allow students to opt in or out of having their survey and quiz results used anonymously in research. 4. Statement of results. The initial modules have been and are being used by three groups of students: (1) students in an undergraduate Introduction to Communication Systems course; (2) an interdisciplinary group of engineering students, including computer science students, who are participating in related undergraduate research project; and (3) students in a graduate-level communications course that includes both electrical and computer engineers. Analysis of results from the first group of students showed statistically significant increases from pre-quiz to post-quiz for each of four modules on fundamental wireless communication concepts. Results for the other students have not yet been analyzed, but also appear to show substantial pre-quiz to post-quiz increases in mean scores. 

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5. Mini-projects based Cybersecurity Modules for an Operating System Course using xv6

   Jansen Tan ; Divya Ravindra ; Quamar Niyaz ; Xiaoli Yang ; Sidike Paheding ; Ahmad Y Javaid ( August 2022 , ASEE Annual Conference proceedings)

   Cybersecurity continues to be a critical aspect within every computing division, especially in the realm of operating system (OS) development. The OS resides at the lower layer above the hardware in the computing hierarchy. If the layers above the OS are well hardened, a security flaw in the OS will compromise the resources in those higher layers. Although several learning resources and courses are available for OS security, they are taught in advanced UG or graduate-level computer security classes. In this work, we develop cybersecurity educational modules that instructors can adoptin their OS courses to emphasize security in OS while teaching its concepts. The goal of this work is to engage students in learning security aspects in OS, while learning its concepts. It will give students a good understanding of different security concepts and how they are implemented in the OS. Towards this, we develop security educational modules for an OS course that will be available to the instructors for adoption in their courses. These modules are designed to be used in a UG-level OS course. To work on these modules, students should be familiar with C programming and OS concepts taught in the class. The modules are intended to be completed within the course of a semester. To achieve this goal, we organize them into three mini-projects witheach can be completed within a few weeks. We chose xv6 as the platform due to its popularity as an educational OS for developing the modules. To develop the modules, we referred to the recent version of a popular OS textbook for the security concepts. The topics discussed in it include authentication, authorization, cryptography, and distributed system security. We kept our educational modules mostly aligned with these topics except distributed system security. We also included a module for implementing a defense mechanism against buffer-overflow attacks, a famous software vulnerability. We created three mini-projects for these modules, each accompanied by proper documentation and a GitHub repository. Two versions are created for each project, one for a student’s assignment available in the repository and another as a solution version for instructors. The first project implements a user authentication system in xv6. Students will implement various specifications such as password structure with encryption and programs such as useradd, passwd, whoami, and login. The implementation guidelines are provided in the documentation, along with skeleton code. The authorization project implements the Unix-style access control system. In this project, students will modify and create various structures and functions within the xv6 kernel. The last project is to build a defense mechanism against buffer-overflow using Address Space Layout Randomization (ASLR). Students are expected to implement a random number generator and modify the executable file loader in xv6. The submission for each project is expected to demonstrate the module behavior comparable to relevant systems present in production grade OS, such as Linux. 

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