The recent advancement in computational thinking (CT) research has reported numerous learning benefits to school-age children. The long-standing perceived difficulty of computer programming has challenged the acquisition of CT skills from programming education. Several block-based programming environments (BBPEs) have been developed to reduce this difficulty and enhance active engagement in computational-related activities. Although numerous studies have examined students’ level of interactions during block-based programming modality (BPM) activities, a major gap in the literature is the paucity of research evidence reporting the association between these interactions and CT. This study, therefore, investigates the association between interaction patterns during BPM activities and CT skills. The present study employed a longitudinal approach where the same participants were observed over eight weeks. Thirty-five, second-year-level computer science and computer education students (mean age: 19.8; male = 23, female = 12) from a research university in Nigeria were recruited. Their computational activities over the study periods were video-recorded. The participants’ CT skills were collected using the computational thinking test and the computational thinking scale. Findings indicate four interaction patterns: learner–learner, learner–content, learner–teacher, and learner–distractor. Learner–learner and learner–content were prevalent. The interaction patterns significantly predict CT skills although significant differences exist across gender, cognitive load, spatial ability, and programming proficiency. The research has provided opportunities for educators to integrate BBPEs in learning programming and CT concepts. Although such integration is likely to occur with the help of strong educational policies, teachers are encouraged to cultivate the spirit of collaboration in students during programming activities.
{"title":"Interaction Patterns During Block-based Programming Activities Predict Computational Thinking: Analysis of the Differences in Gender, Cognitive Load, Spatial Ability, and Programming Proficiency","authors":"Abdullahi Yusuf, Norah Md Noor, Marcos Román-González","doi":"10.5772/acrt.36","DOIUrl":"https://doi.org/10.5772/acrt.36","url":null,"abstract":"The recent advancement in computational thinking (CT) research has reported numerous learning benefits to school-age children. The long-standing perceived difficulty of computer programming has challenged the acquisition of CT skills from programming education. Several block-based programming environments (BBPEs) have been developed to reduce this difficulty and enhance active engagement in computational-related activities. Although numerous studies have examined students’ level of interactions during block-based programming modality (BPM) activities, a major gap in the literature is the paucity of research evidence reporting the association between these interactions and CT. This study, therefore, investigates the association between interaction patterns during BPM activities and CT skills. The present study employed a longitudinal approach where the same participants were observed over eight weeks. Thirty-five, second-year-level computer science and computer education students (mean age: 19.8; male = 23, female = 12) from a research university in Nigeria were recruited. Their computational activities over the study periods were video-recorded. The participants’ CT skills were collected using the computational thinking test and the computational thinking scale. Findings indicate four interaction patterns: learner–learner, learner–content, learner–teacher, and learner–distractor. Learner–learner and learner–content were prevalent. The interaction patterns significantly predict CT skills although significant differences exist across gender, cognitive load, spatial ability, and programming proficiency. The research has provided opportunities for educators to integrate BBPEs in learning programming and CT concepts. Although such integration is likely to occur with the help of strong educational policies, teachers are encouraged to cultivate the spirit of collaboration in students during programming activities.","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":"50 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work focuses on the solution of the convection–diffusion equation, especially for small diffusion coefficients, employing a time-simultaneous multigrid algorithm, which is closely related to multigrid waveform relaxation. For discretization purposes, linear finite elements are used while the Crank–Nicolson scheme acts as the time integrator. By combining all time steps into a global linear system of equations and rearranging the degrees of freedom, a space-only problem is formed with vector-valued unknowns for each spatial node. The generalized minimal residual method with block Jacobi preconditioning can be used to numerically solve the (spatial) problem, allowing a higher degree of parallelization in space. A time-simultaneous multigrid approach is applied, utilizing space-only coarsening and the aforementioned solution techniques for smoothing purposes. Numerical studies analyze the iterative solution technique for 1D test problems. For the heat equation, the number of iterations stays bounded independently of the number of time steps, the time increment, and the spatial resolution. However, convergence issues arise in situations where the diffusion coefficient is small compared to the grid size and the magnitude of the velocity field. Therefore, a higher-order variational multiscale stabilization is used to improve the convergence behavior and solution smoothness without compromising its accuracy in convection-dominated scenarios.
{"title":"Numerical Analysis of a Time-Simultaneous Multigrid Solver for Stabilized Convection-Dominated Transport Problems in 1D","authors":"Wiebke Drews, Stefan Turek, Christoph Lohmann","doi":"10.5772/acrt.37","DOIUrl":"https://doi.org/10.5772/acrt.37","url":null,"abstract":"This work focuses on the solution of the convection–diffusion equation, especially for small diffusion coefficients, employing a time-simultaneous multigrid algorithm, which is closely related to multigrid waveform relaxation. For discretization purposes, linear finite elements are used while the Crank–Nicolson scheme acts as the time integrator. By combining all time steps into a global linear system of equations and rearranging the degrees of freedom, a space-only problem is formed with vector-valued unknowns for each spatial node. The generalized minimal residual method with block Jacobi preconditioning can be used to numerically solve the (spatial) problem, allowing a higher degree of parallelization in space. A time-simultaneous multigrid approach is applied, utilizing space-only coarsening and the aforementioned solution techniques for smoothing purposes. Numerical studies analyze the iterative solution technique for 1D test problems. For the heat equation, the number of iterations stays bounded independently of the number of time steps, the time increment, and the spatial resolution. However, convergence issues arise in situations where the diffusion coefficient is small compared to the grid size and the magnitude of the velocity field. Therefore, a higher-order variational multiscale stabilization is used to improve the convergence behavior and solution smoothness without compromising its accuracy in convection-dominated scenarios.","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":"31 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper deals with artificial neural network (ANN) applied to control a standalone microgrid in French Guiana. ANN is an artificial intelligence technique used to control non-linear and complex systems. ANN associated with the Levenberg–Marquardt (LM) algorithm has many advantages, such as rapid decision-making and improved system transients. Therefore, this technique should be adapted for the control of photovoltaic (PV) systems in the tropical climate of French Guiana with high variation in irradiance. The microgrid is composed of a PV source and a storage battery to supply an isolated building which is modeled by a DC load. The PV source is controlled by an ANN-based MPPT (Maximum Power Point Tracking) controller. To validate our ANN-MPPT, we compared it with one of the very popular MPPT algorithms, which is the P&O-MPPT algorithm. The comparison results show that our ANN-MPPT works well because it can find the maximum power point quickly. In the case of battery control, we tested two feed-forward backpropagation neural network (FFBNN) configurations called method1 and method2 associated with the Levenberg–Marquardt (LM) algorithm. We varied the number of hidden layers in each of these two FFBNN configurations to obtain the optimal number of hidden layers for each configuration which optimizes battery control. Method1 is chosen because it is better than method2, in a sense that it respects the maximum amplitude of the battery current for our application and improves the transient regimes of this current. This best configuration (method1) is then tested with two other learning algorithms for comparison: Bayesian regularization (BR) and scaled conjugate gradient (SCG) methods. The system performance with LM algorithm is better than SCG and BR algorithms. LM algorithm improves the performance of the system in transient regimes while the results obtained with the SGG and BR algorithms are similar. Then, we focused on the advantage of using ANN control compared to the conventional proportional integral control (PI control). The comparison results showed that ANN control associated with the LM algorithm (ANN-LM) made it possible to reduce battery current peaks by 26% in transient regimes compared to conventional PI control. Finally, we present and discuss the results of our simulation obtained with the MATLAB Simulink software.
{"title":"Artificial Neural Network Control Applied to a Photovoltaic-Battery Microgrid System","authors":"Chabakata Mahamat, Jessica Bechet, Laurent Linguet","doi":"10.5772/acrt.34","DOIUrl":"https://doi.org/10.5772/acrt.34","url":null,"abstract":"This paper deals with artificial neural network (ANN) applied to control a standalone microgrid in French Guiana. ANN is an artificial intelligence technique used to control non-linear and complex systems. ANN associated with the Levenberg–Marquardt (LM) algorithm has many advantages, such as rapid decision-making and improved system transients. Therefore, this technique should be adapted for the control of photovoltaic (PV) systems in the tropical climate of French Guiana with high variation in irradiance. The microgrid is composed of a PV source and a storage battery to supply an isolated building which is modeled by a DC load. The PV source is controlled by an ANN-based MPPT (Maximum Power Point Tracking) controller. To validate our ANN-MPPT, we compared it with one of the very popular MPPT algorithms, which is the P&O-MPPT algorithm. The comparison results show that our ANN-MPPT works well because it can find the maximum power point quickly. In the case of battery control, we tested two feed-forward backpropagation neural network (FFBNN) configurations called method1 and method2 associated with the Levenberg–Marquardt (LM) algorithm. We varied the number of hidden layers in each of these two FFBNN configurations to obtain the optimal number of hidden layers for each configuration which optimizes battery control. Method1 is chosen because it is better than method2, in a sense that it respects the maximum amplitude of the battery current for our application and improves the transient regimes of this current. This best configuration (method1) is then tested with two other learning algorithms for comparison: Bayesian regularization (BR) and scaled conjugate gradient (SCG) methods. The system performance with LM algorithm is better than SCG and BR algorithms. LM algorithm improves the performance of the system in transient regimes while the results obtained with the SGG and BR algorithms are similar. Then, we focused on the advantage of using ANN control compared to the conventional proportional integral control (PI control). The comparison results showed that ANN control associated with the LM algorithm (ANN-LM) made it possible to reduce battery current peaks by 26% in transient regimes compared to conventional PI control.\u0000 Finally, we present and discuss the results of our simulation obtained with the MATLAB Simulink software.","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":"112 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. C. Demirkan, Ava Segal, Abhidipta Mallik, Sebnem Duzgun, Andrew J Petruska
First responders in underground mines face a myriad of challenges when searching for personnel in a disaster scenario. Possibly, the most acute challenge is the complete lack of visibility owing to a combination of dust, smoke, and pitch-black conditions. Moreover, the complex environment compounds the difficulty of navigating and searching the area as well as identifying hazardous conditions until in close proximity. Enhanced perception and localization technologies that enable rapid and safe disaster response could mitigate the mine rescue team’s risk and reduce response times. We developed a proof of concept perception enhancement tool for mine rescue personnel in pitch-black conditions by leveraging LiDAR, thermal camera, and data fusion to reconstruct a 3D representation of the space in real-time. This enhancement is visualized on HoloLens, allowing the responders real-time situational awareness of personnel, walls, obstacles, or fires in otherwise opaque environments. The technology is a first step towards faster, safer, and more effective disaster response for mine rescue operations, including detection of unexpected hazards before they become imminent threats.
{"title":"Real-Time Perception Enhancement in Obscured Environments for Underground Mine Search and Rescue Teams","authors":"D. C. Demirkan, Ava Segal, Abhidipta Mallik, Sebnem Duzgun, Andrew J Petruska","doi":"10.5772/acrt.33","DOIUrl":"https://doi.org/10.5772/acrt.33","url":null,"abstract":"First responders in underground mines face a myriad of challenges when searching for personnel in a disaster scenario. Possibly, the most acute challenge is the complete lack of visibility owing to a combination of dust, smoke, and pitch-black conditions. Moreover, the complex environment compounds the difficulty of navigating and searching the area as well as identifying hazardous conditions until in close proximity. Enhanced perception and localization technologies that enable rapid and safe disaster response could mitigate the mine rescue team’s risk and reduce response times. We developed a proof of concept perception enhancement tool for mine rescue personnel in pitch-black conditions by leveraging LiDAR, thermal camera, and data fusion to reconstruct a 3D representation of the space in real-time. This enhancement is visualized on HoloLens, allowing the responders real-time situational awareness of personnel, walls, obstacles, or fires in otherwise opaque environments. The technology is a first step towards faster, safer, and more effective disaster response for mine rescue operations, including detection of unexpected hazards before they become imminent threats.","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":" 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Embodied agents are agents that display human-like behavior while interacting with their environment. Educational robots, which are embodied agents, or more specifically knowledge agents, are extensions of the learners. The robot interface is physically, mentally, and socially human-like and can be programmed to exhibit behaviors that replicate interconnected physical, mental, and social characteristics. As such, they provide a compelling social and emotional learning teaching and learning tool. Social and emotional learning is the process through which children and adults acquire and effectively apply the knowledge, attitudes, and skills necessary to understand and manage emotions, establish and achieve positive goals, feel and show empathy for others, establish and maintain positive relationships, and make responsible decisions. This article explores the space between robots as embodied agents and social and emotional learning.
{"title":"Educational Robots for Social and Emotional Learning","authors":"Pamela M Davis, M. Class","doi":"10.5772/acrt.26","DOIUrl":"https://doi.org/10.5772/acrt.26","url":null,"abstract":"Embodied agents are agents that display human-like behavior while interacting with their environment. Educational robots, which are embodied agents, or more specifically knowledge agents, are extensions of the learners. The robot interface is physically, mentally, and socially human-like and can be programmed to exhibit behaviors that replicate interconnected physical, mental, and social characteristics. As such, they provide a compelling social and emotional learning teaching and learning tool. Social and emotional learning is the process through which children and adults acquire and effectively apply the knowledge, attitudes, and skills necessary to understand and manage emotions, establish and achieve positive goals, feel and show empathy for others, establish and maintain positive relationships, and make responsible decisions. This article explores the space between robots as embodied agents and social and emotional learning.","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125362874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In graduate education (GE), critical reading of reading primary literature (CRPL) is the principal method of learning a discipline and its practices, such as discourse about research and preparation for writing. CRPL is inconsistently taught in GE yet essential, forming a hidden curriculum advantaging the best-prepared students. In traditional reading or journal clubs, PL discourse follows a hierarchical social model, where those with the most knowledge and extroversion dominate discussions. This article reports qualitative findings explaining why online peer engagement using social collaboration annotation (SCA) with embedded prompts supported CRPL in a broader mixed-methods intervention study with doctoral participants in spring 2022. The broader study reported elsewhere was an online intervention delivered over four weeks where participants in various fields read science policy research. Qualitative data collection elicited participants’ practices, experiences, and self-perceptions of CRPL in the new field while using SCA for peer-based discourse and collaboration. The findings indicated that when everyone was learning with SCA, they asked questions and learned from seeing each other’s ideas in real time, thereby correcting mistakes, activating various critical strategies, and improving comprehension and confidence. Self-doubt and fear calmed down in a supportive environment without the hierarchy of traditional journal clubs. The implication is that students encountering CRPL need and benefit from explicit instruction and low-stakes peer-based discourse practice with SCA.
{"title":"The Power of Peer Engagement: Exploring the Effects of Social Collaborative Annotation on Reading Comprehension of Primary Literature","authors":"Genevive Bjorn","doi":"10.5772/acrt.24","DOIUrl":"https://doi.org/10.5772/acrt.24","url":null,"abstract":"In graduate education (GE), critical reading of reading primary literature (CRPL) is the principal method of learning a discipline and its practices, such as discourse about research and preparation for writing. CRPL is inconsistently taught in GE yet essential, forming a hidden curriculum advantaging the best-prepared students. In traditional reading or journal clubs, PL discourse follows a hierarchical social model, where those with the most knowledge and extroversion dominate discussions. This article reports qualitative findings explaining why online peer engagement using social collaboration annotation (SCA) with embedded prompts supported CRPL in a broader mixed-methods intervention study with doctoral participants in spring 2022. The broader study reported elsewhere was an online intervention delivered over four weeks where participants in various fields read science policy research. Qualitative data collection elicited participants’ practices, experiences, and self-perceptions of CRPL in the new field while using SCA for peer-based discourse and collaboration. The findings indicated that when everyone was learning with SCA, they asked questions and learned from seeing each other’s ideas in real time, thereby correcting mistakes, activating various critical strategies, and improving comprehension and confidence. Self-doubt and fear calmed down in a supportive environment without the hierarchy of traditional journal clubs. The implication is that students encountering CRPL need and benefit from explicit instruction and low-stakes peer-based discourse practice with SCA.","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114608003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Virtual learning during the Covid-19 pandemic has made the effective use of technology, as a learning tool, more important than it has ever been. One of the challenges within instructional technology courses for preservice elementary education majors was whether or not to train students to use technology as a means to an end or to focus on technology skills. Instructional technology courses could reinforce traditional approaches or encourage higher order thinking or acquisition of twenty first century skills for formal or informal settings. This study explored the pre-Covid inclinations and experiences of undergraduate preservice teachers who engaged in various types of technology that facilitated both the learning of content and the building of technological skills to varying degrees. This basic qualitative exploratory study looked at preservice teachers’ perceptions about their engagement with the technologies and about their own capabilities. The findings show connections that were most salient to the preservice teachers. These pre-Covid pandemic findings have implications for the current state of instructional technology and learning using technology in the post-Covid pandemic era.
{"title":"Unpacking Preservice Elementary Education Majors’ Pre-Covid Experiences with Instructional Technology: Implications for Post-Covid Technology Use","authors":"Catherine L. Quinlan","doi":"10.5772/acrt.25","DOIUrl":"https://doi.org/10.5772/acrt.25","url":null,"abstract":"Virtual learning during the Covid-19 pandemic has made the effective use of technology, as a learning tool, more important than it has ever been. One of the challenges within instructional technology courses for preservice elementary education majors was whether or not to train students to use technology as a means to an end or to focus on technology skills. Instructional technology courses could reinforce traditional approaches or encourage higher order thinking or acquisition of twenty first century skills for formal or informal settings. This study explored the pre-Covid inclinations and experiences of undergraduate preservice teachers who engaged in various types of technology that facilitated both the learning of content and the building of technological skills to varying degrees. This basic qualitative exploratory study looked at preservice teachers’ perceptions about their engagement with the technologies and about their own capabilities. The findings show connections that were most salient to the preservice teachers. These pre-Covid pandemic findings have implications for the current state of instructional technology and learning using technology in the post-Covid pandemic era.","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117234675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hands-on assessments provide active opportunities for students to practice new skills they have just learned. Massive open online course (MOOC) platforms offer a uniquely large dataset to track the impact of hands-on assessments on learners’ skill development, satisfaction, and career trajectory. While existing MOOC literature explore enrollment and demographic data, few have investigated the outcomes for learners who engage with different types of assessments within these online courses. This article is important because it quantifies the learner impact of hands-on experiences in MOOCs. With innovative analytics and hundreds of millions of course enrollments, online course platforms can shed light on the influence of alternative teaching decisions and assessment types. MOOCs offer data to quantify individual learners’ skill development in different topics before averaging across all course completers. Metrics, such as satisfaction, utilize learners’ self-reported star ratings of course material. Finally, for career outcomes, MOOC platforms can interact with learners after completing an online course to ask them how the content impacted their job-related outcomes, such as confidence in their role, receiving a promotion, or starting a new position. Control variables such as course domain, instructor characteristics, and learner demographics provide researchers with a robust dataset and thorough methodology to systematically track the benefits of hands-on opportunities in online content. This article examines the content structure and learning behavior data on a MOOC platform. The goal of this empirical study was to examine the impact of hands-on assessments on learner outcomes, including retention, satisfaction, skill development, and career outcomes.
{"title":"How Hands-On Assessments Can Boost Retention, Satisfaction, Skill Development, and Career Outcomes in Online Courses","authors":"Alexander T. Urban","doi":"10.5772/acrt.23","DOIUrl":"https://doi.org/10.5772/acrt.23","url":null,"abstract":"Hands-on assessments provide active opportunities for students to practice new skills they have just learned. Massive open online course (MOOC) platforms offer a uniquely large dataset to track the impact of hands-on assessments on learners’ skill development, satisfaction, and career trajectory. While existing MOOC literature explore enrollment and demographic data, few have investigated the outcomes for learners who engage with different types of assessments within these online courses. This article is important because it quantifies the learner impact of hands-on experiences in MOOCs. With innovative analytics and hundreds of millions of course enrollments, online course platforms can shed light on the influence of alternative teaching decisions and assessment types. MOOCs offer data to quantify individual learners’ skill development in different topics before averaging across all course completers. Metrics, such as satisfaction, utilize learners’ self-reported star ratings of course material. Finally, for career outcomes, MOOC platforms can interact with learners after completing an online course to ask them how the content impacted their job-related outcomes, such as confidence in their role, receiving a promotion, or starting a new position. Control variables such as course domain, instructor characteristics, and learner demographics provide researchers with a robust dataset and thorough methodology to systematically track the benefits of hands-on opportunities in online content. This article examines the content structure and learning behavior data on a MOOC platform. The goal of this empirical study was to examine the impact of hands-on assessments on learner outcomes, including retention, satisfaction, skill development, and career outcomes.","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122004804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tasha Austin, Bharat S. Rawal, Alexandra Diehl, Jonathan Cosme
The purpose of this study is to show how AI can serve as an assessment tool to detect potential human bias in decision making for students in higher education. Using student application data, we conduct a small study and apply a set of algorithms to perform deep learning analyses and assess human behaviors when identifying scholarship recipients. We conduct an interview with the organization’s leaders using this data to understand their criteria and expectations for identifying scholarship recipients and collectively explore the insights uncovered using these algorithms. Upon comparison to those recipients awarded the scholarships, we identify opportunities for the organization to implement a quantitative framework—a repeatable set of algorithms to help identify potential bias before awarding future scholarship recipients.
{"title":"AI for Equity: Unpacking Potential Human Bias in Decision Making in Higher Education","authors":"Tasha Austin, Bharat S. Rawal, Alexandra Diehl, Jonathan Cosme","doi":"10.5772/acrt.20","DOIUrl":"https://doi.org/10.5772/acrt.20","url":null,"abstract":"The purpose of this study is to show how AI can serve as an assessment tool to detect potential human bias in decision making for students in higher education. Using student application data, we conduct a small study and apply a set of algorithms to perform deep learning analyses and assess human behaviors when identifying scholarship recipients. We conduct an interview with the organization’s leaders using this data to understand their criteria and expectations for identifying scholarship recipients and collectively explore the insights uncovered using these algorithms. Upon comparison to those recipients awarded the scholarships, we identify opportunities for the organization to implement a quantitative framework—a repeatable set of algorithms to help identify potential bias before awarding future scholarship recipients. ","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123064901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Advanced Persistent Threats (APT) and Targeted Attacks (TA) targeting high-value organizations continue to become more common. These slow (sometimes carried on over the years), fragmented, distributed, seemingly unrelated, very sophisticated, highly adaptable, and, above all, stealthy attacks have existed since the large-scale popularization of computing in the 1990s and have intensified during the 2000s. The aim of attackers has expanded from espionage to attaining financial gain, creating disruption, and hacktivism. These activities have a negative impact on the targets, many times costing significant amounts of money and destabilizing organizations and governments. The resounding goal of this research is to analyze previous academic and industrial research of 72 major APT attacks between 2008 and 2018, using 12 features, and propose a categorization based on the targeted platform, the time elapsed to discovery, targets, type, purpose, propagation methods, and derivative attacks. This categorization provides a view of the effort of the attackers. It aims to help focus the design of intelligent detection systems on increasing the percentage of discovered and stopped attacks.
{"title":"Feature-based Systematic Analysis of Advanced Persistent Threats","authors":"M. Miguez, Bahman Sassani (Sarrafpour)","doi":"10.5772/acrt.21","DOIUrl":"https://doi.org/10.5772/acrt.21","url":null,"abstract":"Advanced Persistent Threats (APT) and Targeted Attacks (TA) targeting high-value organizations continue to become more common. These slow (sometimes carried on over the years), fragmented, distributed, seemingly unrelated, very sophisticated, highly adaptable, and, above all, stealthy attacks have existed since the large-scale popularization of computing in the 1990s and have intensified during the 2000s. The aim of attackers has expanded from espionage to attaining financial gain, creating disruption, and hacktivism. These activities have a negative impact on the targets, many times costing significant amounts of money and destabilizing organizations and governments. The resounding goal of this research is to analyze previous academic and industrial research of 72 major APT attacks between 2008 and 2018, using 12 features, and propose a categorization based on the targeted platform, the time elapsed to discovery, targets, type, purpose, propagation methods, and derivative attacks. This categorization provides a view of the effort of the attackers. It aims to help focus the design of intelligent detection systems on increasing the percentage of discovered and stopped attacks.","PeriodicalId":431659,"journal":{"name":"AI, Computer Science and Robotics Technology","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121554562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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