2021 IEEE Integrated STEM Education Conference (ISEC)最新文献

{"title":"Implementing Blended Learning in K-12 Programming Course: Lesson Design and Student Feedback","authors":"Shuhang Zhang, Chunyu Cui","doi":"10.1109/ISEC52395.2021.9764091","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9764091","url":null,"abstract":"Computational thinking (CT) has been widely integrated into K-12 classrooms through programming education. Numerous initiatives have been developed to lower down the threshold for learning programming, yet instructors may still feel ill-prepared. Blended learning approach, a combination of student-centered learning and teacher-centered instruction, has proved to be an effective teaching approach for K-12 programming courses. With the purpose of providing practical insights for the design of blended programming lessons, this study introduced an instructional unit of a K-12 programming course in a secondary school in China. It elaborated the course regarding lesson design, learning assessment, and course evaluation. The course contains 9 plugged sessions and 24 unplugged sessions, and each session consists of 1) a preview, 2) hands-on activities, and 3) a lesson summary. Student learning was evaluated with performance-based assessments, and a questionnaire was employed to collect students’ feedback for the course. The results indicated that students with low performance were the benefit the most from the course, and students tended to like visual programming tool and stage-mode learning format. Also, students with different learning backgrounds showed different preferences for the instructional elements. Suggestions are provided for further research and course design practices.","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116564970","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}

{"title":"Comparison of Effectiveness of Machine Learning Algorithms for Vehicle Path Prediction","authors":"Sumanth R Moole","doi":"10.1109/ISEC52395.2021.9764068","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9764068","url":null,"abstract":"In modern warfare, intercepting moving enemy targets such as tanks, aircraft, missiles, and drones plays a crucial role. These targets are either controlled by enemy personnel or by sophisticated electronic systems. Therefore, their movements are best characterized by random motion subject to certain physical laws. Predicting these motions is extremely complex and often requires continuous tracking through sophisticated radar equipment. Machine Learning algorithms, such as Artificial Neural Networks, have proven to be effective in learning many real world motions of vehicles on the roads and have been extensively used in the autonomous vehicles. Artificial Neural Networks use activation functions to determine the output of a model from the given observations. After training the model with appropriate activation function, the model can be used for predictions. In this process, the activation functions play a crucial role. Selecting the correct activation function is critical to the success of the model. This project simulates the moving enemy target using a BristleBot (a brush-head fitted with vibrating motor which generates vibrations in the bristles thus propelling the BristleBot) which moves on a flat surface. The motion of the BristleBot is digitized by recording the X-Y coordinates on the path it has taken from the beginning of the run to the end of the run. These runs are repeated and data from multiple runs is stored in a database. Using R Programming language, a neural network training algorithm is simulated where the activation function can be changed (slope-intercept linear function y = mx + b with various slopes and intercepts, quadratic function y = a x2 + bx + c with various a, b, and c values). The resulting models corresponding to each training session are compared with each other to find their similarity to the paths taken by the BristleBot. The effectiveness of these activation functions is then measured by the similarity score. The trained model (or the activation function) with best similarity score is then selected for predicting the future path of the BristleBot. This model then can be stored on a chip and interceptor vehicles can use it to predict the path and intercept the target. This project is a simulation to demonstrate the usefulness of the Machine Learning algorithms (especially, Neural Networks) to train the models and store them on a chip that can guide the autonomous drones and missiles where sophisticated radar and satellite equipment are not feasible to guide them more accurately. Small inexpensive drones can be equipped with these chips to predict the paths of moving targets. Swarming with such drones is more economical in intercepting the targets. The simulation results with BristleBot are analyzed and similarity scores are obtained for different functions. These results indicate a reasonable effectiveness of quadratic functions for path prediction. The poster describes the simulation, linear and quadratic funct","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125137046","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}

{"title":"Open Research Laboratory for Non-Research Focused Institutions","authors":"M. S. Brown","doi":"10.1109/ISEC52395.2021.9764034","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9764034","url":null,"abstract":"Over the last few decades financial pressures on educational institutions have forced faculty to teach more classes taking time away from other activities including research. But research is important to institutions, faculty and students. This paper proposes for the creation of a non-profit entity that through grants and donations could pool institution, faculty and student resources across multiple intuitions to conduct joint research. This would allow institutions, faculty and students to be active in research within the time constraints that they currently have. Literature shows that this increase in research would have numerous benefits to students, faculty and institutions and promote interuniversity and international cooperation.","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"20 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133622527","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}

{"title":"The Fibonacci Sequence and The Golden Ratio in Math and Music","authors":"Nicole E Vassilev","doi":"10.1109/ISEC52395.2021.9764056","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9764056","url":null,"abstract":"The exact origin of the Fibonacci sequence is unknown. However, it is widely recognized to have first been used by Leonardo of Pisa, an Italian mathematician, to solve a breeding issue with rabbits. It has since evolved into a complex system that has been used and taught for many centuries. Whether we realize it or not, Fibonacci numbers and related ideas can be found in almost everything in our lives. A Fibonacci sequence consists of a list of numbers beginning with 0 and 1, in which each number is the sum of the two previous numbers in the sequence. For example, 0, 1, 1, 2, 3, 5 are the first numbers in the Fibonacci sequence, because 0 + 1 =1, 1 + 1 =2, 1 + 2 =3, and 2 + 3 =5. This pattern applies to any number in a Fibonacci sequence. The ratio of two fibonacci numbers that are one next to each other will always be extremely close to 1.618, the “golden ratio.” 1.618 is also known as ”phi” which originates from the 21st letter in the Greek alphabet ɸ. My research will look into the application of the Fibonacci sequence and the golden ratio in music specifically. On a foundational level, the Fibonacci sequence can be observed within a scale. The 5th note in a scale is the most important, and it happens to be the 8th note in an octave, which consists of 13 notes. Upon the division of 8 by 13, the rounded result is 0.615, a number practically identical to the golden ratio. It’s important to note that 5, 8, and 13 are all also numbers in the Fibonacci sequence. Beyond this foundational level, the Fibonacci sequence and golden ratio play a more widespread role in the composition of large musical works, such as in the first movement of a piece by Hungarian composer Béla Bartók. His piece, Music For Strings, Percussions and Celesta, is divided into two parts. Part one has 55 measures, and part two has 34 measures. When those numbers are divided, you get 1.6176, which when rounded, is 1.618 (the golden ratio). The Fibonacci sequence also makes appearances in rhythm, such as in the complex Konnakol rhythm by B.C Manjunath, which uses the first eight numbers of the Fibonacci sequence as its basis. My research will explore these occurrences of the Fibonacci sequence and the golden ratio in musical construction in order to more clearly demonstrate the parallels between music and math.","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126041373","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}

{"title":"Comparing Grover’s Quantum Search Algorithm with Classical Algorithm on Solving Satisfiability Problem","authors":"Runqian Wang","doi":"10.1109/ISEC52395.2021.9764017","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9764017","url":null,"abstract":"The emergence of quantum computing provides us the possibility of solving tasks that might take years classically in just a few minutes. For certain problems, quantum computing exhibits quantum supremacy, meaning that the quantum solution runs exponentially faster than classical algorithms and is able to completely take over classical computers. This high efficiency of quantum computing comes not only from the hardware but also the software, quantum algorithms. The algorithms utilize the qubits to make calculations in order to fulfill specific tasks with the lowest time complexity possible. One such algorithm is named the Grover’s algorithm, which is able to perform database search in $mathcal{O}(sqrt{N})$, and it runs much faster than the traditional algorithm that takes $mathcal{O}(N)$ time to solve the same task. For example, when the task is to find the even integers from N integers, traditional computation will need to run through all of the N integers one by one, making at least N steps of calculation, while by using Grover’s algorithm only around $sqrt{N}$ calculations are needed. This exponential speed-up makes Grover’s algorithm one of the most important quantum algorithms. Grover’s algorithm has a wide application in many fields and is able to improve the time complexity exponentially. One task that can be solved using Grover’s algorithm is the satisfiability problem. This type of problem asks the computer to find a set of values (commonly true or false) for several variables such that they satisfy certain constraints. We use k-SAT problems to refer to satisfiability problems with k boolean variables to be determined. Grover’s algorithm can effectively solve the k-SAT problem by performing the database search on $2 ^{N}$ possible states of the variables. The algorithm’s square root optimization on searching helps to improve the efficiency of this solution significantly. Furthermore, this optimization of Grover’s algorithm may play a more important role when k grows larger, and consequently the efficiency of the quantum solution could improve faster relative to the traditional solution. Yet this hypothesis is never tested due to the lack of a general k-SAT quantum algorithm. No quantum algorithms solving k-SAT problems where k is greater than 3 have been proposed, thus no test has been performed to compare the quantum solution and the classical solution on more general k-SAT problems. In this research, we formulate a general quantum solution for k-SAT problem and compare such solution with the best classical algorithm to determine whether and when the quantum algorithm performs better on satisfiability problems. The comparison will be done through both theoretical deduction as well as real-world implementation. At the end of this research, we will determine whether the proposed quantum algorithm outperforms the classical algorithm on solving k-satisfiability problems.","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"82 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116700740","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}

{"title":"The Math behind Piano Chords","authors":"Zuko A Ranganathan","doi":"10.1109/ISEC52395.2021.9764100","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9764100","url":null,"abstract":"In this poster, I shall describe the mathematics behind piano chords. Both major and minor chords are formed by three keys or notes, and both follow the rule of 7. In a major chord, we follow a 4 + 3 rule, where we pick the root note, and then 4 keys higher and then 3 keys further up. In a minor chord, we follow a 3 + 4 rule, where we pick the root note, and then 3 keys higher and then 4 keys further up. Just a small change in the middle key results in a big change in the mood of the chord (happy vs scary or sad). There are several variations of these formulas when we have chords with inversions. I shall explore the math behind this mood change and explain other mathematical formulae behind piano chords.","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121975958","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}

{"title":"Novel Application to Improve Communication for Children Affected by Autism Spectrum Disorder","authors":"Veda Murthy","doi":"10.1109/ISEC52395.2021.9763977","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9763977","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a spectrum of disorders that affects a child’s communication, social, and emotional skills. ASD is a major challenge for children because they are not able to communicate effectively with others, especially in terms of conveying their emotions. For the 40% of ASD children who are mute and are not able to verbalize their emotions, facial expressions are the primary indicator that family members and caregivers use to recognize their emotion. ASD children display facial features unique to each child, so those who are unfamiliar with the child such as teachers may find it difficult to interpret the child’s emotions. This creates a communication barrier between ASD children and the outside world, leading to frustration and isolation among the 13.7 million ASD children around the world. Current solutions to help ASD children socialize, such as speech practising or assisted learning apps, do not reduce this barrier. This is because these apps are not an immediate solution to this barrier, and can be effective only after months of practice by the child. Also, most of these solutions do not work for mute ASD children. Thus, there is a dire need for an individualized solution that interprets an ASD child’s emotion. My solution is the Cognitive Emotion Interpretation App (CEIA). CEIA uses Artificial Intelligence and Emotion Recognition Technology to map an ASD child’s facial expressions with an emotion. Through CEIA, people who are not familiar with the ASD child (teachers, extended family) can interpret the child’s emotion. When a user (parent, caregiver) downloads the app, they upload photos of the ASD child expressing different emotions and tag the picture with the emotion (e.g. Happy, Sad, Frustrated, Hungry). CEIA then extracts the child’s facial features, and the AI algorithm is trained to associate the picture with the emotion. When the user wants to interpret the child’s emotion, they take a photo of the child exhibiting the emotion and upload it to CEIA. The AI algorithm will evaluate the photo, and list the emotions that match with the highest accuracy. The user can also upload more photos at a later stage, and the AI algorithm will be retrained to take these new photos into its training dataset. A higher number of photos used in training generally yields a higher recognition accuracy, thus users are encouraged to upload many photos of the child’s emotions. The performance of the app will be evaluated on the following metrics: 1) accuracy of the emotion recognizer, 2) amount of time CEIA takes to recognize the emotion, and 3) CEIA’s ease of use. Accuracy will be measured by collecting a sample of a variety of emotions of different users, then measuring if CEIA correctly matched the emotion in the photo. This initial test of accuracy will provide a representative sample of the types of emotions CEIA will need to train on. CEIA will provide a much needed powerful tool to reduce the communication barrier between ASD ","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129769547","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}

{"title":"Roy. G. Biv: The Color Matching Application for Artists With Limited Pigments","authors":"Nina M Borodin, Sylvan Martin, Ryan Sokolowsky","doi":"10.1109/ISEC52395.2021.9763974","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9763974","url":null,"abstract":"When looking at a finished art piece, it is hard to discern what pigments are used to create a particular color. To aid art conservationists and novice artists in color replication, we developed an application that takes in the RGB values of the desired color and calculates the pigment ratios necessary for replicating that color. From a survey of 139 respondents, a total of 86.3% wish that there was a product that would calculate pigments to mix for a specific color. The user interface of the application is familiar and intuitive; it contains a camera screen that averages the RGB values within a crosshair, a screen displaying the calculated pigment ratio, and a color library in which a color and its associated pigment ratio are saved. The application has a 97.8% RGB scanning repeatability, showing that the RGB input is nearly identical each time a color is scanned. To train a machine learning model, a database of 872 hand-painted acrylic entries was constructed using a limited palette. The final training RMSE for the boosted tree model was 0.036 and the final testing RMSE was 0.141. The median color difference in the pigment values between the replicated color and the original color was 0.0668. This shows that the mixed color is 93.32% similar to the desired color. The application not only successfully extracts RGB values from a scanned image to tell the user the necessary pigment values for recreating a color, but also is unique in its non-spectral approach to subtractive color mixing.","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133225641","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}

{"title":"Static Straw Spinner","authors":"G. Saintil, Hunter Jushchuk","doi":"10.1109/ISEC52395.2021.9764095","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9764095","url":null,"abstract":"Static electricity was first discovered by Ewald Georg von Kleist, a German inventor. Static electricity is created by causing friction between two items. It can create a negative charge or a positive charge. These charges can attract each other if they are opposites and will repel each other if they are the same charges. When these charges come in contact with things like metal, which has a positive charge, you can get shocked.How It works When rubbed with wool the two straws end up with the same charges. When two objects have the same charge such as two positive charges or two negative charges they repel each other. This causes the straw you are holding to push the straw on the cup.How teachers use this in the class room This experiment shows static electricity in its simplest form, and how two of the same charges push against each other.How this could be used in the future Right now static electricity is used for electrostatic generators. Because movement is in a lot of things we do, static electricity could be something we use to power a lot of our items in our daily lives.","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130464545","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}

{"title":"Math & Crafts, Educational Activities: 400 Indigenous Kids Learning Math from Engineers and Scientists","authors":"Ernesto Vega Janica","doi":"10.1109/ISEC52395.2021.9764076","DOIUrl":"https://doi.org/10.1109/ISEC52395.2021.9764076","url":null,"abstract":"This paper provides a framework for the planning and implementation stages involved in teaching a math class, in full compliance with local educational programs, Additional content based on native/indigenous numerical systems is also provided. The educational program includes a combination of theory and practice to help kids appreciate technical concepts and provides a wide-range of learning possibilities for other applications. These “Math & Crafts” activities will be implemented in five schools within the Arhuaco School System, part of an indigenous community of approximately 30,000 people. Four hundred children in 5th grade of elementary school are our initial audience. Future projects are expected to include reaching out to urban students. Note: Due to the COVID-19 global pandemic, prerecorded and remote classes will be provided.","PeriodicalId":329844,"journal":{"name":"2021 IEEE Integrated STEM Education Conference (ISEC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114300453","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}