A Framework for Developing and Assessing Programming Skills using H5P and Moodle Phase #2
Assessing student’s knowledge and skills for programming is often limited to their knowledge of the language and coding ability. Development tools are often an overlooked aspect of programming. Some attempt have been made to add content to courses but it is often difficult to assess student skills through traditional means. Combining H5P content with Moodle offers an interactive way for students to learn, practice, and instructors to assess skills as opposed to assess knowledge. The goal of this project is to design and develop a framework for H5P content for learning and developing skills using tools such as: Vim, Make, and Gdb.
Use of 360 LIDAR and the ROS Operating System in a Robotics Laboratory Setting
Providing students with both theoretical knowledge and practical skills is desired but often limited within a single Robotics course. In addition, some advanced algorithms (such as SLAM - simultaneous location and mapping) might not fit within the contents of an entry level course however the topics can still provide great teaching value in terms of engagement and interest. The question becomes how to incorporate difficult topics without overwhelming students. The goal of this project is to design and develop a modular framework for using the Robotic Operating System (ROS) and a 360 LIDAR for teaching robotic localization within the computer science curriculum.
Lab assignment development for introductory Computer Science course
The student will work collaboratively with a supervisor to develop new CMPT 101 lab assignments for the next year which will include writing code to demonstrate various coding constructs, testing and evaluating code implementations, and finalizing assignment specifications.
Level UP-Building online tutorials for a web development project (Phase 2)
positions available: 3In this project, we will need to build 6 online tutorials for building web applications. Tutorials include:1. Building Backend server tutorials2. Build frontend tutorials3. creating a video recording of the tutorials
LevelUp: Recognizing problems in complex and compound sentences (PART I)
Positions: 1Summary:Problem solving for artificial intelligence begins by recognizing that a problem exists. Grammatically, problems are generally found in multiple sentences, known as a problem statement, in the interrogative and imperative form. Extracting problem information from simple sentences is straightforward, however, for complex and compound sentences becomes challenging. The goal of this project is to develop decision tree rules to classify sentences into simple, compound, or complex form and then to further classify their parts into declarative, interrogative, imperative, or exclamatory form.The project will start with a literature review problem recognition and sentence structure. Next, time will be spent on creating the rules for classifying sentences into their various forms. Next, a database of sentences will be created and their form determined. Finally, testing will be done using randomly chosen sentences from the database.Qualifications:The applicant is expected to have some experience with English grammar shown through completing courses from the English DepartmentTimeline:May 15-June 30, 2021Funds:Stipend - $1400 (20hrs/week * 4weeks )
LevelUp: Recognizing problems in complex and compound sentences (PART II)
Positions: 1Summary:Problem solving for artificial intelligence begins by recognizing that a problem exists. Grammatically problems are generally found in multiple sentences, known as a problem statement, in the interrogative and imperative form. Extracting problem information from simple sentences is straightforward, however, for complex and compound sentences becomes challenging. The goal of this project is develop a program, using Natural Language processing and previously developed decision rules, to classify sentences into simple, compound, or complex form and then to further classify their parts into declarative, interrogative, imperative, or exclamatory form.The project will start by reviewing the rules for classification and learning the Natural Language Processing library. Next, the program will be developed using Python. Finally, testing will be done using a previously created database of sentences.Qualifications:The applicant is expected to have some Python programming experience. Understanding of Machine Learning and the use of a NLP library would be considered an asset.Timeline:July 01-August 31, 2021Funds:Stipend - $1400 (20hrs/week * 4weeks )
LevelUp: Augmented Reality Demonstrations in Computer Science classes
Positions available: 1Summary:Learning theoretical concepts in classes can be difficult and are often supplemented by applied examples and demonstrations. In an online environment this can be difficult for kinesthetic learners where physically manipulating objects is not easily accomplished. Augmented Reality (AR) offers a solution to this by allowing a student to manipulate a 3D object on a screen through gestures. The goal of this project is to design, develop, and implement Augmented Reality models for difficult concepts found in the computing science curriculum.The project will start by developing a concept inventory of challenging topics in computer science courses for which AR models can be developed. Open source modeling software will then be used to develop the 3D models. Next, Unity game engine will be used to develop the animations and gestures for manipulating the model. Testing will then be done on smart phones and AR glasses.Qualifications:The applicant is expected to have some experience working with, modeling, a game engine, and a OOP programming language at the start of the project.Timeline:July 01-August 31, 2021Funds:Level Up Stipend - $1400 (20hrs/week * 4weeks )
Level UP- Effect of COVID-19 pandemic on education Phase 2
To conduct a province-wide study of school divisions’ experiences with teacher retention before and after the COVID-19 pandemic. This will be achieved by distributing a survey inquiring about how staff employing, retention, and dismissal have changed since the pandemic. The survey will also identify hiring practices and division properties such as rural/urban. Once complete, this survey will be submitted to the College of Alberta School of Superintendents (CASS), Alberta’s educational leaders that oversee the success of school systems in Alberta.Survey data will be used with data analysis techniques; unsupervised learning techniques such as clustering will identify key factors associated with higher or lower staff hiring, retention, and other quantifiers. Patterns associated with hiring techniques will be analyzed, and conclusions about population geographical factors’ relation to the survey results will be drawn.
Level UP- Effect of COVID-19 pandemic on education Phase 1
To conduct a province-wide study of school divisions’ experiences with teacher retention before and after the COVID-19 pandemic. This will be achieved by distributing a survey inquiring about how staff employing, retention, and dismissal have changed since the pandemic. The survey will also identify hiring practices and division properties such as rural/urban. Once complete, this survey will be submitted to the College of Alberta School of Superintendents (CASS), Alberta’s educational leaders that oversee the success of school systems in Alberta.Survey data will be used with data analysis techniques; unsupervised learning techniques such as clustering will identify key factors associated with higher or lower staff hiring, retention, and other quantifiers. Patterns associated with hiring techniques will be analyzed, and conclusions about population geographical factors’ relation to the survey results will be drawn.
Level UP-Analyzing factors that cause Car speeding Phase 1
positions available: 2This analysis used data from speed zones and traffic signs using methods such as functional data clustering algorithms. In this project, we will1. Determine indicators associated with a car speeding such as known police zones, how enforcement is done in these areas and unknown information about the day (weather, holiday, sporting events) that may or may not affect the data.2. Apply functional data clustering and model-based clustering algorithms
A Framework for Developing and Assessing Programming Skills using H5P and Moodle
Assessing student’s knowledge and skills for programming is often limited to their knowledge of the language and coding ability. Development tools are often an overlooked aspect of programming. Some attempt have been made to add content to courses but it is often difficult to assess student skills through traditional means. Combining H5P content with Moodle offers an interactive way for students to learn, practice, and instructors to assess skills as opposed to assess knowledge. The goal of this project is to design and develop a framework for H5P content for learning and developing skills using tools such as: Vim, Make, and Gdb.
Level UP-Multicommodity Flow Reliability for Energy Harvesting WSNs Phase 2
positions available: 1In this project, we will design and develop an iterative framework for estimating lower and upper bounds of reliability measures for energy harvesting wireless sensor networks that are shared between multiple applications. Each application has its defined quality of service and information requirements. The considered sensor network uses an energy management scheme to optimize the communication energy consumption of sensor nodes.
Level UP-Analyzing factors impacting COVID-19 vaccination rates Phase 1
positions available: 3This analysis used COVID-19 vaccination data, and country indicators from the World Bank to 1. Determine indicators that are associated with vaccination rate2. Create indices to measure the Vaccine Utilization and Vaccination Motivation per country3. Apply Decision trees and Pearson correlations to determine relationships with country indicators and these indices
Level UP-Multicommodity Flow Reliability for Energy Harvesting WSNs Phase 1
positions available: 1In this project, we will design and develop an iterative framework for estimating lower and upper bounds of reliability measures for energy harvesting wireless sensor networks that are shared between multiple applications. Each application has its defined quality of service and information requirements. The considered sensor network uses an energy management scheme to optimize the communication energy consumption of sensor nodes.
Level UP-Building online tutorials for a wireless sensor network project
positions available: 3In this project, we will need to build 7 online tutorials for building wireless sensor network applications using Contiki OS and Cooja simulation for CC1350 Launchpad. Tutorials include:1. Building peripherals control tutorials2. Building multi-threading low-power application tutorials3. Building communication and routing protocols tutorials4. Building wireless sensor network simulation tutorials5. Creating video recordings and documentations of the tutorials
Level UP-Analyzing factors impacting COVID-19 vaccination rates Phase 2
positions available: 4This analysis used COVID-19 vaccination data, and country indicators from the World Bank to 1. Determine indicators that are associated with vaccination rate2. Create indices to measure the Vaccine Utilization and Vaccination Motivation per country3. Apply Decision trees and Pearson correlations to determine relationships with country indicators and these indices
Level UP-Building online tutorials for a web development project
positions available: 3In this project, we will need to build 6 online tutorials for building web applications. Tutorials include:1. Building Backend server tutorials2. Build frontend tutorials3. creating a video recording of the tutorials
Level UP-Analyzing factors impacting COVID-19 vaccination rates Phase 1
positions available: 3This analysis used COVID-19 vaccination data, and country indicators from the World Bank to 1. Determine indicators that are associated with vaccination rate2. Create indices to measure the Vaccine Utilization and Vaccination Motivation per country3. Apply Decision trees and Pearson correlations to determine relationships with country indicators and these indices
Develop of a plugin to an Automated Assessment Platform for Providing student Feedback
Providing meaningful and just-in-time feedback is an essential part for student learning. Often with large, first year Computer Science courses, marking labs involves assessing programming code. Instructor corrections can often be foreign to a beginner programmer. The goal of this project is to design, develop, and implement a feedback system within a marking platform which in the end offers students corrections and learning aids specific to their assessments.
Developing CF for video data fit as multivariate functional data. Phase 3
positions available: 2This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects).To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.
Developing CF for video data fit as multivariate functional data. Phase 1
positions available: 2This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects).To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.
Developing CF for video data fit as multivariate functional data. Phase 2
positions available: 2This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects).To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.
Level UP- Effect of COVID-19 pandemic on education Phase 3
This project aims to determine the impact that COVID-19 has had on Alberta teacher retention. Over the last two Phases, a survey was distributed to every public and Catholic school division across Alberta, querying the state of school staff hiring, retention, resignation, and retirement before and after the COVID-19 pandemic. This Phase will conclude the survey collection process, focusing on analyzing the data collected. Many data mining analysis methods will be employed to determine common factors across these teacher retention results, including clustering, decision tree analysis, correlation tables, and more. The R coding language will be used for the analysis, which will be run in the RStudio development environment. Following this analysis, a research paper will be composed, including related works, background information, and an in-depth explanation of the study and the results.
Analysing the effect of industrial environmental damage in Canada. Phase 1
positions available: 2This project aims to quantify/discover environmental damage due to industrial activity in Canada. This will be achieved by combining various datasets from the Canadian government and other certified sources.In this project, we willLook at environmental indicators such as infectious diseases, malformation rates, endocrine and stress responses, genotoxicity, and concentrations of heavy metals, naphthenic acids and polycyclic aromatic hydrocarbons within amphibians as well as the overall ecosystems.Apply functional data clustering and model-based clustering algorithms.Show the current trajectory of these ecosystems towards recovery and regrowth.
Analysing the effect of industrial environmental damage in Canada. Phase 2
positions available: 2This project aims to quantify/discover environmental damage due to industrial activity in Canada. This will be achieved by combining various datasets from the Canadian government and other certified sources.In this project, we willLook at environmental indicators such as infectious diseases, malformation rates, endocrine and stress responses, genotoxicity, and concentrations of heavy metals, naphthenic acids and polycyclic aromatic hydrocarbons within amphibians as well as the overall ecosystems.Apply functional data clustering and model-based clustering algorithms.Show the current trajectory of these ecosystems towards recovery and regrowth.
Level UP-Analyzing factors that cause Car speeding Phase 2
positions available: 2This analysis used data from speed zones and traffic signs using methods such as functional data clustering algorithms. In this project, we will1. Determine indicators associated with a car speeding such as known police zones, how enforcement is done in these areas and unknown information about the day (weather, holiday, sporting events) that may or may not affect the data.2. Apply functional data clustering and model-based clustering algorithms