This document describes research on using deep learning to predict student performance in massive open online courses (MOOCs). It introduces GritNet, a model that takes raw student activity data as input and predicts outcomes like course graduation without feature engineering. GritNet outperforms baselines by more than 5% in predicting graduation. The document also describes how GritNet can be adapted in an unsupervised way to new courses using pseudo-labels, improving predictions in the first few weeks. Overall, GritNet is presented as the state-of-the-art for student prediction and can be transferred across courses without labels.
The document summarizes an internship report for a full-stack developer internship at Professional Computer System (PCS). The internship involved learning Spring as a backend framework and React as a frontend framework to develop a Canteen Management System web application. Key activities included research, installing software, and working on the specific project. The expected outcome was a functional web application for managing a canteen with features like record keeping, user and admin portals, and an ordering system.
This document summarizes a final year defense presentation on leveraging an interactive web-based virtual classroom. The presentation outlines the introduction, aim and objectives, hypothesis, research questions, literature review, proposed model, design and development, testing and evaluation, result analysis, performance analysis, and conclusion. It discusses designing a virtual classroom to allow interactive teaching and learning between teachers and students. Testing showed the classroom was an effective environment for both learning and teaching. The presentation concludes that a virtual classroom can benefit teachers and students and hopes it will support learning and teaching in the future.
Shobha Rani Kondapalli is a testing professional with over 3 years of experience in manual and automation testing. She has expertise in banking payments domains, ISO8583 and ISO20022 protocols, and the full software development and testing lifecycles. She is proficient in testing techniques including manual testing, regression testing, UI automation testing, and automation testing using Groovy. She is skilled in testing tools such as Selenium, Jira, and has experience in programming languages including Java and Groovy. She has worked on several projects for clients such as ACI Payment Systems and DBS, focusing on automation testing for financial applications.
This paper proposes two approaches for document-level question answering: a pipeline approach and a confidence-based approach. The pipeline approach selects a single paragraph and extracts an answer from it. The confidence-based approach assigns confidence scores to answers from multiple paragraphs and returns the highest scoring answer. The paper experiments with different training methods for the confidence model and evaluates on several datasets, finding the shared normalization and no-answer option methods perform best. Error analysis shows the model still struggles with connecting statements across sentences and paragraphs.
This webinar will explain the process, methodology, and results used at Apollo Group to evaluate MongoDB and ultimately replace Oracle for a core platform component.
Autometed Online Course Registration System is a software development project final presentation. here , I applyed . and software development waterfall feedback model. Development Software Engineering Project Presentation
The document discusses OSGeo's participation in the 2017 Google Code-in competition. Some key points: - OSGeo had 20 volunteers who mentored over 600 students on tasks for 11 OSGeo projects. - The majority of students were from India and the US and completed tasks in areas like outreach, documentation, and coding. - Mentors provided feedback that it was a learning experience for OSGeo's first time participating and that some students struggled without sufficient guidance. - Lessons included preventing plagiarism, clarifying task requirements, and managing the large time commitment required from mentors.
1) The document proposes a new continual user representation learning method called TERACON that learns from a continuous stream of tasks while retaining knowledge from previous tasks and capturing relationships between tasks. 2) TERACON uses task embeddings to generate relation-aware task-specific masks that maintain learning ability and facilitate capturing task relationships. 3) It prevents "catastrophic forgetting" using a knowledge retention module with pseudo-labeling on past tasks.
Colorado Community College System's CHAMP Instructional Design meeting presentation from Pam Packer and Joe Martin of Red Rocks Community College discussing "flipped" hybrid project-based course design.
This document summarizes the experience of transitioning a biannual face-to-face course on Cloud Computing into a fully online asynchronous course using Cloud Computing tools. The online course saw dramatically more students from more countries complete the course while maintaining a high satisfaction rate. Cloud services from Amazon Web Services, Google Apps, and others were used to deploy remote labs, automate feedback collection, and support over 230 students from 8 countries in an always-open online format. The success of the online transition demonstrates that Cloud services can effectively support high-quality online education at a large scale.
The document summarizes feedback from a survey on using COSMIC (a method for measuring functional size of software) in Agile projects. It finds that while COSMIC can objectively measure size at any level, in practice it is difficult to use at the user story level due to variable requirements quality. However, COSMIC is useful for overall project sizing, estimating, quality control of requirements, and measuring progress. While Agile adoption is increasing, there is a lack of standardization and published information on performance measurement across projects.
This document describes work to develop an auto-grading system for a parallel programming course at Rice University. The key points are: - The current grading process is manual, time-consuming, and provides limited feedback to students. An auto-grading system was developed to address these issues. - The auto-grading system extends the Web-CAT tool to support auto-grading of parallel programs written in Habanero Java (HJ). - Features implemented include correctness testing, performance testing on a cluster, backup of submissions, static analysis tools, and profiling to provide feedback to students. This provides a more efficient, consistent and transparent grading process.
This document describes work to extend the Web-CAT auto-grading system to support a parallel programming course (COMP 322) at Rice University. The extensions allow Web-CAT to automatically grade student submissions on correctness, performance, and code quality. This provides faster feedback to students, reduces grading workload, and enables large online offerings of the course. Key features added include automated testing of parallel programs written in Habanero Java, performance evaluation on a cluster, backup of submissions, static analysis, profiling, and a leaderboard. The goal is to create a transparent, consistent and scalable grading process.
This document summarizes Stefan Mohacsi's 15 years of experience with model-based testing. It describes the evolution of his automated test case generator from early Prolog tools to modern model-based testing tools integrated with test management systems. Key developments included adding support for graphical user interfaces, task flow modeling, requirements tracing, test data generation, and integration with test execution tools. The experience showed that model-based testing improves test efficiency and reduces maintenance costs compared to manual testing when applied correctly with a focus on reusable test models and integration within a test framework.
The document discusses relational knowledge distillation (RKD), a technique for transferring knowledge from a teacher model to a student model. It begins by providing background on knowledge distillation and recent approaches. It then introduces RKD, which transfers relational information between examples in the teacher's embedding space, such as distances and angles, rather than just individual example outputs. The document describes experiments applying RKD to metric learning, image classification, and few-shot learning, finding it improves student model performance over other distillation methods. It concludes RKD effectively leverages relational information to transfer knowledge between models.
Review of Project Management Methodologies for Software Development Life Cycles (SDLC) by Maksym Dovgopolyi, PMP
The document summarizes the end user training programs that the University of Alabama at Birmingham implemented when transitioning from Salesforce Classic to Lightning Experience. It describes a multi-step process including getting buy-in from power users, conducting initial trainings, assigning Trailhead modules for homework, providing additional breakout sessions, and measuring success through usage metrics and feedback. Key aspects of the training covered Trailhead, custom training materials, leveraging Chatter, and tailoring the approach for different user groups like undergraduates and graduates. Overall lessons learned focused on balancing big and small training formats and ensuring training materials stay up to date.
The document discusses various machine learning clustering algorithms like K-means clustering, DBSCAN, and EM clustering. It also discusses neural network architectures like LSTM, bi-LSTM, and convolutional neural networks. Finally, it presents results from evaluating different chatbot models on various metrics like validation score.