The presenter will introduce the K-State LMS data portal and introduce some available insights from there and focus on one particular facet of this big data--the third-party apps that K-State faculty, admin, and staff have activated and what that says about how we're using Canvas.
Canvas LMS data portal for the Kansas State University instance
A data dictionary: Version 1.16.2 (https://portal.inshosteddata.com/docs)
Data extraction and processing
What it can tell us: (un)available data and information
Activated third-party tools in K-State Online Canvas LMS instance
Some caveats
What this says about what K-Staters (early adopters) are using
Practical applications of this third-party app activation data
Adding value to LMS data portal data
Leveraging Flat Files from the Canvas LMS Data Portal at K-StateShalin Hai-Jew
A lot of data are created in an LMS instance, and much of this can be analyzed for insight. In 2016, Instructure, the makers of Canvas, made their LMS data available to their customers through a data portal (updated monthly). This portal enables access to a number of flat files related to that particular instance. This presentation showcases how this big data was analyzed on a regular laptop with basic office software, to summarize Kansas State University’s use of the LMS. Methods for analysis include the following: basic descriptive statistics, survival analysis, computational linguistic analysis, and others.
The results are reported out with both numbers and data visualizations, including classic pie charts, line graphs, bar charts, mixed-charts, word clouds, and others. The findings provide some insights about how to approach the data, how to use a data dictionary, and other methods for extracting the data for awareness and practical decision-making. This work also is suggestive of next steps for more advanced analysis (using the flat files in a SQL database).
More information about this may be accessed at http://scalar.usc.edu/works/c2c-digital-magazine-spring--summer-2017/wrangling-big-data-in-a-small-tech-ecosystem.
When digital learning objects (DLOs) were initially conceptualized, based on object-oriented programming, there were initial high hopes that people could build learning objects that were re-usable by others. DLOs have come a long way in the past few decades, and many are available for free on various repositories, referatories, digital libraries, and other sources. In a recent research project, the presenter explored what features of DLOs make them adoptable for online learning and created a ten-element model for DLO adoption. The reality is that adoption of DLOs is not cost-free and not effort-free. The ten elements include the following categories:
Pedagogical Value
Learner Engagement
Presentational Features
Legal Considerations
Technological Features
Instructor (Adopter) Control
Applicability to the Respective Learning Contexts (Local Conditions)
Local Costs to Deploy
Labeling and Documentation, Contributor and Informational Source Crediting
Global Transferability and Adoptability
She then analyzed her decades of work in instructional design in higher education (and private industry) to see what features were addressed in the respective funded DLOs. She found discrepancies between what makes DLOs adoptable and what is built and suggests some practical ways to close those gaps with techniques and technologies, in order to further support and propel the “digital learning object economy”.
Making the Most of the New File Upload Question Feature in an LMS: Nine Appl...Shalin Hai-Jew
The document discusses 9 scenarios for using the file upload feature in online learning platforms like Canvas and Qualtrics. Scenario 1 involves learners uploading files as proof of completing tasks. Scenario 2 has learners uploading files for web-mediated presentations. Scenario 3 has learners conducting research and collecting data by uploading files. Scenario 4 enhances location-based learning by having learners upload files from their local contexts. Scenario 5 supports collaboration through shared file uploads. Scenario 6 documents live events through uploaded files. Scenarios 7-9 involve simulations, games, creative works and co-building through uploaded files. The document discusses considerations around intellectual property, privacy, data storage and learning from uploaded content.
Using the Qualtrics Research Suite as a Training LMS Shalin Hai-Jew
This presentation discusses using the Qualtrics Research Suite as a training learning management system (LMS) at Kansas State University. Some key features that make Qualtrics useful as a training LMS include its multimedia capabilities, logic functions, branching logic, scoring features, integration with Google Translate, and data analytics dashboards. The presentation provides an overview of the basic needs for automated trainings on a university campus and how Qualtrics meets these needs through its various features. While additional features could improve Qualtrics as a training LMS, it demonstrates potential as an alternative to traditional LMS platforms.
Using Large-Scale LMS Data Portal Data to Improve Teaching and Learning (at K...Shalin Hai-Jew
This document describes an analysis of data from Kansas State University's Canvas learning management system (LMS) to identify ways to improve teaching and learning. The analysis examines data from 79 tables covering topics like courses, assignments, quizzes and enrollment. Preliminary findings note the types and states of courses/sections/assignments/quizzes. Further analysis could investigate relationships between these elements and student outcomes over time to identify best practices and areas for improvement in how the LMS is utilized. The goal is to generate insights that can enhance the university's use of its LMS and integrated tools to support teaching and learning.
Diagramming, Figures, and Imagery (2D): Think Visual in Online LearningShalin Hai-Jew
Learners will…
define “visual thinking” and “visual cognition”
describe some dimensions of visuals in online learning
describe some ways to create visuals in online learning
consider some uses of visuals in online learning
explore legal considerations related to online learning visuals
consider going open-source for visuals
think about signatures and styles in terms of online visuals (and sharing broadly)
contemplate common errors in visualizations for online learning
review ways to think visually
Creating Effective Data Visualizations for Online Learning Shalin Hai-Jew
Virtually every type of online learning involves some type of data visualization. Some common data visualizations include timelines, process diagrams, linegraphs, bar charts, pie charts, treemap diagrams, dendrograms, cluster diagrams, geographical maps, network graphs, word clouds, word networks, scatter diagrams, scatterplot matrices, intensity matrices, decision trees, and others. Indeed, there is also data in screenshots, photos, drawings, videos, or other types of visuals. Online dashboards contain rich data visualizations to convey dynamic data. Some data, such as big data, may only be conveyed in visuals for human understanding and interpretation; in raw form, the meaning is obscured and elusive. Data visualizations highlight salient aspects of data, and they have to be aligned for particular multi-uses: (1) user awareness and understanding, (2) data analytics, and (3) decision-making. This session defines some best practices for informative and engaging data visualizations for online learning. Original real-world examples are provided from modern software programs.
Creating Effective Data Visualizations in Excel 2016: Some BasicsShalin Hai-Jew
One of the mainstays of a modern software toolkit is Excel 2016, from Microsoft Office 2016. By reputation, Excel is considered a beginner’s tool that self-respecting data analysts would bypass, but Excel is fairly high-powered, can take up to 1.06 million rows of data per set, contains complex statistical analysis capabilities (without the need for scripting), and enables rich data visualizations. It has a number of rich add-ons to empower different analytical and data visualization functionalities. It works as a great bridging tool to more complex types of statistical analyses.
This session walks participants through some basic built-in data visualizations in Excel 2016, including pie charts and doughnuts, bar charts, tree maps and sunburst diagrams, cluster diagrams, spider (radar) charts, scattergraphs, and others. This session will cover how data structures and desired emphases will determine the options for particular data visualizations.
In this session, participants will
review how to load a data table,
read the general data in a data table (or worksheet),
process or clean the data as needed,
use the Recommended Charts feature,
decide which built-in data visualizations to use, and
consider how to add relevant data visualization elements (including data labels, background grids, axis labels, and titles) for a coherent and effective data visualization.
Also, participants will help co-build data visualizations from open-source and other datasets.
Leveraging Flat Files from the Canvas LMS Data Portal at K-StateShalin Hai-Jew
A lot of data are created in an LMS instance, and much of this can be analyzed for insight. In 2016, Instructure, the makers of Canvas, made their LMS data available to their customers through a data portal (updated monthly). This portal enables access to a number of flat files related to that particular instance. This presentation showcases how this big data was analyzed on a regular laptop with basic office software, to summarize Kansas State University’s use of the LMS. Methods for analysis include the following: basic descriptive statistics, survival analysis, computational linguistic analysis, and others.
The results are reported out with both numbers and data visualizations, including classic pie charts, line graphs, bar charts, mixed-charts, word clouds, and others. The findings provide some insights about how to approach the data, how to use a data dictionary, and other methods for extracting the data for awareness and practical decision-making. This work also is suggestive of next steps for more advanced analysis (using the flat files in a SQL database).
More information about this may be accessed at http://scalar.usc.edu/works/c2c-digital-magazine-spring--summer-2017/wrangling-big-data-in-a-small-tech-ecosystem.
When digital learning objects (DLOs) were initially conceptualized, based on object-oriented programming, there were initial high hopes that people could build learning objects that were re-usable by others. DLOs have come a long way in the past few decades, and many are available for free on various repositories, referatories, digital libraries, and other sources. In a recent research project, the presenter explored what features of DLOs make them adoptable for online learning and created a ten-element model for DLO adoption. The reality is that adoption of DLOs is not cost-free and not effort-free. The ten elements include the following categories:
Pedagogical Value
Learner Engagement
Presentational Features
Legal Considerations
Technological Features
Instructor (Adopter) Control
Applicability to the Respective Learning Contexts (Local Conditions)
Local Costs to Deploy
Labeling and Documentation, Contributor and Informational Source Crediting
Global Transferability and Adoptability
She then analyzed her decades of work in instructional design in higher education (and private industry) to see what features were addressed in the respective funded DLOs. She found discrepancies between what makes DLOs adoptable and what is built and suggests some practical ways to close those gaps with techniques and technologies, in order to further support and propel the “digital learning object economy”.
Making the Most of the New File Upload Question Feature in an LMS: Nine Appl...Shalin Hai-Jew
The document discusses 9 scenarios for using the file upload feature in online learning platforms like Canvas and Qualtrics. Scenario 1 involves learners uploading files as proof of completing tasks. Scenario 2 has learners uploading files for web-mediated presentations. Scenario 3 has learners conducting research and collecting data by uploading files. Scenario 4 enhances location-based learning by having learners upload files from their local contexts. Scenario 5 supports collaboration through shared file uploads. Scenario 6 documents live events through uploaded files. Scenarios 7-9 involve simulations, games, creative works and co-building through uploaded files. The document discusses considerations around intellectual property, privacy, data storage and learning from uploaded content.
Using the Qualtrics Research Suite as a Training LMS Shalin Hai-Jew
This presentation discusses using the Qualtrics Research Suite as a training learning management system (LMS) at Kansas State University. Some key features that make Qualtrics useful as a training LMS include its multimedia capabilities, logic functions, branching logic, scoring features, integration with Google Translate, and data analytics dashboards. The presentation provides an overview of the basic needs for automated trainings on a university campus and how Qualtrics meets these needs through its various features. While additional features could improve Qualtrics as a training LMS, it demonstrates potential as an alternative to traditional LMS platforms.
Using Large-Scale LMS Data Portal Data to Improve Teaching and Learning (at K...Shalin Hai-Jew
This document describes an analysis of data from Kansas State University's Canvas learning management system (LMS) to identify ways to improve teaching and learning. The analysis examines data from 79 tables covering topics like courses, assignments, quizzes and enrollment. Preliminary findings note the types and states of courses/sections/assignments/quizzes. Further analysis could investigate relationships between these elements and student outcomes over time to identify best practices and areas for improvement in how the LMS is utilized. The goal is to generate insights that can enhance the university's use of its LMS and integrated tools to support teaching and learning.
Diagramming, Figures, and Imagery (2D): Think Visual in Online LearningShalin Hai-Jew
Learners will…
define “visual thinking” and “visual cognition”
describe some dimensions of visuals in online learning
describe some ways to create visuals in online learning
consider some uses of visuals in online learning
explore legal considerations related to online learning visuals
consider going open-source for visuals
think about signatures and styles in terms of online visuals (and sharing broadly)
contemplate common errors in visualizations for online learning
review ways to think visually
Creating Effective Data Visualizations for Online Learning Shalin Hai-Jew
Virtually every type of online learning involves some type of data visualization. Some common data visualizations include timelines, process diagrams, linegraphs, bar charts, pie charts, treemap diagrams, dendrograms, cluster diagrams, geographical maps, network graphs, word clouds, word networks, scatter diagrams, scatterplot matrices, intensity matrices, decision trees, and others. Indeed, there is also data in screenshots, photos, drawings, videos, or other types of visuals. Online dashboards contain rich data visualizations to convey dynamic data. Some data, such as big data, may only be conveyed in visuals for human understanding and interpretation; in raw form, the meaning is obscured and elusive. Data visualizations highlight salient aspects of data, and they have to be aligned for particular multi-uses: (1) user awareness and understanding, (2) data analytics, and (3) decision-making. This session defines some best practices for informative and engaging data visualizations for online learning. Original real-world examples are provided from modern software programs.
Creating Effective Data Visualizations in Excel 2016: Some BasicsShalin Hai-Jew
One of the mainstays of a modern software toolkit is Excel 2016, from Microsoft Office 2016. By reputation, Excel is considered a beginner’s tool that self-respecting data analysts would bypass, but Excel is fairly high-powered, can take up to 1.06 million rows of data per set, contains complex statistical analysis capabilities (without the need for scripting), and enables rich data visualizations. It has a number of rich add-ons to empower different analytical and data visualization functionalities. It works as a great bridging tool to more complex types of statistical analyses.
This session walks participants through some basic built-in data visualizations in Excel 2016, including pie charts and doughnuts, bar charts, tree maps and sunburst diagrams, cluster diagrams, spider (radar) charts, scattergraphs, and others. This session will cover how data structures and desired emphases will determine the options for particular data visualizations.
In this session, participants will
review how to load a data table,
read the general data in a data table (or worksheet),
process or clean the data as needed,
use the Recommended Charts feature,
decide which built-in data visualizations to use, and
consider how to add relevant data visualization elements (including data labels, background grids, axis labels, and titles) for a coherent and effective data visualization.
Also, participants will help co-build data visualizations from open-source and other datasets.
Education must capitalize on the trend within technology toward big data. New types of data are becoming available. From evidence approaches to xAPI and the whole Training and Learning Architecture(TLA) big data is the foundation of all.
Semantic Relatedness of Web Resources by XESA - Philipp SchollCROKODIl consortium
This document discusses using extended explicit semantic analysis (XESA) to measure semantic relatedness between short text snippets for recommendation purposes. It proposes enhancing ESA by incorporating additional semantic information from Wikipedia, such as article links and categories. An evaluation compares the performance of ESA, XESA using the article graph, XESA using categories, and a combination. The results show that XESA using the article graph improves over ESA by up to 9% and performs best for recommending related snippets.
This study analyzed the use of the Moodle e-learning platform at the University of Aveiro in Portugal. A questionnaire was administered to 278 students to characterize their use of Moodle. The results showed that students primarily use Moodle as a repository to download course materials, with an average of 49 accesses per month. While students recognized the importance of communication tools for learning, these tools were underutilized. Overall, Moodle had potential but was not fully leveraged for its interactive features to enhance teaching and learning.
LOR Characteristics and ConsiderationsScott Leslie
The document summarizes the findings of a research project that evaluated 6 different learning object repository (LOR) products. It discusses some of the issues with LORs, such as their immaturity as a technology and market. It provides high-level summaries of the 6 products reviewed, noting their main strengths and weaknesses. Overall, it finds the products generally support search/browse but lack features like syndication, community/evaluation, and content aggregation. It concludes that the best LOR solution depends on how the problem is defined and what existing systems are in place.
Involving students in managing their own learningeLearning Papers
The primary function of universities is to equip students with the knowledge and skills they need to prosper throughout their professional career. Today, to be successful, students will need to continually enhance their knowledge and skills, in order to address immediate problems and to participate in a process of continuing vocational and professional development.
Authors: Malinka Ivanova, Tatyana Ivanova
Knowledge Building and Competence Development in eLearning 2.0 SystemsMalinka Ivanova
The document discusses eLearning 2.0 systems and their ability to support knowledge building and competence development. It outlines key aspects of eLearning 2.0 including user-generated content, collaboration, and use of various online tools. The document then analyzes several eLearning 2.0 systems based on their knowledge capturing, sharing, and communication features. It compares these systems to the IEEE Learning Technology Systems Architecture standard.
The document discusses virtual reference services that libraries can offer. It defines virtual reference as delivering personalized reference resources to users outside the physical library. Some benefits of offering virtual reference include helping achieve the library's mission, reaching more users, and providing resources regardless of location or time. Virtual reference can be delivered through various formats like email, chat, webliographies and frequently asked questions pages. The document also discusses technologies, software options and costs associated with setting up and maintaining virtual reference services.
Open management education and social software20110407Jan Pawlowski
how to use open content / open educational resources for management education using social software tool? OpenScout (www.openscout.net) provides access to thousands of hours to freely available management contents - we discuss how to utilize social software in learning scenarios as well as for the adaptation of learning materials
Combination of resource based learning with instructional designed and collab...CROKODIl consortium
The document discusses the CROKODIL learning platform, which combines resource-based learning, instructional design, and collaborative learning. It describes how the platform supports self-organized learning through resource networks and recommendation tools. The goal is to help learners effectively search, organize, annotate and share resources to support their knowledge acquisition.
Managing lifelong learning records through blockchain. Patrick Ocheja , Brend...eraser Juan José Calderón
Managing lifelong learning records through blockchain de Patrick Ocheja , Brendan Flanagan, Hiroshi Ueda and Hiroaki Ogata publicado en Research and Practice in Technology Enhanced Learning (2019) 14:4
The document discusses DAX LLC's proprietary distributed autonomous classroom platform. It aims to revolutionize eLearning by building on a private blockchain with user-guided adaptation. Key features include students learning at their own pace from lessons, peers and industry experts. Teachers are rated in real time and course content is hybridized from core lessons and student/expert contributions. The system utilizes blockchain and cryptocurrency techniques to automate grading and incentivize high quality content while maintaining student/teacher accountability.
This document defines key terms related to online education systems. It discusses terms like online education, e-learning, learning management systems, student management systems, and integrated online education systems. It also presents the Jigsaw and Hub models for how these different systems interact and exchange data. The goal is to establish a common framework and definitions for understanding online education projects.
Learning Analaytics and Information Visualizationmetamath
Learning analytics and information visualization tools can provide valuable insights into student learning and participation during online courses. The presentation described tools that can generate summaries of student activity, participation networks, connections between students and course materials, and relationships between participation levels and academic performance. Visualizations are shown that allow filtering of the data in these ways. The tools aim to provide information to both students and teachers to help identify at-risk students, motivate participation, and evaluate instructional design.
How could Open Badges Transform ePortfolio Practices and Technologies! Serge Ravet
Looking at the history of ePortfolio practice and technologies over the last 10 years, one is entitled in asking: what has changed? Is the ePortfolio technology we have today that different from what we had 10 years ago? While there is certainly a wider spread of ePortfolios, have ePortfolios transformed practice or been assimilated by institutions?
Open Badges are the opportunity to reinvent ePortfolio technology and practice, and create the conditions for an effective shift of the locus of power from institutions to individuals and communities. Shall we be able to seize this opportunity?
This document discusses e-learning technologies and systems. It describes communication technologies used in e-learning like asynchronous tools like blogs and discussion boards and synchronous tools like chat sessions. It also discusses learning management systems, collaborative software, pedagogical approaches to e-learning, and interaction models in e-learning systems. Architectural examples of service-oriented e-learning systems are provided. A virtual learning environment demo is also referenced.
Big data integration for transition from e-learning to smart learning framework eraser Juan José Calderón
Big data integration for transition from e-learning to smart learning framework . Dr. Prakash Kumar Udupi Mr. Puttaswamy Malali Mr. Herald Noronha Department of Computing Department of Computing Department of Computing Middle East College Middle East College Middle East College .
The Social Semantic Server - A Flexible Framework to Support Informal Learnin...Sebastian Dennerlein
The document describes the Social Semantic Server (SSS), a flexible framework developed to support informal learning in workplace settings. The SSS was designed based on theories of distributed cognition and meaning making to facilitate collaboration and knowledge sharing through artifacts. It implements a service-oriented architecture with various microservices to integrate tools for informal learning. Examples of tools built on the SSS include Bits & Pieces for sensemaking experiences, KnowBrain for collaborative discussions, and Bookmarker/Attacher for exploring topics. The SSS aims to provide a technical infrastructure that supports meaning making during artifact-mediated communication in the workplace.
This document discusses whether employers look at and consult ePortfolios. It provides several pieces of evidence that suggest employers are interested in ePortfolios and the skills and competencies they demonstrate:
- A 2013 survey found that over 80% of employers said an electronic portfolio would be useful in ensuring job applicants have the necessary knowledge and skills.
- Focus groups with employers found that many would be willing to view an ePortfolio via a link in an email, resume, or interview.
- Research with employer focus groups found that the majority view ePortfolios favorably as a way for candidates to describe their skills and experiences through accessible evidence of accomplishments.
- Benefits cited by employers include ePortfolios
2016 Building Bridges - Need for a Data Management StrategyBrad Bronsch
The document discusses the need for institutions to have a data management strategy. It notes the challenges of integrating data across different systems used by institutions for student information, learning, housing, and other functions. The document recommends adopting an enterprise data integration platform to standardize how data is accessed and moved between systems. It provides an example of how the Talend platform can be used to integrate weather data from the NOAA API with data in a database, demonstrating the platform's functionality and ease of use. The document concludes that data integration is key to a successful data management strategy.
Academic Innovation Data Showcase 2-14-19umichiganai
On Thursday, February 14 from 9:30 a.m. to 12:00 p.m. the Office of Academic Innovation hosted our first Data Showcase - an event for all University of Michigan (U-M) community members to come take a tour through the data that power our work.
Education must capitalize on the trend within technology toward big data. New types of data are becoming available. From evidence approaches to xAPI and the whole Training and Learning Architecture(TLA) big data is the foundation of all.
Semantic Relatedness of Web Resources by XESA - Philipp SchollCROKODIl consortium
This document discusses using extended explicit semantic analysis (XESA) to measure semantic relatedness between short text snippets for recommendation purposes. It proposes enhancing ESA by incorporating additional semantic information from Wikipedia, such as article links and categories. An evaluation compares the performance of ESA, XESA using the article graph, XESA using categories, and a combination. The results show that XESA using the article graph improves over ESA by up to 9% and performs best for recommending related snippets.
This study analyzed the use of the Moodle e-learning platform at the University of Aveiro in Portugal. A questionnaire was administered to 278 students to characterize their use of Moodle. The results showed that students primarily use Moodle as a repository to download course materials, with an average of 49 accesses per month. While students recognized the importance of communication tools for learning, these tools were underutilized. Overall, Moodle had potential but was not fully leveraged for its interactive features to enhance teaching and learning.
LOR Characteristics and ConsiderationsScott Leslie
The document summarizes the findings of a research project that evaluated 6 different learning object repository (LOR) products. It discusses some of the issues with LORs, such as their immaturity as a technology and market. It provides high-level summaries of the 6 products reviewed, noting their main strengths and weaknesses. Overall, it finds the products generally support search/browse but lack features like syndication, community/evaluation, and content aggregation. It concludes that the best LOR solution depends on how the problem is defined and what existing systems are in place.
Involving students in managing their own learningeLearning Papers
The primary function of universities is to equip students with the knowledge and skills they need to prosper throughout their professional career. Today, to be successful, students will need to continually enhance their knowledge and skills, in order to address immediate problems and to participate in a process of continuing vocational and professional development.
Authors: Malinka Ivanova, Tatyana Ivanova
Knowledge Building and Competence Development in eLearning 2.0 SystemsMalinka Ivanova
The document discusses eLearning 2.0 systems and their ability to support knowledge building and competence development. It outlines key aspects of eLearning 2.0 including user-generated content, collaboration, and use of various online tools. The document then analyzes several eLearning 2.0 systems based on their knowledge capturing, sharing, and communication features. It compares these systems to the IEEE Learning Technology Systems Architecture standard.
The document discusses virtual reference services that libraries can offer. It defines virtual reference as delivering personalized reference resources to users outside the physical library. Some benefits of offering virtual reference include helping achieve the library's mission, reaching more users, and providing resources regardless of location or time. Virtual reference can be delivered through various formats like email, chat, webliographies and frequently asked questions pages. The document also discusses technologies, software options and costs associated with setting up and maintaining virtual reference services.
Open management education and social software20110407Jan Pawlowski
how to use open content / open educational resources for management education using social software tool? OpenScout (www.openscout.net) provides access to thousands of hours to freely available management contents - we discuss how to utilize social software in learning scenarios as well as for the adaptation of learning materials
Combination of resource based learning with instructional designed and collab...CROKODIl consortium
The document discusses the CROKODIL learning platform, which combines resource-based learning, instructional design, and collaborative learning. It describes how the platform supports self-organized learning through resource networks and recommendation tools. The goal is to help learners effectively search, organize, annotate and share resources to support their knowledge acquisition.
Managing lifelong learning records through blockchain. Patrick Ocheja , Brend...eraser Juan José Calderón
Managing lifelong learning records through blockchain de Patrick Ocheja , Brendan Flanagan, Hiroshi Ueda and Hiroaki Ogata publicado en Research and Practice in Technology Enhanced Learning (2019) 14:4
The document discusses DAX LLC's proprietary distributed autonomous classroom platform. It aims to revolutionize eLearning by building on a private blockchain with user-guided adaptation. Key features include students learning at their own pace from lessons, peers and industry experts. Teachers are rated in real time and course content is hybridized from core lessons and student/expert contributions. The system utilizes blockchain and cryptocurrency techniques to automate grading and incentivize high quality content while maintaining student/teacher accountability.
This document defines key terms related to online education systems. It discusses terms like online education, e-learning, learning management systems, student management systems, and integrated online education systems. It also presents the Jigsaw and Hub models for how these different systems interact and exchange data. The goal is to establish a common framework and definitions for understanding online education projects.
Learning Analaytics and Information Visualizationmetamath
Learning analytics and information visualization tools can provide valuable insights into student learning and participation during online courses. The presentation described tools that can generate summaries of student activity, participation networks, connections between students and course materials, and relationships between participation levels and academic performance. Visualizations are shown that allow filtering of the data in these ways. The tools aim to provide information to both students and teachers to help identify at-risk students, motivate participation, and evaluate instructional design.
How could Open Badges Transform ePortfolio Practices and Technologies! Serge Ravet
Looking at the history of ePortfolio practice and technologies over the last 10 years, one is entitled in asking: what has changed? Is the ePortfolio technology we have today that different from what we had 10 years ago? While there is certainly a wider spread of ePortfolios, have ePortfolios transformed practice or been assimilated by institutions?
Open Badges are the opportunity to reinvent ePortfolio technology and practice, and create the conditions for an effective shift of the locus of power from institutions to individuals and communities. Shall we be able to seize this opportunity?
This document discusses e-learning technologies and systems. It describes communication technologies used in e-learning like asynchronous tools like blogs and discussion boards and synchronous tools like chat sessions. It also discusses learning management systems, collaborative software, pedagogical approaches to e-learning, and interaction models in e-learning systems. Architectural examples of service-oriented e-learning systems are provided. A virtual learning environment demo is also referenced.
Big data integration for transition from e-learning to smart learning framework eraser Juan José Calderón
Big data integration for transition from e-learning to smart learning framework . Dr. Prakash Kumar Udupi Mr. Puttaswamy Malali Mr. Herald Noronha Department of Computing Department of Computing Department of Computing Middle East College Middle East College Middle East College .
The Social Semantic Server - A Flexible Framework to Support Informal Learnin...Sebastian Dennerlein
The document describes the Social Semantic Server (SSS), a flexible framework developed to support informal learning in workplace settings. The SSS was designed based on theories of distributed cognition and meaning making to facilitate collaboration and knowledge sharing through artifacts. It implements a service-oriented architecture with various microservices to integrate tools for informal learning. Examples of tools built on the SSS include Bits & Pieces for sensemaking experiences, KnowBrain for collaborative discussions, and Bookmarker/Attacher for exploring topics. The SSS aims to provide a technical infrastructure that supports meaning making during artifact-mediated communication in the workplace.
This document discusses whether employers look at and consult ePortfolios. It provides several pieces of evidence that suggest employers are interested in ePortfolios and the skills and competencies they demonstrate:
- A 2013 survey found that over 80% of employers said an electronic portfolio would be useful in ensuring job applicants have the necessary knowledge and skills.
- Focus groups with employers found that many would be willing to view an ePortfolio via a link in an email, resume, or interview.
- Research with employer focus groups found that the majority view ePortfolios favorably as a way for candidates to describe their skills and experiences through accessible evidence of accomplishments.
- Benefits cited by employers include ePortfolios
2016 Building Bridges - Need for a Data Management StrategyBrad Bronsch
The document discusses the need for institutions to have a data management strategy. It notes the challenges of integrating data across different systems used by institutions for student information, learning, housing, and other functions. The document recommends adopting an enterprise data integration platform to standardize how data is accessed and moved between systems. It provides an example of how the Talend platform can be used to integrate weather data from the NOAA API with data in a database, demonstrating the platform's functionality and ease of use. The document concludes that data integration is key to a successful data management strategy.
Academic Innovation Data Showcase 2-14-19umichiganai
On Thursday, February 14 from 9:30 a.m. to 12:00 p.m. the Office of Academic Innovation hosted our first Data Showcase - an event for all University of Michigan (U-M) community members to come take a tour through the data that power our work.
Confirming PagesLess managing. More teaching. Greater AlleneMcclendon878
Confirming Pages
Less managing. More teaching. Greater learning.
INSTRUCTORS GET:
• Interactive Applications – book-specific interactive
assignments that require students to APPLY what
they’ve learned.
• Simple assignment management, allowing you to
spend more time teaching.
• Auto-graded assignments, quizzes, and tests.
• Detailed Visual Reporting where student and
section results can be viewed and analyzed.
• Sophisticated online testing capability.
• A filtering and reporting function
that allows you to easily assign and
report on materials that are correlated
to accreditation standards, learning
outcomes, and Bloom’s taxonomy.
• An easy-to-use lecture capture tool.
Would you like your students to show up for class more prepared? (Let’s face it, class
is much more fun if everyone is engaged and prepared…)
Want ready-made application-level interactive assignments, student progress
reporting, and auto-assignment grading? (Less time grading means more time teaching…)
Want an instant view of student or class performance relative to learning
objectives? (No more wondering if students understand…)
Need to collect data and generate reports required for administration or
accreditation? (Say goodbye to manually tracking student learning outcomes…)
Want to record and post your lectures for students to view online?
INSTRUCTORS...
With McGraw-Hill's Connect® MIS,
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Management Information Systems
FOR THE INFORMATION AGE
NINTH EDITION
Stephen Haag
DANIELS COLLEGE OF BUSINESS
UNIVERSITY OF DENVER
Maeve Cummings
KELCE COLLEGE OF BUSINESS
PITTSBURG STATE UNIVERSITY
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MANAGEMENT INFORMATION SYSTEMS FOR THE INF ...
This document outlines the roles and responsibilities within an organizational chart for an instructional technology department at a university. It includes roles such as the director of educational technology, assistant directors of application support and instructional design, instructional designers, web developers, and various support roles. The justification section explains that successful distance education programs require adequate support structures at multiple levels to develop infrastructure, design programs, provide training and support, and maintain the programs. Project teams need various roles and a clear understanding of objectives from stakeholders.
This document outlines the curriculum for VCE IT 2014, including 4 units covering topics like data analysis, software development, networks, and careers involving information and communication technologies. Each unit includes 3 areas of study. Area of Study 1 focuses on processes like selecting and analyzing data to create visualizations. Area of Study 2 involves programming, databases, and career pathways. Area of Study 3 addresses collaboration, problem-solving methodologies, and contemporary ICT issues. Students apply a problem-solving methodology across stages of analysis, design, development and evaluation in their projects. They use a variety of software tools to complete solutions meeting specific purposes.
This document provides an introduction to SQL and databases. It discusses the proliferation of data and importance of databases. Key topics covered include different types of databases, the components of a database system including the DBMS, and the functions of a DBMS. The document traces the evolution of databases from manual file systems to integrated database management systems and discusses important database terminology like metadata and relationships. It also emphasizes the importance of database design.
Easy Analytics on AWS with Amazon Redshift, Amazon QuickSight, and Amazon Mac...Amazon Web Services
AWS has a large and growing portfolio of big data management and analytics services, designed to be integrated into solution architectures that meet the needs of your business. In this session, we look at analytics through the eyes of a business intelligence analyst, a data scientist, and an application developer, and we explore how to quickly leverage Amazon Redshift, Amazon QuickSight, RStudio, and Amazon Machine Learning to create powerful, yet straightforward, business solutions.
Santosh Boggavarapu has over 9 years of experience in database development and design. He currently works as a Senior Analyst at BA Continuum India, where he develops mortgage trading tools using technologies like SQL Server, Netezza, and XML. Previously, he worked as a Tech Lead at Tech Mahindra on projects involving Dow Jones risk compliance solutions and a credit information service for Mastercard banks in India. He has extensive experience with databases, ETL, and performance tuning.
AN EMPIRICAL STUDY OF USING CLOUD-BASED SERVICES IN CAPSTONE PROJECT DEVELOPMENTcsandit
Cloud computing is gaining prominence and popularity in three important forms: Software as a Service, Platform as a Service, and Infrastructure as a Service. In this paper, we will present
an empirical study of how these cloud-based services were used in an undergraduate Computer Science capstone class to enable agile and effective development, testing, and deployment of sophisticated software systems, facilitate team collaborations among students, and ease the project assessment and grading tasks for teachers. Especially, in this class, students and teachers could leverage time, talent, and resources collaboratively and distributedly on his/her own schedule, from his/her convenient location, and using heterogeneous programming platforms thanks to such a completely All-In-Cloud environment, which eliminated the necessity of spending valuable development time on local setup, configuration, and maintenance, streamlined version control and group management, and greatly increased the collective productivity of student groups. Despite of the relatively steep learning curve in the beginning of the semester, all nine groups of students benefitted tremendously from such an All-In-Cloud experience and eight of them completed their substantial software projects successfully. This paper is concluded with a vision on expandin and standardizing the adoption of the Cloud ecosystem in other Computer Science classes in the future.
EasySOA business case and real world use case 20130220Marc Dutoo
EasySOA is a lightweight SOA governance solution that provides a non-intrusive layer over existing SOA implementations to improve governance. It utilizes a collaborative document management platform like Nuxeo to store SOA models, specifications, and other documents. This includes business concepts, technical specifications of services, and deployment information. It also facilitates automated discovery of services and their documentation from code to integrate information from multiple teams. EasySOA aims to improve visibility and sharing of SOA assets without burdening teams with new tools or processes.
Maruti Gollapudi has over 17 years of experience as a principal architect, specializing in digital customer experience. Some of his significant contributions include developing a data aggregation and analytics platform hosted on AWS that enables capabilities like social analytics, text analytics using NLP and machine learning, and enterprise search. He has experience building solutions leveraging technologies such as Java, JBoss, Kafka, MongoDB, Solr, Watson, and various analytics and social APIs. Recent projects include developing a headless CMS for page building and dynamic content modification for CNBC, and architecting a middleware for CNBC's integration with Uber to dynamically serve ride-related content.
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Yael Garten
2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047
Description:
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.
If You want This Project Entittled "JPS-School Management System"
Contact - Sarthak Khabiya
Email :-sarthakkhabiya@gmail.com
Contact Number - +91-8717912597
This document discusses Microsoft training and certifications for a software career. It outlines several popular Microsoft courses including Dot.Net, SharePoint, SQL Server DBA, MSBI, and Dynamics AX. It notes the advantages of online Microsoft training such as flexibility to learn anywhere anytime, increased student interaction and enrichment, and cost savings over traditional classes. The document promotes Training Icon as an institution that offers these Microsoft courses online.
Notespane - A community based learning systemIRJET Journal
Notespane is an e-learning platform that allows users to efficiently share and access study materials through notes. It provides features like notes, quizzes, a planner, scheduler, calculator, help guide, and background music to facilitate learning. The system is built with React for the frontend, Spring Boot for the backend, and AWS services for database management. It aims to create a community of lifelong learners by providing an integrated platform for sharing knowledge through notes in various formats like text, PDFs, presentations, videos and links.
David Nilsen is seeking a position as a project/program manager with over 23 years of experience managing complex software development projects for the US military. He has managed over 65 projects and led teams of up to 22 people. Nilsen has extensive experience designing and developing learning management systems, mobile applications, and interactive multimedia instruction for the Army, Navy, Marines and service academies. He is proficient in Agile development practices and creating flexible architectures to allow for expanded functionality.
Thomas Dang has over 10 years of experience as a software architect and programmer analyst at the University of British Columbia where he has implemented security measures like 2-factor authentication and vulnerability testing to harden applications. He has also integrated applications with cloud services and identity management systems. As a data analyst, he led teams to analyze confidential data and design visual tools to help clients. Previously, he worked as a software engineer at Electronic Arts where he engineered features for games and prototyped new concepts. He is skilled in programming, automation, cloud services, integration, and ensuring usability, documentation, and policy compliance.
Similar to The K-State Online Canvas LMS Data Portal and Five Years of Activated Third-Party Apps (20)
Long nonfiction chapters are not in-style and may never have been. Where average chapter lengths of nonfiction book chapters are about 4,000 – 7,000 words in length, some may be several times that max range number. The explanation is that there is some irreducible complexity that that chapter addresses that cannot be addressed in shorter form. This slideshow explores some methods for writing longer chapters while still maintaining coherence, focus, and reader interest…and while using some technological tools to write and edit more efficiently.
Overcoming Reluctance to Pursuing Grant Funds in AcademiaShalin Hai-Jew
Starting as an organization’s new grant writer can be a challenge, especially in a case where there has been a time lapse since the last one left. People get out of the habit of pursuing grant funds. This slideshow addresses some of the reasons for such reluctance and proposes some ways to mitigate these.
Writing grants is one common way that those in institutions of higher education may acquire some funds—small and big, one-off and continuing—to conduct research, hire faculty and researchers and learners and others, update equipment, update or build up new buildings, and achieve other work. This slideshow explores some aspects of the work of grant writing in the present moment in higher education.
Contrasting My Beginner Folk Art vs. Machine Co-Created Folk Art with an Art-...Shalin Hai-Jew
This document contrasts handmade folk art with machine-generated folk art created with an AI system. Handmade art involves material costs, learning over time, and serendipity, while machine art is more efficient but relies on the system's tendencies. Both can be used for self-expression, stress relief, and entertainment. However, handmade art may better support poetry, visual exploration, and thinking while machine art excels at structure, cultural references, and finding online audiences. The author views machine-assisted art as a collaboration that should augment but not replace manual skills.
Creating Seeding Visuals to Prompt Art-Making Generative AIsShalin Hai-Jew
Art-making generative AIs have come to the fore. A basic work pipeline typically involves starting with text prompts -> generated images. That image may be used to seed further iterations. Deep Dream Generator (DDG) enables the application of “modifiers” of various types (artist styles, visual adjectives, others) to be applied in addition to the text prompt.
Another approach involves beginning with a “seeding image,” a born-digital or digitized (born-analog) visual on which AI-generated art may be based for a multi-channel and multi-modal prompt. This slideshow provides some observations of how to think about seeding images, particularly in terms of how the DDG handles them, with its “algorithmic pareidolia” (“Deep Dream,” Wikipedia, July 3, 2023).
Human art-making is often about throwing mass-scale conversations. Artists are thought to help bridge humanity into the future. Whether generative AI art enables this or not is still not clear.
Multimodal “Art”-Making Generative AIs
Generative AI encompasses a broad range of computational technologies that emulate human intelligence across many domains including natural language processing, speech recognition, vision systems, gameplay, art creation, decision making, robotics and more. Generative AIs can be prompted through text, images or other modalities to create novel works based on their training data. Prompt engineering involves refining prompts to steer the AI's output. While generative AIs show promise for human-machine collaboration and art-making, challenges remain regarding factuality, derivative works, and achieving refined output.
Digital templates can provide structure for inputting information and also enable additional functionality like autocompletion, auto-correction, and dynamic layouts. Templates may be shared broadly and used in various applications. They are designed forms that can be created using a top-down or bottom-up approach and should be tested and evolved over time. Common examples of templates in higher education include forms, organizers, manuscripts, slideshows, videos, and digital learning objects.
In qualitative data analytics, computation is seen as complementing the work of human researchers by bolstering data analysis. Qualitative data analysis tools enable various types of computational analysis of both structured and unstructured data, including text analysis, visualization, and machine learning techniques. However, human researchers still play an important role in curating data and developing codebooks to guide both human and computational analysis of the data.
Common Neophyte Academic Book Manuscript Reviewer MistakesShalin Hai-Jew
1) Academic book reviewing is a common but often unpaid volunteer role that requires experience to avoid mistakes.
2) Neophyte or inexperienced reviewers must understand publishing context, ask relevant questions of manuscripts, and maintain impartiality and confidentiality.
3) Reviewers should approach their role with empathy, recognizing authors' challenges and investing time in preparation, while upholding quality standards to benefit authors, publishers, and disciplines.
Fashioning Text (and Image) Prompts for the CrAIyon Art-Making Generative AIShalin Hai-Jew
CrAIyon (formerly DALL-E after Salvador “Dali”) is a web-facing art-making generative AI tool online (https://www.craiyon.com/) that enables the uses of text (and image) prompts for the creation of watermarked, lightweight visuals. Counterintuitively, the rough visuals are much more usable for recombinations and remixes and recreations into usable digital visuals for various digital learning objects. The textual prompts are not particularly intuitive because of how the generative AI program was trained on mass-scale visuals). There is an art and occasional indirection to working prompts after each try, with the resulting nine-image proof sheets that CrAIyon outputs. The tool can be used iteratively for different outputs.
The tool sometimes turns out serendipitous surprises, including an occasional work so refined that it can be used / shared almost unedited. One challenge in using CrAIyon comes from their request for credit (for all non-subscribers to their service). Another comes from the visual watermarking (orange crayon at the bottom right of the image). However, this tool is quite useful for practical applications if one is willing to engage deep digital image editing (Adobe Photoshop, Adobe Illustrator).
Augmented Reality in Multi-Dimensionality: Design for Space, Motion, Multiple...Shalin Hai-Jew
Augmented reality (AR)—the use of digital overlays over physical space—manifests in a wide range of spaces (indoor, outdoor; virtual) and ways (in real space (with unaided human vision); in head gear; in smart glasses; on mobile devices, and others). There are various authoring technologies that enable the making of AR experiences for various users. This work uses a particular tool (Adobe Aero®) to explore ways to build AR for multiple dimensions, including the fourth dimension (motion, changes over time).
Based on the respective purposes of the AR experience, some basic heuristics are captured for
space design (1),
motion design (2),
multiple perception design (sight, smell, taste, sound, touch) (3),
and virtual- and tangible- interactivity (4).
The document provides an overview of the Adobe Aero training session, including pre-training, during training, and post-training steps. It then details the two hours of training, which include an introduction to augmented reality and the Adobe Aero app. Key concepts around AR like file types, scale, field of view, interaction design, and uses for teaching and learning are explained. The document outlines a simplified workflow for designing mobile AR experiences for education.
Some Ways to Conduct SoTL Research in Augmented Reality (AR) for Teaching and...Shalin Hai-Jew
One of the extant questions about augmented reality (AR) is how (in)effective it is for the teaching and learning in various formal, nonformal, and informal contexts. The research literature shows mixed findings, which are often highly context-based (and not generalizable). There are some non-trivial costs to the design/development/deployment of AR for teaching and learning. For the users, there is cognitive load on the working memory [(1) extraneous/poor design, (2) intrinsic/inherent difficulty in topic, and (3) germane/forming schemas]. For teachers, there are additional knowledge, skills, and abilities / attitudes (KSAs) that need to be brought to bear.
Exploring the Deep Dream Generator (an Art-Making Generative AI) Shalin Hai-Jew
The Deep Dream Generator was created by Google engineer Alexander Mordvintsev in 2014. It has a public facing instance at https://deepdreamgenerator.com/, which enables people to use text prompts and image prompts (individually or in combination) to inspire the art-generating generative AI to output images. This work highlights some process-based walk-throughs of the tool, some practical uses, some lightweight art learning, some aspects of the online social community on this platform, and other insights. Some works by the AI prompted by the presenter may be seen here: https://deepdreamgenerator.com/u/sjjalinn.
(This is the first draft of a slideshow that will be used in a conference later in the year.)
Augmented Reality for Learning and AccessibilityShalin Hai-Jew
Recently, the presenter conducted a systematic review of the academic literature and an environmental scan to learn how to set up an augmented reality (AR) shop at an institution of higher education. The ambition was to not only set up AR in an accessible and legal way but also be able to test for potential +/- effects of AR on teaching and learning. The research did not go past the review stage, because of a lack of funding, but some insights about accessibility in AR were acquired.
(The visuals are from Deep Dream Generator and CrAIyon.)
Engaging Pixabay as an open-source contributor to hone digital image editing,...Shalin Hai-Jew
This slideshow describes the author's early experiences with creating two accounts on Pixabay in order to advance digital editing skills in multimedia. The two accounts are located at https://pixabay.com/users/sjjalinn-28605710/ and https://pixabay.com/users/wavegenerics-29440244/ ...
This work explores four main spaces where researchers publish about educational technology: academic-commercial, open-access, open-source, and self-publishing.
Human-Machine Collaboration: Using art-making AI (CrAIyon) as cited work, o...Shalin Hai-Jew
It is early days for generative art AIs. What are some ways to use these to complement one's work while staying legal (legal-ish)?
Correction: .webp is a raster format
Getting Started with Augmented Reality (AR) in Online Teaching and Learning i...Shalin Hai-Jew
University creative shops are exploring whether they can get into the game of producing AR-enhanced experiences: campus tours, interactive gaming, virtual laboratories, exploratory art spaces, simulations, design labs, online / offline / blended teaching and learning modules, and other AR applications.
This work offers a basic environmental scan of the AR space for online teaching and learning, and it includes pedagogical design leads from the current research, technological knowhow, hands-on design / development / deployment of learning objects, and online teaching and learning methods.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
The K-State Online Canvas LMS Data Portal and Five Years of Activated Third-Party Apps
1. The K-State Online
Canvas LMS Data Portal and
Five Years of
Activated Third-Party Apps
E V E N T : H A V E A B Y T E !
S E P T . 2 2 , 2 0 1 7
K - S T A T E U N I O N F O R U M H A L L
K A N S A S S T A T E U N I V E R S I T Y
2. Presentation
The presenter will introduce the K-State LMS data portal and introduce some available insights
from there and focus on one particular facet of this big data--the third-party apps that K-State
faculty, admin, and staff have activated and what that says about how we're using Canvas.
2
3. Overview
Canvas LMS data portal for the Kansas State University instance
◦ A data dictionary: Version 1.16.2 (https://portal.inshosteddata.com/docs)
◦ Data extraction and processing
◦ What it can tell us: (un)available data and information
Activated third-party tools in K-State Online Canvas LMS instance
Some caveats
What this says about what K-Staters (early adopters) are using
Practical applications of this third-party app activation data
Adding value to LMS data portal data
And internal reports, too
3
4. Canvas LMS Data Portal
for the Kansas State
University Instance
4
5. K-State’s Learning Management System
(LMS)
K-State Online is based on the Canvas LMS.
LMSes generally have the following capabilities: delivery of learning contents,
intercommunications, collaboration, assessment creation and deployment, grading,
collaborations, live web conferencing, persistent profile creation for presence, e-portfolio
development, and secure links to various protected on-campus data systems.
Canvas LMS is evolved on an agile and fast development cycle with updates every few weeks.
This is a hosted solution, which means that the LMS functionality and data reside on Instructure
servers.
Canvas is known for its integrations with various social media and other platforms.
In late 2015, Canvas rolled out its data portal, and K-State signed on to access its data in October
2016.
◦ The data tables range in size up to millions of rows of data.
5
8. Basic details
The data is organized apparently in what
is known as the “star schema,” with both
“fact” and “dimension” tables
(filename_fact, filename_dim).
Generally, _fact tables contain foreign
keys paired with the primary keys in
dimension tables.
Fact tables contain quantitative data
about events applied to _dim content
data.
79 data tables
.gz files from SQL
Uncompress
Change to . csv
ingest into Access to select data…and
export to Excel for processing and
visualizing (or)
“union” combined files in SQL
Express, query, and export to Excel for
processing and visualizing
8
13. A brief inventory: Dimension files (cont.)
quiz_submission
quiz_submission_historical
role
score
submission_comment
submission_comment_participant
submission
user
wiki
wiki_page
13
14. Canvas LMS Data Portal data
The Canvas LMS data portal data is updated daily.
Most of the data on the portal enables summary observations.
Not all data generated in the Canvas LMS is available in the data portal. Outcomes information
is not included, so running analyses by outcomes (Who passed? Who failed?) is not so easily
captured. (This requires access to grade data from KSIS.)
There are some challenges with both the foreign and primary keys.
◦ Querying this data as “flat files” has its limits. It’s much harder to query across data tables without
clarity between the keys.
Putting this up on SQL has its limits, too, even though it’s somewhat faster.
The data portal includes personally identifiable information (PII) and outright names, and so the
data has to be handled with care.
14
15. Canvas LMS
Data Portal Data
(cont.)
According to a client recent web conference sponsored by
Instructure, it does not look like a data dashboard is in the
works yet.
There are third-party efforts in higher education to create
some programs to enable automated download, the assigning
of keys, and so on, to enable some queries for local
applications.
◦ Two presenters offered insights on an effort to create a Canvas
DataViewer:
◦ Bill Jones, Director, Instructional Technology & Info Management, and
◦ Andrew Anders, LMS Coordinator, both of Eastern Michigan University
15
17. Third-party apps
Third-party apps in the Canvas LMS instance may be activated at the global level of the instance,
which enables access to those apps by all users and all in-instance courses.
Otherwise, third-party apps may be activated at course level by the instructor and / or admin of
the course.
◦ The apps have to be activated in each course and cannot be activated across multiple courses
simultaneously.
17
18. External Apps in
K-State Instance
of Canvas LMS
Path:
Settings (left menu) ->
Apps (fourth tab over from the left)
Can filter by “All,” “Not Installed,”
“Installed”
Listed in Alphabetical Order
Then Have to Use the Dropdown for
“More External Tools”
18
19. Some available summary activated app
stats
295 unique integrated apps available as of August 2017
◦ Enabled through Learning Tools Interoperability (LTI) standards (by the IMS Global Learning Consortium)
Apps are a combination of free, mixed-cost, subscription-based access, and purchase-based
access.
The point is to integrate functionalities, resources, communities, and other elements in a
seamless way from the LMS.
At K-State, 3,753 activations of third-party (and some in-house) apps have been recorded during
the lifespan of the LMS from late 2013 to late July 2017.
◦ There are popular apps which are widely activated, and then long tails of a few activations.
◦ In terms of how many unique apps K-Staters have activated, they have activated approximately 103
unique applications (both third-party created and locally created).
19
20. What app types are available?
PROPRIETARY DIGITAL CONTENTS
Proprietary digital contents (by third-party
content providers for e-books, videos, virtual
labs, and others)
◦ Online labs for experiential learning
◦ Recorded lectures
Geographical map integrations
Mass media contents / news sources
OPEN EDUCATIONAL RESOURCES
Open educational resources (OERs)
Open-access educational resources
Open-source digital learning object repository
/ referatory
Coding / coding APIs
20
21. What app types are available?
CUSTOMIZED LEARNING, LEARNING AIDS,
TEACHING AIDS FOR LEARNING
Artificial intelligence-informed learning tools
Learning supports
Dropout indicators for early warning (for
teachers)
EXPRESSION, DIGITAL CONTENT CREATION,
RECORDINGS
Gamified content creation
Coding
Mathematical expression
◦ Graphing calculator
Visual expression
◦ Comic creation tools
21
22. What app types are available?(cont.)
SOCIAL MEDIA
Social media platforms
◦ Microblogging sites
◦ Video sharing (predominantly; open-source and
proprietary)
◦ Video hosting and annotation
◦ Image sharing (predominantly)
Folk tagging online
DIGITAL CONTENT HOSTING SERVICES
Digital content hosting sites (digital files,
digital notebooks, and others)
Online learning LMSes
Portfolio building
Third-party video media hosting (with access
to desktop lecture capture software)
22
23. What app types are available?(cont.)
MICRO-CREDENTIALING, BADGING
Micro-credentialing
Badging
ASSESSMENT SUPPORTS
Test-taking and quizzing
◦ Browser lockdown for high-value assessment-
taking online
◦ Proctoring
Scantron supports
AI-informed assessment tool for customized
learning
23
24. What app types are available?(cont.)
REMOTE PROCTORING, SECURE REMOTE
TESTING
Remote proctoring
Secure remote testing / assessment
INTERCOMMUNICATIONS, COLLABORATIONS
Radio frequency device registrations (such as
clicker devices)
24
25. What app types are available?(cont.)
WORK MANAGEMENT
Time management application
VIRTUAL MEETINGS
Web conferencing, web meetings
Recorded virtual meetings
25
26. What app types are available?(cont.)
RESEARCH TOOLS, DATA TOOLS
Collaborative online research (for learners)
Course analytics (for instructors)
NEWS MEDIA FEEDS, ACADEMIC PUBLISHING
CONTENTS
Mainstream media news
News feeds
26
27. What app types are available?(cont.)
MISCELLANY
Bridging to general contents
Single sign-on for online learning
and others…
27
28. Some developer work
A perusal of the activated apps shows some local developer work with some bridges to
application programming interfaces (APIs).
There are references to legacy accounts.
For example, there are the following: “Scantron (dev),” “Scantron (hotfix),” “Scantron v3 (prod
test),” etc., with multiple versions of the prior activations in multiple accounts (probably to
enable developer access and testing and documentation)
Another few were variations on “photo roster test.”
There are “made at K-State” apps and tools.
28
29. Some local deployed apps
University data reporting
Submit ASL report
All reports & survey results
Several user names: Scott F. and Kakali B.
Study area
Senior Survey Results
Alumni Survey Results
---- Survey Results
ELP-KSIS Reporting
Webassign
Specific Twitter feed (#hashtagged)
Aviation Reporting
FGS
MGS
TEVAL pilot
Hale Library Guide
29
30. Some local deployed apps (cont.)
K-State Attendance
NTSB
FAA
Student Learning Outcomes
Canvas Help
Canvas Guides
30
31. Other third-party apps (not mentioned in the Canvas apps page)
Mathalicious
Cerego
OneDrive for Business
iMathAS Test
Google Charts
CrowdMark
WayPoint Outcomes
Kool Learning
Weebly
Sapling Learning
Word Press
Canvas Commons
31
39. For free? Outright purchase?
Subscription?
The apps themselves are free, and most serve as bridges to online contents, services, and
resources.
◦ Many of the apps enable access to wholly free services.
◦ Some apps offer some limited version of a for-pay service.
◦ Some apps bridge to wholly subscription-only services / resources.
◦ Some apps link to full-purchase resources.
What are the proportions of the free vs. cost-based online services?
◦ Based on the app descriptions on Canvas…and on the Web, some early calculations were made.
39
40. Base rate of free to cost-based apps
(from a rough count of all available apps)
40
Free or No Cost
12%
Subscription or Purchase
Required
88%
An Estimate of Free vs. Cost-based
Free or No Cost Subscription or Purchase Required
41. Base app types and counts
41
Categories of Third-Party Apps Available
Proprietary digital contents 72
Open educational resources 23
Customized learning, learning aids, teaching aids for learning 30
Expression, digital content creation, recordings 28
Social media 9
Digital content hosting services 25
Micro-credentialing, badging 6
Assessment supports 32
Proctoring, secure remote testing 11
Intercommunications, collaborations 20
Work management 2
Virtual meetings 11
Research tools, data tools 10
News media feeds, academic publishing contents 4
Miscellany 12
55. Named K-State resource activations in 2017
(partial year)
55
K-State Attendance
46%
K-State Library Resources for Graduate
Students
7%
K-State Polytechnic Library
27%
Library Resources
20%
K-State Resources Activated in K-State Canvas LMS Instance
(2017)
K-State Attendance K-State Library Resources for Graduate Students K-State Polytechnic Library Library Resources
56. A cumulative count of newly activated
third-party apps(2013 – 2017)
56
0
200
400
600
800
1000
1200
1400
2013 (partial) 2014 2015 2016 2017 (partial)
CumulativeThird-PartyActivations
Year-over-Year Counts
Summary of Third-Party App Activations in K-State Instance of Canvas: 2013 - 2017
57. The year of the…
(from the top dozen most frequently activated apps each year)
2013 (partial): social educational video, graphing, authoring tool, digital contents,
microblogging, attendance tool, text chat
2014: … plus self-created contents, coding app, open-source learning contents, social imagery,
mainline news media, Internet Archives
2015: … plus virtual labs, games for child learning, online collaborative grading platform,
Scantron, plagiarism detection
2016: … slideshows from social sharing, quiz app mediation, K-State attendance, virtual
notetaking
2017 (7 months in): … exam mediation, proctoring, Google apps, digital-to-paper assignments
57
58. Available third-party apps vs. activated
third-party apps at K-State
58
Categories of Third-Party Apps Available At K-State
Proprietary digital contents 72 25
Open educational resources 23 17
Customized learning, learning aids, teaching aids for learning 30 11
Expression, digital content creation, recordings 28 13
Social media 9 4
Digital content hosting services 25 5
Micro-credentialing, badging 6 2
Assessment supports 32 10
Proctoring, secure remote testing 11 1
Intercommunications, collaborations 20 3
Work management 2 1
Virtual meetings 11 4
Research tools, data tools 10 0
News media feeds, academic publishing contents 4 4
Miscellany 12 3
59. How does K-State’s third-party activations
compare with the base categories?
(basedon unique tool activations not #s of activations, expressed in a spider chart)
59
0
10
20
30
40
50
60
70
80
Proprietary digital contents
Open educational resources
Customized learning, learning aids,
teaching aids for learning
Expression, digital content
creation, recordings
Social media
Digital content hosting services
Micro-credentialing, badging
Assessment supportsProctoring, secure remote testing
Intercommunications,
collaborations
Work management
Virtual meetings
Research tools, data tools
News media feeds, academic
publishing contents
Miscellany
A Comparison between K-State's Third-Party Activations by Category with
Available Apps: (2013 - 2017)
Available At K-State
64. Multiple paths to integrate applications
To understand this data better, though, it helps to note that there are other ways to integrate
some of these tools within a Canvas course.
◦ Using an inline frame (iframe) to embed the online software as a service (SAAS) or the social media
platform (like microblogging sites) or a third-party content provider portal…
◦ Using a uniform resource locator (URL) to link to online resources
In other words, activating third-party apps is not an exclusive channel to the functionality.
64
65. Activation but not used?
Decommissioned?
ACTIVATION BUT NOT USED?
Also, third-party apps may be activated but
not used.
◦ For some of the tools, they have to be
subscribed to and integrated with a learning
sequence for them to be of actual use.
Activation is just a first step.
USED INITIALLY BUT LATER DE-
COMMISSIONED?
This data does not include end dates for when
an activated app may have been de-
commissioned.
This data does include when an app was
revised, but those findings were not included
here.
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66. Renaming of third-party apps
Some users have also renamed apps for customizations, sometimes with some residua of a prior
existing name and other times wholly unrecognizably named.
Reliance on the names can be somewhat misleading.
◦ Names can “ambiguate” / confuse, and they can disambiguate / clarify.
66
67. What This says about
What K-Staters (Early
Adopters) are Using
67
68. Closer analysis of these data…might
suggest…
Any guesses?!
Which departments (within which subject areas / domains) are activating third-party apps…
Which news sources are more popular with K-State profs than others…
Which publishers and third-party content providers are more popular than others…
Which virtual labs are desirable for online / blended / F2F learning…
Which user-generated video sources are preferred…
Whether open-source digital learning objects are popularly used or not…
What authoring tools and software programs are used to generate digital learning contents…
Whether (and which) clickers are used in some of the larger classrooms…
68
70. How can this data be applied?
K-Staters can raise awareness of these respective tools with their colleagues to see if there are
ways to enhance the teaching and learning.
Those who provide support for teaching and learning can offer support in this area.
70
72. More about
third-party apps
on Canvas?
While this example focused on a few
related flat files, and the analytics
were simple, it is totally possible to
ramp up this work.
One simple way to add value here
would be not only focus mostly on the
most frequently activated 3rd party apps
but also look at the “long tail” of single
activations.
It may be helpful to ask instructors and
other users why they activated
particular tools and how they use them.
It is possible to compare activations of
types and specific tools over the years.
With data from other comparable
universities, it is possible to compare
activation patterns.
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73. Broadening askable questions
Broaden the askable queries and questions from the data by…
◦ Engaging the front-end part of the LMS and working with faculty
◦ Using the PII and student information systems information (with IRB approval)
◦ Conducting analyses of student learning based on outcomes and machine learning (with decision trees)
◦ Capturing primary keys and foreign keys to be able to ask questions across the data tables (on a
relational database)
◦ And others…
73
75. And pre-packaged reports data (admin access)
Course Storage
Grade Export
LTI Report
Last Enrollment Activity
Last User Access
MGP Grade Export
Outcome Results
Provisioning
Public Courses
Recently Deleted Courses
SIS Export (student information systems)
Student Competency
Students Access Report
Students with No Submissions
Unpublished Courses
Unused Courses
User Access Tokens
Zero Activity
75
78. View course
analytics (instructor
access from course pages)
Activity by Date
Submissions
Grades
By Student (Page Views,
Participations, Submissions, On Time,
Late, Missing, Current Score)
78
80. Conclusion and contact
Dr. Shalin Hai-Jew
◦ iTAC, Kansas State University
◦ 212 Hale / Farrell Library
◦ 785-532-5262
◦ shalin@k-state.edu
Interested in a first skim of K-State LMS Data Portal data?
◦ “Wrangling Big Data in a Small Tech Ecosystem” (Oct. 2016, June 2017)
◦ A downloadable copy is on SlideShare: https://www.slideshare.net/ShalinHaiJew/wrangling-big-data-in-a-small-tech-ecosystem
◦ Rough Categorization of Third-Party Apps from Canvas (download): http://www.k-
state.edu/ID/RoughCategorizationofThirdPartyAppsfromCanvas.xlsx or http://www.k-
state.edu/ID/RoughCategorizationofThirdPartyAppsfromCanvas.pdf
◦ This is based on a subjective assessment of the often-multifaceted tool’s main functions based on short descriptions in Canvas and website
descriptions if the initial description is insufficient.
Slideshow on Adobe Spark: https://spark.adobe.com/page/PQRxrknjH2nxp/
The presenter does not have any formal ties with any of the mentioned technologies.
80