Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch015
A virtual “privilege walk” is an interactive survey that helps a respondent consider the role of unfair advantage in social relationships and where he or she stands in relation to social power based on family life, access to resources, social positioning and embodiment, and other factors. In 2014, at Kansas State University, an open-access virtual privilege walk was created to align with the launch of a graduate social justice certificate program. This chapter explores that privilege walk instrument through (1) a computational text analysis, (2) descriptive statistics around the responses to the instrument, and (3) an exploratory factor analysis (based on three years of anonymous data) to see how well the underlying factors align with the intended factors and to find directions for improving the instrument.
虚拟“特权行走”是一项互动调查，帮助被调查者根据家庭生活、资源获取、社会定位和体现等因素，思考不公平优势在社会关系中的作用，以及他或她在社会权力中的地位。2014年，堪萨斯州立大学(Kansas State University)创建了一个开放获取的虚拟特权之旅，以配合研究生社会正义证书项目的启动。本章通过(1)计算文本分析，(2)围绕对工具的响应的描述性统计，以及(3)探索性因素分析(基于三年的匿名数据)来探索特权行走工具，以了解潜在因素与预期因素的一致程度，并找到改进工具的方向。
{"title":"An Exploratory Factor Analysis of an Open-Access Virtual “Privilege Walk” Instrument","authors":"","doi":"10.4018/978-1-5225-7528-3.ch015","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch015","url":null,"abstract":"A virtual “privilege walk” is an interactive survey that helps a respondent consider the role of unfair advantage in social relationships and where he or she stands in relation to social power based on family life, access to resources, social positioning and embodiment, and other factors. In 2014, at Kansas State University, an open-access virtual privilege walk was created to align with the launch of a graduate social justice certificate program. This chapter explores that privilege walk instrument through (1) a computational text analysis, (2) descriptive statistics around the responses to the instrument, and (3) an exploratory factor analysis (based on three years of anonymous data) to see how well the underlying factors align with the intended factors and to find directions for improving the instrument.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114905088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch001
A learning management system (LMS) data portal contains data collected as a byproduct of the running of the LMS. A data dictionary related to that data portal contains both critical information about the data and a list of terms and definitions describing the data contents. The modern LMS studied here has multiple channels for data capture and analytics: (1) the front-facing LMS (at both the instructor level and the admin level), (2) the reports feature (for system administrators), and (3) the data portal (for system administrators). This chapter describes some ways to understand data possibilities through the examination of an LMS data portal data dictionary and light LMS data exploration.
{"title":"Reading Data Possibilities From an LMS Data Portal Data Dictionary","authors":"","doi":"10.4018/978-1-5225-7528-3.ch001","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch001","url":null,"abstract":"A learning management system (LMS) data portal contains data collected as a byproduct of the running of the LMS. A data dictionary related to that data portal contains both critical information about the data and a list of terms and definitions describing the data contents. The modern LMS studied here has multiple channels for data capture and analytics: (1) the front-facing LMS (at both the instructor level and the admin level), (2) the reports feature (for system administrators), and (3) the data portal (for system administrators). This chapter describes some ways to understand data possibilities through the examination of an LMS data portal data dictionary and light LMS data exploration.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"45 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132639512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch014
This chapter introduces the use of basic time-to-event analysis (a variation of “survival analysis”) to identify time-series patterns from learning management system (LMS) data portal datasets to enable empirical-based theorizing and interpretation. This approach addresses questions such as How long does it usually take before a particular event occurs? What time patterns may be seen in empirical data? What sorts of analysis and decision making can be understood from the time patterns? This chapter uses multiple datasets—related to assignment submittals and their time to grading, learner enrollments and the updates to those enrollments, and group membership and how long groups last, and other data—to demonstrate this process.
{"title":"Basic Time-to-Event Analyses of Online Educational Data","authors":"","doi":"10.4018/978-1-5225-7528-3.ch014","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch014","url":null,"abstract":"This chapter introduces the use of basic time-to-event analysis (a variation of “survival analysis”) to identify time-series patterns from learning management system (LMS) data portal datasets to enable empirical-based theorizing and interpretation. This approach addresses questions such as How long does it usually take before a particular event occurs? What time patterns may be seen in empirical data? What sorts of analysis and decision making can be understood from the time patterns? This chapter uses multiple datasets—related to assignment submittals and their time to grading, learner enrollments and the updates to those enrollments, and group membership and how long groups last, and other data—to demonstrate this process.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114859162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch002
On Canvas's data portal, the “external_tool_activation_dim” data table showcases applications activated on the LMS instance through an LTI or other integration mechanism. The “apps” include those by third-party content providers, publishers, software makers, social media platforms, as well as in-house developers. The linked resources include e-books, simulated labs, inter-communications tools, digital content hosting services, assessment supports, proctoring services, work management tools, micro-credentialing services, and others. Understanding which third-party and customized applications are activated may shed light on the interests of the online instructors, the gaps between activated applications and available ones, local custom-coded applications, and others. This chapter captures activated app data through the full lifespan of the LMS instance at Kansas State University to the present moment and encapsulates five academic years: Fall 2013 – Summer 2014, Fall 2014 – Summer 2015, Fall 2015 – Summer 2016, Fall 2016 – Summer 2017, and Fall 2017 – Spring 2018.
{"title":"Five Academic Years of Activated Third-Party and Custom-Coded Applications on an LMS Instance","authors":"","doi":"10.4018/978-1-5225-7528-3.ch002","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch002","url":null,"abstract":"On Canvas's data portal, the “external_tool_activation_dim” data table showcases applications activated on the LMS instance through an LTI or other integration mechanism. The “apps” include those by third-party content providers, publishers, software makers, social media platforms, as well as in-house developers. The linked resources include e-books, simulated labs, inter-communications tools, digital content hosting services, assessment supports, proctoring services, work management tools, micro-credentialing services, and others. Understanding which third-party and customized applications are activated may shed light on the interests of the online instructors, the gaps between activated applications and available ones, local custom-coded applications, and others. This chapter captures activated app data through the full lifespan of the LMS instance at Kansas State University to the present moment and encapsulates five academic years: Fall 2013 – Summer 2014, Fall 2014 – Summer 2015, Fall 2015 – Summer 2016, Fall 2016 – Summer 2017, and Fall 2017 – Spring 2018.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126146415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch008
In the dozen years since massive open online courses (MOOCs) have been a part of open-source online learning, the related platforms and technologies have settled out to some degree. This chapter indirectly explores 10 of the most well-known MOOC platforms based on social data from the following sources: large-scale web search data (via Google Correlate), academic research indexing (Google Scholar), social imagery and related image tagging (Google Image Search), crowd-sourced articles from a crowd-sourced encyclopedia (Wikipedia), microblogging data (Twitter), and posts and comments from social networking data (Facebook). This analysis is multimodal, to include text and imagery, and the analyses are enabled by various forms of “distant reading,” including topic modeling, sentiment analysis, and computational text analysis, and manual coding of social imagery. This chapter aims to define MOOC platforms indirectly by their course contents and the user bases (and their social media-based discourses) that have grown up around each.
{"title":"Peripheral Vision","authors":"","doi":"10.4018/978-1-5225-7528-3.ch008","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch008","url":null,"abstract":"In the dozen years since massive open online courses (MOOCs) have been a part of open-source online learning, the related platforms and technologies have settled out to some degree. This chapter indirectly explores 10 of the most well-known MOOC platforms based on social data from the following sources: large-scale web search data (via Google Correlate), academic research indexing (Google Scholar), social imagery and related image tagging (Google Image Search), crowd-sourced articles from a crowd-sourced encyclopedia (Wikipedia), microblogging data (Twitter), and posts and comments from social networking data (Facebook). This analysis is multimodal, to include text and imagery, and the analyses are enabled by various forms of “distant reading,” including topic modeling, sentiment analysis, and computational text analysis, and manual coding of social imagery. This chapter aims to define MOOC platforms indirectly by their course contents and the user bases (and their social media-based discourses) that have grown up around each.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114686747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch005
In a formal online learning course in higher education, learners usually respond to both assignments and assessments in order to achieve the learning and to provide evidence of their progress. In a learning management system (LMS) instance, analysts may access (1) high-level descriptions of selected features of the assignments and assessments through an administrator-accessed data portal (and a reports section), and they may access (2) close-in descriptions from the learner-facing side. This chapter describes an exploration of the assignments and assessments in a live LMS instance, based on both high-level and close-in analyses; systematized approaches to harness such information to benefit teaching and learning; and proposes some tentative ways to improve teaching and learning for the particular university.
{"title":"Improving Teaching and Learning From High-Level and Close-In Features of Assignments and Assessments in an LMS Instance","authors":"","doi":"10.4018/978-1-5225-7528-3.ch005","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch005","url":null,"abstract":"In a formal online learning course in higher education, learners usually respond to both assignments and assessments in order to achieve the learning and to provide evidence of their progress. In a learning management system (LMS) instance, analysts may access (1) high-level descriptions of selected features of the assignments and assessments through an administrator-accessed data portal (and a reports section), and they may access (2) close-in descriptions from the learner-facing side. This chapter describes an exploration of the assignments and assessments in a live LMS instance, based on both high-level and close-in analyses; systematized approaches to harness such information to benefit teaching and learning; and proposes some tentative ways to improve teaching and learning for the particular university.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131946159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch013
The data created as a byproduct of the functioning of a learning management system (LMS) have been made available to administrators of LMSes through multiple channels on Instructure's Canvas LMS. One of these channels is the packaged “Reports” function in the Admin section, which enables users to download data tables based on formal terms of the academic calendar (all terms, fall, spring, summer, and others). This work explores some highlights from select extracted eras (time periods) of a live LMS instance at Kansas State University. This chapter includes the first term out of the gate for the LMS, public courses and recently deleted ones during the fall/spring/summer sessions during the LMS lifespan, learning tools interoperability (LTI) reports in the LMS instance, competencies, and other insights. Various contemporary data analytics methods are applied to extract meanings from this time-based data.
{"title":"Highlights From Extracted Eras of a Live LMS Instance","authors":"","doi":"10.4018/978-1-5225-7528-3.ch013","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch013","url":null,"abstract":"The data created as a byproduct of the functioning of a learning management system (LMS) have been made available to administrators of LMSes through multiple channels on Instructure's Canvas LMS. One of these channels is the packaged “Reports” function in the Admin section, which enables users to download data tables based on formal terms of the academic calendar (all terms, fall, spring, summer, and others). This work explores some highlights from select extracted eras (time periods) of a live LMS instance at Kansas State University. This chapter includes the first term out of the gate for the LMS, public courses and recently deleted ones during the fall/spring/summer sessions during the LMS lifespan, learning tools interoperability (LTI) reports in the LMS instance, competencies, and other insights. Various contemporary data analytics methods are applied to extract meanings from this time-based data.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114996376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch010
This chapter explores two social images sets extracted from a Google Image search around two education-related topics: “online learning” and “instructional design.” For both topics, hundreds of images were extracted, and both image sets offer insights on the target topics, who is using the imagery, and how the images are used. This chapter further tests a hypothesis about social imagery: that they are important parts of strategic messaging and that the social imagery for online learning may focus on messaging inviting participation in online learning (to potential and continuing learners) and those for instructional design may focus on messaging to practitioners and would-be practitioners to join the field and for administrators and executives to hire instructional designers. The coding approach was defined a priori, and then the images were roughly coded. The initial findings are reported.
{"title":"Visual Senses of “Online Learning” and “Instructional Design”","authors":"","doi":"10.4018/978-1-5225-7528-3.ch010","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch010","url":null,"abstract":"This chapter explores two social images sets extracted from a Google Image search around two education-related topics: “online learning” and “instructional design.” For both topics, hundreds of images were extracted, and both image sets offer insights on the target topics, who is using the imagery, and how the images are used. This chapter further tests a hypothesis about social imagery: that they are important parts of strategic messaging and that the social imagery for online learning may focus on messaging inviting participation in online learning (to potential and continuing learners) and those for instructional design may focus on messaging to practitioners and would-be practitioners to join the field and for administrators and executives to hire instructional designers. The coding approach was defined a priori, and then the images were roughly coded. The initial findings are reported.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134324312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch009
Since its origin in 2011, the E-Learning Faculty Modules (built on a MediaWiki understructure) has evolved into a resource with over 130 articles in three tiers: Beginners' Studio, E-Learning Central, and Advanced Workshop. This resource has remained focused on supporting online instructors in their work. Since this resource is built in an open-source way on a designed wiki structure, it is possible to data-fy various aspects of the wiki: (1) the emergent wiki-hosted contents, (2) user page views, and (3) observable gaps with ideas for next steps. This chapter demonstrates some of the easy-access data about online usage of an open-access open-source resource distributed through a Web 2.0 technology.
{"title":"Datafication of the “E-Learning Faculty Modules” for Next Steps","authors":"","doi":"10.4018/978-1-5225-7528-3.ch009","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch009","url":null,"abstract":"Since its origin in 2011, the E-Learning Faculty Modules (built on a MediaWiki understructure) has evolved into a resource with over 130 articles in three tiers: Beginners' Studio, E-Learning Central, and Advanced Workshop. This resource has remained focused on supporting online instructors in their work. Since this resource is built in an open-source way on a designed wiki structure, it is possible to data-fy various aspects of the wiki: (1) the emergent wiki-hosted contents, (2) user page views, and (3) observable gaps with ideas for next steps. This chapter demonstrates some of the easy-access data about online usage of an open-access open-source resource distributed through a Web 2.0 technology.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121052748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-7528-3.ch004
For instructional designers, one of the early steps in any design involves an environmental scan to see what publicly available online learning objects, sequences, and raw materials exist for the topic. “Conceptual reverse engineering” involves analyzing the online learning objects and sequences to infer how those objects may have been created, what technologies were likely used, the probable learning objectives, the apparent target audience, the prospective costs/inputs, and other factors. This information may be used to understand the state of the art, to inform a competing design methods, to inform the selection of technologies, to budget design and development work, to decide whether or not to adopt available third-party learning objects, and other applications. This chapter describes the creation of the conceptual reverse engineering of online learning objects and sequences (CREOLOS), which includes a step for validating/invalidating the reverse-engineered design.
{"title":"“Conceptual Reverse Engineering” of Online Learning Objects and Sequences for Practical Applications","authors":"","doi":"10.4018/978-1-5225-7528-3.ch004","DOIUrl":"https://doi.org/10.4018/978-1-5225-7528-3.ch004","url":null,"abstract":"For instructional designers, one of the early steps in any design involves an environmental scan to see what publicly available online learning objects, sequences, and raw materials exist for the topic. “Conceptual reverse engineering” involves analyzing the online learning objects and sequences to infer how those objects may have been created, what technologies were likely used, the probable learning objectives, the apparent target audience, the prospective costs/inputs, and other factors. This information may be used to understand the state of the art, to inform a competing design methods, to inform the selection of technologies, to budget design and development work, to decide whether or not to adopt available third-party learning objects, and other applications. This chapter describes the creation of the conceptual reverse engineering of online learning objects and sequences (CREOLOS), which includes a step for validating/invalidating the reverse-engineered design.","PeriodicalId":332480,"journal":{"name":"Methods for Analyzing and Leveraging Online Learning Data","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124593602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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