Using Social Image Sets to Explore Virtual Embodiment in Second Life® as Indicators of Formal, Nonformal, and Informal Learning

Capturing Second Life® imagery sets from Yahoo's Flickr and Google Images enables indirect and backwards analysis (in a decontextualized way) to better understand the role of SL in people's virtual self-identities and online practices. Through manual bottom-up coding, based on grounded theory, such analyses can provide empirical-based understandings of how people are using SL for formal, nonformal, and informal learning. This chapter involves a review of the literature and then a light and iterated analysis of 1,550 randomly batch-downloaded screenshots from SL (including stills from machinima) to explore the potential of social image analysis to make inferences about human learning in SL in the present.

Highlights From Extracted Eras of a Live LMS Instance

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.

Assessing Practical Accessibility in Online Courses Based on Local Conditions

Another approach to exploring online learning data is to see what is not there or what is absent. One use case for this is “practical accessibility” or the accessibility accommodations in online learning courses (or learning objects). This chapter includes a review of the current extant literature, a close-in analysis of several dozen real-world courses (in static format) through an instructional design/developer lens, in service of the following objectives: 1) the drafting of an initial instrument that may be used to assess the accessibility level of an online learning course or digital learning object, 2) the identification of the most common accessibility issues in online courses at a Midwestern university (based on a sample setoff online courses), and 3) the identification of a model course with full or near-full accessibility and seeing what may be learned from that and from specific accessibility accommodations that may be beneficial in other contexts.

Datafication of the “E-Learning Faculty Modules” for Next Steps

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.

Peripheral Vision

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.

Creating and Analyzing Induced Decision Trees From Online Learning Data

Decision trees may be created in various ways. They may be manually drawn based on data, or they may be induced directly from data using supervised machine learning. Decision trees induced from online learning data may evoke insights that may benefit teaching and learning. This work introduces a method for inducing decision trees and addresses how to set the parameters for the trees based on particular decision making and research question making. This work uses online learning data to create decision trees and to enable practical insights from the resulting data visualizations.

Improving Teaching and Learning From High-Level and Close-In Features of Assignments and Assessments in an LMS Instance

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.

Basic Time-to-Event Analyses of Online Educational Data

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.

Visual Senses of “Online Learning” and “Instructional Design”

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.

“Conceptual Reverse Engineering” of Online Learning Objects and Sequences for Practical Applications

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.