The new IT architecture also requires that tools be able to "work together" to provide better information (analytics) to both faculty and students on their progress and to administrators on their usage. This may mean that data provided by apps is easily made available to an analysis service or even that one tool provides information directly to another tool. For instance, a classroom clicker application might import assessment items created in an LMS and transfer the results data back to the LMS, to a classroom capture system, or to both. An e-textbook application might accept links inserted by the instructor and send data on quiz performance to the LMS and send usage information to the bookstore.
To make the discussion more tangible, Figure 1 shows the types of content, software, and systems that need to be encompassed by the new architecture. In the upper left is the wide variety of learning environments that are today exemplified by the course, LMS, and portal and that in the future will evolve into diverse new forms, such as MOOCs. In the upper right is the growing number of learning tools and content that can be plugged into the learning environments. Such tools can be content-neutral, like a collaboration system, or content-specific, like an adaptive tutoring homework application that goes along with a specific textbook or content area. In the lower right are the other traditionally isolated academic applications, including the new or expanded standalone cloud services that provide specialized learning tools or services, many of which are already in use today. These include library systems, lecture capture, e-portfolio, and assessment systems used by faculty to create, grade, and analyze exams. Though these are typically silo'ed, in the future they will need to be seamlessly integrated for use by faculty and students so that data can be easily exchanged between systems. Finally, in the lower left are the "back office" enterprise systems that typically manage the "system of record." These enterprise systems are critical in the context of learning analytics, since they provide a wide range of demographic and performance data and can be used to look at how student success changes over time.
The center of Figure 1 highlights some of the key areas of open standards and associated services (web services and application programming interfaces) that the higher education community (colleges and universities and suppliers of all types) must converge on in order to enable low-cost, agile, and seamless integration and data exchange among the four categories of software. This is not an exhaustive list but, rather, is representative of the types of exchanges that enable "connectedness" of applications within the context of an institutional or system-wide IT environment.
For over a decade now, the New Media Consortium has issued an annual Horizon Report, detailing the six technologies that it predicts will soon impact colleges and universities. These predictions identify emerging technologies on three “horizons”: four to five years, two to three years, and one year or less.
Ultimately, effective leadership is at the core of trust. District leaders must look to develop a culture shift that starts with trusting those individuals they put in place to lead their initiatives. District leaders must also be open to thinking outside the box when it comes to developing technology plans. They need to understand that we don't buy or integrate technology like we used to do 10-15 years ago. Technology is constantly moving and evolving, and while we as educators must keep up with it, we must also balance our approach to integrating technology. The school or district that moves too quickly or tries to stay too far ahead of the curve may soon find itself in a hole.
It struck me that in the field of analytics right now we are probably placing a lot of expectation on academic staff of being able to interpret data from their virtual learning ecosystem and turn this into appropriate actions, but without giving them the support they need to understand the data and what inferences can be drawn from it.
If you are an instructional designer beginning to work with the Experience API (xAPI), you may be wondering how to go about designing and developing an xAPI solution. Some of the authoringtools you use may support the xAPI. But the only activities some of these tools track are the same bookmarking, scoring, and completion data that SCORM handles.
To realize the full activity-tracking potential of the xAPI in your design, you will need to collaborate with a web developer who knows how to program and who can manually code the xAPI statements. This article outlines 10 steps you can take to plan and communicate your xAPI design to a web developer.
In this article, I want to focus on tips and tools that are free and easily accessible. There are loads of great paid tools out there, and I use many of them. But it is hard to expect someone just getting into this field to pay for expensive software without actually knowing what they are getting into.
Using data to drive learning outcomes isn’t a new concept, really. For as long as teachers have been giving students assessments, the assessments and results have been used by both students and teachers (even if only loosely) to determine how to move forward. What needs to be reviewed more? What was covered/studied well? Learning analytics takes this concept and kicks it up a notch. Well, more like a thousand notches, especially if you’re considering things like adaptive computer based testing that changes as students use it.
This years’ BETT show (British Education and Training Technology - the UK equivalent of ISTE or TCEA) presented a dizzying range speakers and exhibitors, and it was set against some interesting changes in UK education… To name a few, the national curriculum now includes coding, schools should now be teaching character, or ‘grit’ alongside subjects, …
Debates about education are by no means new: What’s the best way to teach? What’s the best way to learn? What should the curriculum be? Who should have access to specialized knowledge and specialized training? How does technology impact all of these questions? (See Plato’s The Republic, for example, on what the education of “philosopher kings” should entail or Plato’sPhaedrus on the dangers to learning of technology (well, of writing).)
Rather than outline the history of education or the history of education theory from Plato the philosopher to PLATO the online learning system, here is a brief overview of 5 of the 20th century’s most important educational theorists. Their influence can still be felt today, both in how we view the educational system and the educational process. As is the case with most theories, these individuals’ work has been adopted, refuted, tweaked, and ignored to varying degrees.
Learning analytics is the study of student behaviour through patterns in their digital world: how often, when and where they log on; the digital resources that they use; the web sites they visit; the social media platforms that they use. At the present time, this is a nascent field of interest in most colleges and universities. But over the next few years, the analysis of the digital trails that students leave as they move through the digital world will become central to curriculum design, learning support, assessment and quality assurance. Two recent reports by Jisc – British education’s digital solutions provider – set the stage for these changes, and point to some early work that needs to be prioritized if these developments are in the interests of students and enable better education.
Expert panelists for this year's report — the NMC Horizon Report: 2015 Higher Education Edition — identified six barriers facing education, half of which carried over from the previous year's report. The challenges fell into three categories: those that are solvable, those that are difficult to solve but within our capabilities to understand and those that are so complex they're difficult even to grasp ... and nigh impossible to fix.
As institutional managers, administrators and researchers are well aware, any practice involving data collection and reuse has inherent legal and ethical implications. Most institutions have clear guidelines and policies in place governing the collection and use of research data; however it is less common for institutions to have legal and ethical guidelines on the use of data gathered from internal systems (Prinsloo & Slade, 2013). As is often the case, the development of legal frameworks has not kept pace with the development of new technologies.
I said it was going to be fun looking at the costs of digital media in education, but it wasn’t. When I came to write this section, I thought it would be a breeze. I wrote about this topic as recently as 2005. All I needed to do is tweak it a little to bring it up to date, I thought.
However, there has truly been a revolution in the media available for teaching and learning in the last ten years, and this revolution has completely up-ended many of the assumptions about costs previously made in this field. Most of the research on costs of educational media had been done by people (like myself) working mainly in distance education, because that was where technology was being mainly used for teaching. That has all changed now: media have gone mainstream.
The sage-on-a-stage model of instruction has dominated higher education since the Middle Ages when there was only one book to be read aloud to assembled students. Today, surveys of faculty members reveal that 70 to 90 percent of classroom time is spent “transferring information” via lecture. The problem is that virtually every study on the topic in the past 20 years has demonstrated that little learning occurs as a result of this method.
Share this post Have you heard about Tin Can? As you know, the Sharable Content Object Reference Model (SCORM) is a set of technical standards for e-learning software products, and it governs how online learning content and learning management systems (LMSs) communicate with each other. The Tin Can API (sometimes known as the Experience API or xAPI) …
The rapid pace of technological change has become the norm in modern digital and information landscapes. Most operating systems, including both Microsoft and Mac OS, change every year or two; mobile devices develop annually with increasing sophistication; and new social- and cloud-based software appear and disappear almost daily. Within educational contexts, the pressure of such changes is felt acutely by educators, administrators, and learners alike.
The m-Learning industry needs to catch up with the mobile industry at large. ... Mobile learning needs integration as part of a complete learning environment including a LMS, collaboration tools, informal learning management tools, and both push and pull technologies. It is time to get ready for mobile learning initiatives, starting with trying various approaches for designing m-Learning “courselets” and testing them on various types of devices using different operating systems."
This document presents options for open source software for use in the education sector. Some of these may have uses outside of education, but they are presented here in the context of their specific benefits to educational establishments, or their use in the course of teaching and learning.
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