When you hear the term data, you might not get very excited. Data has a reputation for being dry, boring and difficult to deal with. All this changes, however, when you consider data’s capabilities.
The truth is, of all the amazing technological advancements in recent decades, the ability to quickly capture, analyze and apply data on student learning and progress has the potential to facilitate some of the biggest improvements to our educational system we’ve seen yet. We’ve had access to basic student data through standardized testing for a long time, but only in the past decade have complex data systems become affordable and accessible enough for administrators and teachers to use them to track real-time student data. These systems can help us differentiate and personalize instruction, use formative assessments effectively and optimize districtwide tech initiatives — in short, they can revolutionize education.
This isn’t hype. Research backs up data’s crucial role in maximizing student success.
Click here to learn from local and international expert modellers atData Modelling Zone, AustraliaBy Steve Hoberman.What is the Data Model Scorecard?An application’s flexibility and data quality depend quite a bit on the underlying data model. In other words, a good data model can lead to a good application and a bad data model can lead to a bad application. Therefore we need an objective way of measuring what is good or bad about the model. After reviewing hundreds of data models, I formalized the criteria I have been using into what I call the Data Model Scorecard.The Scorecard contai
This paper reviews the literature related to learning analytics and seeks to define learning analytics, its processes, and its potential to advance teaching and learning in online education. While until …
The Caliper Analytics interoperability specification from the IMS Global Learning Consortium has achieved "candidate final release" status.
Caliper Analytics is a new standard that aims to reduce the cost of collecting analytics data from digital educational tools and to simplify the process of presenting that data consistently, according to information from IMS Global. The specification includes a Sensor API (application programming interface) to enable the collection of analytics data and Metric Profiles to establish a common format for presenting that data.
Much like over-the-counter medicines, student data should carry easy-to-understand labeling and guidance to help people use it properly.
Student data systems can help educators differentiate and personalize instruction, use formative assessments effectively and optimize districtwide tech initiatives. In short, they can revolutionize education.
Yet student data is only as effective as an educator’s ability to interpret it. Most data reports fall short because they are counterintuitive or difficult to understand.
- Learner-centered approaches to higher education require that instructors have insight into their students' characteristics, but instructors often prepare their courses long before they have an opportunity to meet the students.
- To address this dilemma, we developed the institutional
Student Profile Report, which informs instructors about the demographics, enrollment history, and general academic performance of the students in their upcoming classes before the start of the semester.
- Survey results indicated that instructors found the report interesting and useful, and our analysis of grade outcomes found that the report did not introduce bias in letter grade assignment.
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.
That learning analytics are set to take off is hardly surprising. After all, its common cause that many of us allow the new virtual behemoths access to our every keystroke in return for the advantages that this brings us. Amazon uses our search and purchasing patterns to suggest what books we might also like to read on the same subject. Google scans the content of every email to select the advertisements that are most appropriate to our needs. Weather apps use our location to tell us whether the sun will shine on us today. Students use all these services, and many more; they are not surprised if their universities use their personal data in similar ways. Indeed, Jisc’s work shows that students are – at present – relaxed about all this.
Data experts convene to create profile of the “big data specialist” position, which is predicted to be in high demand if data skills remain untaught
Students at both the K-12 and university levels should learn how to handle and interpret big data, but to do this, educators at both levels must be comfortable using and teaching about big data.
Big data is quickly becoming one of the most important fields, and workers who are able to handle, analyze, and interpret data will be in high demand in the workforce. And this need is critical in education, from students who must know how to use data as part of learning, to educators who should be able to interpret student data.
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