This literature review, list of definitions, and resources provide a guide for community college leaders evaluating analytics for their institutional technology to promote student success. In the end, a technology strategy inclusive of analytics and combined with an unwavering focus on student achievement might prove an essential precursor to student success at all colleges.
The Education Week Spotlight on Creating a Culture of Datais a collection of articles hand-picked by our editors for their insights on:
Promoting a culture of data accessibility in schoolsChallenges to the long-term usefulness of state longitudinal data systemsPolicy shifts in the use of students' educational recordsAddressing privacy challenges in a digital age
You get the seven articles below in a downloadable PDF.
Welcome to our project Transforming Learning and Teaching with Practical Analytics.
Learning analytics take advantage of large data sets that relate to student online behaviour. These data sets are analysed to inform approaches that help staff make meaningful and informed decisions around student learning and retention, as well as strengthen student retention and success.
There are systems, such as learning analytics, for staff to understand and interpret student behaviour in Blackboard but what is required is understanding and training in the use of analytics and predictive analytics to make better use of these tools.
The project will develop a practical approach to learning analytics (what we term practical analytics) by providing feedback to academic and teaching staff to support meaningful blended delivery. The project will enable learning analytics for early intervention and support academic staff to translate data into improvement in their teaching practice. We aim to increase the number of staff capable of easily accessing and applying analytics to their teaching practice.
Tool offers a new way for students to view class progress
“My Class Insights gives students more data and control over their learning, making WebAssign powerful tool for increasing student engagement,” said Jack Narayan, WebAssign chief academic officer and mathematics professor. “Now students can quickly see the areas they need to pay more attention to, and the data presented in My Class Insights should encourage them to fully invest in learning before an exam.”
Reading sources in the right order can avoid you having to eat humble pie. Immediately after posting Privacy and the Use of Learning Analytics in which I questioned the ability of learning analytics to suggest appropriate interventions, I came across this article in the South African Institute of Distance Education’s (SAIDE) newsletter about a conference in South Africa on Exploring the potential of data analytics to inform improved practice in higher education: connecting data and people.
At this conference, Professor Tim Renick, Vice-President of Georgia State University in the USA, reported on his institution’s accomplishment of eliminating race and income as a predictor of student success.
Amid concerns about the growing use – and abuse – of quantitative measures in universities, a major new review examines the role of metrics in the assessment of research, from the REF to performance management
At the heart of great classroom instruction is the teacher’s ability to identify individual student needs and adjust instruction and intervention accordingly. However, a recent survey by Lexia Learning®, a Rosetta Stone® (NYSE:RST) company, suggests that significant gaps exist in teachers’ abilities to quickly access, interpret and act upon student data to support instruction. The survey, which polled more than 200 educators at the International Society for Technology in Education 2015 Conference and Expo (ISTE), indicated there is a growing need to have assessments more quickly pinpoint skill deficiencies while rapidly providing teachers with connections to actionable data for immediate instructional planning.
According to the survey, only 35 percent of the survey respondents felt that teachers at their schools had a high or very high level of comfort connecting data to instruction. The low figure suggests that the majority of teachers still have trouble using data to identify students’ problem areas and adjust instruction accordingly. When teachers are unable to make that correlation, students are unlikely to receive the appropriate level or intensity of instruction needed to drive academic success.
Executive SummaryIn data mining and data analytics, tools and techniques once confined to research laboratories are being adopted by forward-looking industries to generate business intelligence for improving decision making. Higher education institutions are beginning to use analytics for improving the services they provide and for increasing student grades and retention. The U.S. Department of Education’s National Education Technology Plan, as one part of its model for 21st-century learning powered by technology, envisions ways of using data from online learning systems to improve instruction.
The Predictive Analytics Reporting (PAR) Framework was launched in 2011. PAR's Ellen Wagner discusses insights from four years of exploring the impact of predictive modeling for improving postsecondary student success.
Can we imagienPersonal data collection has long been used to boost productivity but could tiny microphones – embedded in each employee’s own lanyard – be about to change the face of office culture forever?
A Deloitte team in Newfoundland adopted the pilot project last winter during office renovations – CBC News recently reported on its impact. According to one in-house HR expert, it’s been a huge success.
"The minute that you get the report that you're not speaking enough and that you don't show leadership, immediately, the next day, you change your behaviour," Silvia Gonzalez-Zamora told CBC News.
"It's powerful to see how people want to display better behaviours or the behaviours that you're moving them towards,” added the analytics leader.
The concept isn’t as Orwellian as you might think – while the microphones pick up tone of voice and frequency of contributions during meetings, they don’t record specific conversations.
Similarly, accelerometers measure body language and track how often an employee pushes away from their desk – but don’t count coffee breaks or trips to the toilet.
All the data collected is done so anonymously and each employee is assigned their own confidential ID – this way they can compare their own behaviour against that of other participants without fear of reprisal.
Kim Flintoff's insight:
Can we imagine this kind of data collectiion making its way into Teaching and Learning? These devices are simple "wearable technologies" integrating with an Internet of Things to become data new data source for analysiing learning behaviours... presents a whole raft of ethical considerations...
Building an exceptional student experience requires and alignment of various disparate data across the institution. A key part of the exceptional student experience is identifying which students are at risk of dropping out or leaving their institution. As reputation and the bottom line are impacted, it is critical to stem attrition in higher education. But what do you do once you've identified which students are at risk? Successful intervention is key to keeping students on track. That could consistent of academic intervention for extra course study, better advising, a revised set of classes. Or it could be non-academic intervention: more financial aid, a work-study program, or social services intervention.
Learning analytics involves the measurement, collection, analysis and reporting of ‘big data’ related to learners and their contexts, with the intention of providing actionable intelligence that supports teaching and learning. At The Open University, there is increased recognition that “smart-and-pedagogically-informed” learning analytics are urgently needed to solve the student-retention problem.
One of the goals of academic assessment is to identify which students need help; the sooner they can be identified, the better. The promise of technology has been that its ability to collect unique data could make this process timelier, more accurate, and less burdensome. But how might technolo
Following on from our last webinar on Sharing our TinCan/xAPI@Work Journey, we give an update on creating a working proof of concept for TinCan/xAPI. Dr Kirsty Kitto will presents on the work being done in developing a toolkit, which uses xAPI to store data about student participation in learning activities designed using standard social media tools such as Facebook etc.
- What is status of adoption of TinCan/xAPI in the industry ? How fast or slowly is it moving now ? - What can you realistically achieve now with xAPI ? What is the road map you need to take ? - Are there opportunities for the corporate and education sectors to collaborate to increase adoption ?
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